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a3d6e17f2fd8a563909ee5f0de9c1f39c266a9ee
|
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T22:27:59.463715](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r4/blob/main/results_2023-10-25T22-27-59.463715.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.16411493288590603,
"em_stderr": 0.0037930361727013637,
"f1": 0.20782508389261733,
"f1_stderr": 0.0038264002899183395,
"acc": 0.4511208594202994,
"acc_stderr": 0.010571455427847876
},
"harness|drop|3": {
"em": 0.16411493288590603,
"em_stderr": 0.0037930361727013637,
"f1": 0.20782508389261733,
"f1_stderr": 0.0038264002899183395
},
"harness|gsm8k|5": {
"acc": 0.13191811978771797,
"acc_stderr": 0.009321265253857515
},
"harness|winogrande|5": {
"acc": 0.7703235990528808,
"acc_stderr": 0.011821645601838238
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r4
|
[
"region:us"
] |
2023-09-12T09:37:42+00:00
|
{"pretty_name": "Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4", "dataset_summary": "Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r4\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T22:27:59.463715](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r4/blob/main/results_2023-10-25T22-27-59.463715.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.16411493288590603,\n \"em_stderr\": 0.0037930361727013637,\n \"f1\": 0.20782508389261733,\n \"f1_stderr\": 0.0038264002899183395,\n \"acc\": 0.4511208594202994,\n \"acc_stderr\": 0.010571455427847876\n },\n \"harness|drop|3\": {\n \"em\": 0.16411493288590603,\n \"em_stderr\": 0.0037930361727013637,\n \"f1\": 0.20782508389261733,\n \"f1_stderr\": 0.0038264002899183395\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13191811978771797,\n \"acc_stderr\": 0.009321265253857515\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838238\n }\n}\n```", "repo_url": "https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|arc:challenge|25_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T22_27_59.463715", "path": ["**/details_harness|drop|3_2023-10-25T22-27-59.463715.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T22-27-59.463715.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T22_27_59.463715", "path": ["**/details_harness|gsm8k|5_2023-10-25T22-27-59.463715.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T22-27-59.463715.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|hellaswag|10_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": 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["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T10-37-25.589822.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T10-37-25.589822.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_25T22_27_59.463715", "path": ["**/details_harness|winogrande|5_2023-10-25T22-27-59.463715.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-25T22-27-59.463715.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_12T10_37_25.589822", "path": ["results_2023-09-12T10-37-25.589822.parquet"]}, {"split": "2023_10_25T22_27_59.463715", "path": ["results_2023-10-25T22-27-59.463715.parquet"]}, {"split": "latest", "path": ["results_2023-10-25T22-27-59.463715.parquet"]}]}]}
|
2023-10-25T21:28:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T22:27:59.463715(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T22:27:59.463715(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T22:27:59.463715(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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5,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T22:27:59.463715(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
ca7faae33562f19a339c5ae1c05f09eccbd41788
|
# Dataset of elisabeth_bathory_brave/エリザベート・バートリー〔ブレイブ〕/伊丽莎白·巴托里〔勇者〕 (Fate/Grand Order)
This is the dataset of elisabeth_bathory_brave/エリザベート・バートリー〔ブレイブ〕/伊丽莎白·巴托里〔勇者〕 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `pink_hair, long_hair, blue_eyes, pointy_ears, horns, tail, dragon_horns, bangs, dragon_tail, curled_horns, dragon_girl, ribbon, two_side_up, breasts, small_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 771.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elisabeth_bathory_brave_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 429.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elisabeth_bathory_brave_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1284 | 948.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elisabeth_bathory_brave_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 678.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elisabeth_bathory_brave_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1284 | 1.32 GiB | [Download](https://huggingface.co/datasets/CyberHarem/elisabeth_bathory_brave_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/elisabeth_bathory_brave_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, detached_sleeves, looking_at_viewer, open_mouth, smile, solo, blush, plaid_skirt, holding_microphone, ;d, one_eye_closed, fang, corset, microphone_stand, tail_ornament, bare_shoulders, heart |
| 1 | 6 |  |  |  |  |  | 1girl, :d, bare_shoulders, blush, corset, detached_sleeves, hair_ribbon, looking_at_viewer, open_mouth, plaid_skirt, solo, fang, simple_background, white_background |
| 2 | 6 |  |  |  |  |  | 1girl, detached_sleeves, looking_at_viewer, simple_background, solo, white_background, open_mouth, :d, black_dress, holding_microphone |
| 3 | 6 |  |  |  |  |  | 1girl, detached_sleeves, looking_at_viewer, smile, solo, black_dress, closed_mouth, blush |
| 4 | 5 |  |  |  |  |  | 1girl, detached_sleeves, hair_between_eyes, looking_at_viewer, official_alternate_costume, solo, holding_umbrella, long_sleeves, oil-paper_umbrella, open_mouth, purple_kimono, wide_sleeves, :d, blush, hair_ribbon, purple_umbrella, streaked_hair, v-shaped_eyebrows, black_sleeves, bow, hair_scrunchie, oni_horns, purple_dress, purple_scrunchie, purple_sleeves, sidelocks, skin_fang, sleeveless_kimono, sleeves_past_wrists, very_long_hair, white_hair, wings |
| 5 | 13 |  |  |  |  |  | 1girl, detached_sleeves, dress_flower, hat_flower, holding_microphone, looking_at_viewer, solo, striped_headwear, top_hat, vertical-striped_dress, frilled_dress, blush, hair_between_eyes, microphone_stand, pink_dress, pink_headwear, pink_rose, pig, sleeveless_dress, layered_dress, long_sleeves, open_mouth, fang, simple_background, squirrel, white_background, :d, earrings, polka_dot_dress, wrist_cuffs, animal, v-shaped_eyebrows, very_long_hair |
| 6 | 14 |  |  |  |  |  | 1girl, solo, witch_hat, looking_at_viewer, jack-o'-lantern, detached_sleeves, vertical-striped_dress, open_mouth, choker, earrings, pumpkin, bat_wings, black_thighhighs, :d, demon_tail, fang, halloween_costume, star_print, blush, holding, horns_through_headwear, polearm |
| 7 | 8 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, collarbone, hair_ribbon, white_bikini, frilled_bikini, navel, simple_background, white_background, open_mouth, smile, ;d, bare_shoulders, closed_mouth, cloud, day, fang, ocean, official_alternate_costume, one_eye_closed, outdoors, sky |
| 8 | 6 |  |  |  |  |  | 1girl, bikini_armor, black_thighhighs, hair_ribbon, navel, oversized_clothes, red_bikini, solo, tiara, vambraces, white_background, blush, looking_at_viewer, pauldrons, red_armor, silver_trim, simple_background, white_cape, hair_between_eyes, armored_boots, blue_ribbon, closed_mouth, gauntlets, purple_ribbon, red_choker, very_long_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | detached_sleeves | looking_at_viewer | open_mouth | smile | solo | blush | plaid_skirt | holding_microphone | ;d | one_eye_closed | fang | corset | microphone_stand | tail_ornament | bare_shoulders | heart | :d | hair_ribbon | simple_background | white_background | black_dress | closed_mouth | hair_between_eyes | official_alternate_costume | holding_umbrella | long_sleeves | oil-paper_umbrella | purple_kimono | wide_sleeves | purple_umbrella | streaked_hair | v-shaped_eyebrows | black_sleeves | bow | hair_scrunchie | oni_horns | purple_dress | purple_scrunchie | purple_sleeves | sidelocks | skin_fang | sleeveless_kimono | sleeves_past_wrists | very_long_hair | white_hair | wings | dress_flower | hat_flower | striped_headwear | top_hat | vertical-striped_dress | frilled_dress | pink_dress | pink_headwear | pink_rose | pig | sleeveless_dress | layered_dress | squirrel | earrings | polka_dot_dress | wrist_cuffs | animal | witch_hat | jack-o'-lantern | choker | pumpkin | bat_wings | black_thighhighs | demon_tail | halloween_costume | star_print | holding | horns_through_headwear | polearm | collarbone | white_bikini | frilled_bikini | navel | cloud | day | ocean | outdoors | sky | bikini_armor | oversized_clothes | red_bikini | tiara | vambraces | pauldrons | red_armor | silver_trim | white_cape | armored_boots | blue_ribbon | gauntlets | purple_ribbon | red_choker |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:--------------------|:-------------|:--------|:-------|:--------|:--------------|:---------------------|:-----|:-----------------|:-------|:---------|:-------------------|:----------------|:-----------------|:--------|:-----|:--------------|:--------------------|:-------------------|:--------------|:---------------|:--------------------|:-----------------------------|:-------------------|:---------------|:---------------------|:----------------|:---------------|:------------------|:----------------|:--------------------|:----------------|:------|:-----------------|:------------|:---------------|:-------------------|:-----------------|:------------|:------------|:--------------------|:----------------------|:-----------------|:-------------|:--------|:---------------|:-------------|:-------------------|:----------|:-------------------------|:----------------|:-------------|:----------------|:------------|:------|:-------------------|:----------------|:-----------|:-----------|:------------------|:--------------|:---------|:------------|:------------------|:---------|:----------|:------------|:-------------------|:-------------|:--------------------|:-------------|:----------|:-------------------------|:----------|:-------------|:---------------|:-----------------|:--------|:--------|:------|:--------|:-----------|:------|:---------------|:--------------------|:-------------|:--------|:------------|:------------|:------------|:--------------|:-------------|:----------------|:--------------|:------------|:----------------|:-------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | | X | X | X | | | | X | X | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | | X | | | X | | | | | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | | X | X | X | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | | | | | | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 13 |  |  |  |  |  | X | X | X | X | | X | X | | X | | | X | | X | | | | X | | X | X | | | X | | | X | | | | | | X | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 14 |  |  |  |  |  | X | X | X | X | | X | X | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | X | | X | X | X | X | X | | | X | X | X | | | | X | | | X | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | | X | | | X | X | | | | | | | | | | | | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/elisabeth_bathory_brave_fgo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T09:37:57+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-13T08:22:10+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of elisabeth\_bathory\_brave/エリザベート・バートリー〔ブレイブ〕/伊丽莎白·巴托里〔勇者〕 (Fate/Grand Order)
=======================================================================================
This is the dataset of elisabeth\_bathory\_brave/エリザベート・バートリー〔ブレイブ〕/伊丽莎白·巴托里〔勇者〕 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are 'pink\_hair, long\_hair, blue\_eyes, pointy\_ears, horns, tail, dragon\_horns, bangs, dragon\_tail, curled\_horns, dragon\_girl, ribbon, two\_side\_up, breasts, small\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
987853484c9bdd8c2585e1da11700695b150ad15
|
# Dataset Card for Evaluation run of guardrail/llama-2-7b-guanaco-instruct-sharded
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/guardrail/llama-2-7b-guanaco-instruct-sharded
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [guardrail/llama-2-7b-guanaco-instruct-sharded](https://huggingface.co/guardrail/llama-2-7b-guanaco-instruct-sharded) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_guardrail__llama-2-7b-guanaco-instruct-sharded",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T13:49:18.537687](https://huggingface.co/datasets/open-llm-leaderboard/details_guardrail__llama-2-7b-guanaco-instruct-sharded/blob/main/results_2023-10-23T13-49-18.537687.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.04498741610738255,
"em_stderr": 0.00212270539302231,
"f1": 0.10647126677852378,
"f1_stderr": 0.0025116486687068954,
"acc": 0.4021070828693379,
"acc_stderr": 0.009961973606864256
},
"harness|drop|3": {
"em": 0.04498741610738255,
"em_stderr": 0.00212270539302231,
"f1": 0.10647126677852378,
"f1_stderr": 0.0025116486687068954
},
"harness|gsm8k|5": {
"acc": 0.07808946171341925,
"acc_stderr": 0.007390654481108218
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620292
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_guardrail__llama-2-7b-guanaco-instruct-sharded
|
[
"region:us"
] |
2023-09-12T09:44:27+00:00
|
{"pretty_name": "Evaluation run of guardrail/llama-2-7b-guanaco-instruct-sharded", "dataset_summary": "Dataset automatically created during the evaluation run of model [guardrail/llama-2-7b-guanaco-instruct-sharded](https://huggingface.co/guardrail/llama-2-7b-guanaco-instruct-sharded) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_guardrail__llama-2-7b-guanaco-instruct-sharded\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T13:49:18.537687](https://huggingface.co/datasets/open-llm-leaderboard/details_guardrail__llama-2-7b-guanaco-instruct-sharded/blob/main/results_2023-10-23T13-49-18.537687.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.04498741610738255,\n \"em_stderr\": 0.00212270539302231,\n \"f1\": 0.10647126677852378,\n \"f1_stderr\": 0.0025116486687068954,\n \"acc\": 0.4021070828693379,\n \"acc_stderr\": 0.009961973606864256\n },\n \"harness|drop|3\": {\n \"em\": 0.04498741610738255,\n \"em_stderr\": 0.00212270539302231,\n \"f1\": 0.10647126677852378,\n \"f1_stderr\": 0.0025116486687068954\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07808946171341925,\n \"acc_stderr\": 0.007390654481108218\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620292\n }\n}\n```", "repo_url": "https://huggingface.co/guardrail/llama-2-7b-guanaco-instruct-sharded", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T10_44_14.063451", "path": ["**/details_harness|arc:challenge|25_2023-09-12T10-44-14.063451.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T10-44-14.063451.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T13_49_18.537687", "path": ["**/details_harness|drop|3_2023-10-23T13-49-18.537687.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T13-49-18.537687.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T13_49_18.537687", "path": ["**/details_harness|gsm8k|5_2023-10-23T13-49-18.537687.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T13-49-18.537687.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T10_44_14.063451", "path": ["**/details_harness|hellaswag|10_2023-09-12T10-44-14.063451.parquet"]}, {"split": "latest", "path": 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|
2023-10-23T12:49:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of guardrail/llama-2-7b-guanaco-instruct-sharded
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model guardrail/llama-2-7b-guanaco-instruct-sharded on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T13:49:18.537687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of guardrail/llama-2-7b-guanaco-instruct-sharded",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model guardrail/llama-2-7b-guanaco-instruct-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T13:49:18.537687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of guardrail/llama-2-7b-guanaco-instruct-sharded",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model guardrail/llama-2-7b-guanaco-instruct-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T13:49:18.537687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of guardrail/llama-2-7b-guanaco-instruct-sharded## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model guardrail/llama-2-7b-guanaco-instruct-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T13:49:18.537687(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
f17abfb3d18ca67983df536e56b177e9c44fb546
|
# OCR GENERATED Machine-Readable Zone (MRZ) Text Detection
The dataset includes a collection of **GENERATED** photos containing Machine Readable Zones (MRZ) commonly found on identification documents such as passports, visas, and ID cards. Each photo in the dataset is accompanied by **text detection** and **Optical Character Recognition (OCR)** results.
This dataset is useful for developing applications related to *document verification, identity authentication, or automated data extraction from identification documents*.
### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-generated-machine-readable-zone-mrz-text-detection) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of documents
- **boxes** - includes bounding box labeling for the original images
- **annotations.xml** - contains coordinates of the bounding boxes and detected text, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes and detected text . For each point, the x and y coordinates are provided.
# Example of XML file structure
.png?generation=1694514503035476&alt=media)
# Text Detection in the Documents might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-generated-machine-readable-zone-mrz-text-detection) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
TrainingDataPro/ocr-generated-machine-readable-zone-mrz-text-detection
|
[
"task_categories:image-to-text",
"task_categories:object-detection",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"legal",
"region:us"
] |
2023-09-12T09:47:34+00:00
|
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-text", "object-detection"], "tags": ["code", "legal"]}
|
2023-09-14T15:18:14+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-to-text #task_categories-object-detection #language-English #license-cc-by-nc-nd-4.0 #code #legal #region-us
|
# OCR GENERATED Machine-Readable Zone (MRZ) Text Detection
The dataset includes a collection of GENERATED photos containing Machine Readable Zones (MRZ) commonly found on identification documents such as passports, visas, and ID cards. Each photo in the dataset is accompanied by text detection and Optical Character Recognition (OCR) results.
This dataset is useful for developing applications related to *document verification, identity authentication, or automated data extraction from identification documents*.
### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.
![](URL
# Get the dataset
### This is just an example of the data
Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- images - contains of original images of documents
- boxes - includes bounding box labeling for the original images
- URL - contains coordinates of the bounding boxes and detected text, created for the original photo
# Data Format
Each image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and detected text . For each point, the x and y coordinates are provided.
# Example of XML file structure
![](URL
# Text Detection in the Documents might be made in accordance with your requirements.
## TrainingData provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: URL
TrainingData's GitHub: URL
|
[
"# OCR GENERATED Machine-Readable Zone (MRZ) Text Detection\nThe dataset includes a collection of GENERATED photos containing Machine Readable Zones (MRZ) commonly found on identification documents such as passports, visas, and ID cards. Each photo in the dataset is accompanied by text detection and Optical Character Recognition (OCR) results.\n\nThis dataset is useful for developing applications related to *document verification, identity authentication, or automated data extraction from identification documents*.",
"### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Dataset structure\n- images - contains of original images of documents\n- boxes - includes bounding box labeling for the original images\n- URL - contains coordinates of the bounding boxes and detected text, created for the original photo",
"# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and detected text . For each point, the x and y coordinates are provided.",
"# Example of XML file structure\n![](URL",
"# Text Detection in the Documents might be made in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
"TAGS\n#task_categories-image-to-text #task_categories-object-detection #language-English #license-cc-by-nc-nd-4.0 #code #legal #region-us \n",
"# OCR GENERATED Machine-Readable Zone (MRZ) Text Detection\nThe dataset includes a collection of GENERATED photos containing Machine Readable Zones (MRZ) commonly found on identification documents such as passports, visas, and ID cards. Each photo in the dataset is accompanied by text detection and Optical Character Recognition (OCR) results.\n\nThis dataset is useful for developing applications related to *document verification, identity authentication, or automated data extraction from identification documents*.",
"### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Dataset structure\n- images - contains of original images of documents\n- boxes - includes bounding box labeling for the original images\n- URL - contains coordinates of the bounding boxes and detected text, created for the original photo",
"# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and detected text . For each point, the x and y coordinates are provided.",
"# Example of XML file structure\n![](URL",
"# Text Detection in the Documents might be made in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
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] |
[
"passage: TAGS\n#task_categories-image-to-text #task_categories-object-detection #language-English #license-cc-by-nc-nd-4.0 #code #legal #region-us \n# OCR GENERATED Machine-Readable Zone (MRZ) Text Detection\nThe dataset includes a collection of GENERATED photos containing Machine Readable Zones (MRZ) commonly found on identification documents such as passports, visas, and ID cards. Each photo in the dataset is accompanied by text detection and Optical Character Recognition (OCR) results.\n\nThis dataset is useful for developing applications related to *document verification, identity authentication, or automated data extraction from identification documents*.### The dataset is solely for informational or educational purposes and should not be used for any fraudulent or deceptive activities.\n\n![](URL# Get the dataset### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.# Dataset structure\n- images - contains of original images of documents\n- boxes - includes bounding box labeling for the original images\n- URL - contains coordinates of the bounding boxes and detected text, created for the original photo# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and detected text . For each point, the x and y coordinates are provided.# Example of XML file structure\n![](URL# Text Detection in the Documents might be made in accordance with your requirements.## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
985c71d5aa25ab70bfabfa7bcc7100ba7da607ac
|
# Touch Rugby Rules Dataset
train.csv is comprised of a set of questions based on rules from the [International Touch Website](https://cdn.internationaltouch.org/public/FIT%205th%20Edition%20Rulebook.pdf)
For educational and non-commercial use only.
|
Trelis/touch-rugby-rules
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"fine-tuning",
"touch rugby",
"region:us"
] |
2023-09-12T09:55:36+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["fine-tuning", "touch rugby"]}
|
2023-09-30T12:16:06+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #touch rugby #region-us
|
# Touch Rugby Rules Dataset
URL is comprised of a set of questions based on rules from the International Touch Website
For educational and non-commercial use only.
|
[
"# Touch Rugby Rules Dataset\n\nURL is comprised of a set of questions based on rules from the International Touch Website\n\nFor educational and non-commercial use only."
] |
[
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #touch rugby #region-us \n",
"# Touch Rugby Rules Dataset\n\nURL is comprised of a set of questions based on rules from the International Touch Website\n\nFor educational and non-commercial use only."
] |
[
39,
35
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #touch rugby #region-us \n# Touch Rugby Rules Dataset\n\nURL is comprised of a set of questions based on rules from the International Touch Website\n\nFor educational and non-commercial use only."
] |
b81b656899b2c0e79a5b4beff1bc01f99522cedb
|
# Dataset Card for "TruckDet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
malteee/TruckDet1
|
[
"region:us"
] |
2023-09-12T10:06:47+00:00
|
{"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}, {"name": "id", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 78780289.0, "num_examples": 651}], "download_size": 78781526, "dataset_size": 78780289.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-12T12:44:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TruckDet"
More Information needed
|
[
"# Dataset Card for \"TruckDet\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TruckDet\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TruckDet\"\n\nMore Information needed"
] |
8a58d15362b6bf0dc374176037d26a037ee6aaec
|
# Dataset Card for "704dc3cf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/704dc3cf
|
[
"region:us"
] |
2023-09-12T10:24:45+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1340, "dataset_size": 182}}
|
2023-09-12T10:24:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "704dc3cf"
More Information needed
|
[
"# Dataset Card for \"704dc3cf\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"704dc3cf\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"704dc3cf\"\n\nMore Information needed"
] |
3ceb7e892cec156a963c257bb55d0e802a489344
|
# Dataset Card for "medmcqa_alpaca_format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
maximegmd/medmcqa_alpaca_format
|
[
"region:us"
] |
2023-09-12T10:28:37+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 120644997, "num_examples": 182822}, {"name": "test", "num_bytes": 1077057, "num_examples": 6150}, {"name": "validation", "num_bytes": 2009220, "num_examples": 4183}], "download_size": 79503290, "dataset_size": 123731274}}
|
2023-09-12T10:29:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "medmcqa_alpaca_format"
More Information needed
|
[
"# Dataset Card for \"medmcqa_alpaca_format\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"medmcqa_alpaca_format\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"medmcqa_alpaca_format\"\n\nMore Information needed"
] |
ea4c3ecdd19934056fcd168c5dacdf4992519836
|
# Dataset Card for "Soldering-Data-Tiny-Complete-Sentence"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AndyLiu0104/Soldering-Data-Tiny-Complete-Sentence
|
[
"region:us"
] |
2023-09-12T10:34:58+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18013335.875, "num_examples": 10481}], "download_size": 11576663, "dataset_size": 18013335.875}}
|
2023-09-12T13:33:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Soldering-Data-Tiny-Complete-Sentence"
More Information needed
|
[
"# Dataset Card for \"Soldering-Data-Tiny-Complete-Sentence\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Soldering-Data-Tiny-Complete-Sentence\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Soldering-Data-Tiny-Complete-Sentence\"\n\nMore Information needed"
] |
b0750c57aad6b84ab95017e56b71eaf6f7a02287
|
# Dataset Card for Evaluation run of matsuo-lab/weblab-10b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/matsuo-lab/weblab-10b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [matsuo-lab/weblab-10b](https://huggingface.co/matsuo-lab/weblab-10b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_matsuo-lab__weblab-10b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T01:19:08.917295](https://huggingface.co/datasets/open-llm-leaderboard/details_matsuo-lab__weblab-10b/blob/main/results_2023-10-25T01-19-08.917295.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573356817,
"f1": 0.04808515100671153,
"f1_stderr": 0.00120444202294628,
"acc": 0.3197517552042787,
"acc_stderr": 0.008443800220462488
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573356817,
"f1": 0.04808515100671153,
"f1_stderr": 0.00120444202294628
},
"harness|gsm8k|5": {
"acc": 0.014404852160727824,
"acc_stderr": 0.003282055917136976
},
"harness|winogrande|5": {
"acc": 0.6250986582478295,
"acc_stderr": 0.013605544523788001
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_matsuo-lab__weblab-10b
|
[
"region:us"
] |
2023-09-12T10:51:03+00:00
|
{"pretty_name": "Evaluation run of matsuo-lab/weblab-10b", "dataset_summary": "Dataset automatically created during the evaluation run of model [matsuo-lab/weblab-10b](https://huggingface.co/matsuo-lab/weblab-10b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_matsuo-lab__weblab-10b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T01:19:08.917295](https://huggingface.co/datasets/open-llm-leaderboard/details_matsuo-lab__weblab-10b/blob/main/results_2023-10-25T01-19-08.917295.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.00027736144573356817,\n \"f1\": 0.04808515100671153,\n \"f1_stderr\": 0.00120444202294628,\n \"acc\": 0.3197517552042787,\n \"acc_stderr\": 0.008443800220462488\n },\n \"harness|drop|3\": {\n \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.00027736144573356817,\n \"f1\": 0.04808515100671153,\n \"f1_stderr\": 0.00120444202294628\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.014404852160727824,\n \"acc_stderr\": 0.003282055917136976\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6250986582478295,\n \"acc_stderr\": 0.013605544523788001\n }\n}\n```", "repo_url": "https://huggingface.co/matsuo-lab/weblab-10b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T11_50_50.938631", "path": ["**/details_harness|arc:challenge|25_2023-09-12T11-50-50.938631.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T11-50-50.938631.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T01_19_08.917295", "path": ["**/details_harness|drop|3_2023-10-25T01-19-08.917295.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T01-19-08.917295.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T01_19_08.917295", "path": ["**/details_harness|gsm8k|5_2023-10-25T01-19-08.917295.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T01-19-08.917295.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T11_50_50.938631", "path": ["**/details_harness|hellaswag|10_2023-09-12T11-50-50.938631.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T11-50-50.938631.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T11_50_50.938631", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T11-50-50.938631.parquet", 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"**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T11-50-50.938631.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T11-50-50.938631.parquet", 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|
2023-10-25T00:19:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of matsuo-lab/weblab-10b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model matsuo-lab/weblab-10b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T01:19:08.917295(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of matsuo-lab/weblab-10b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model matsuo-lab/weblab-10b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T01:19:08.917295(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of matsuo-lab/weblab-10b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model matsuo-lab/weblab-10b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T01:19:08.917295(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of matsuo-lab/weblab-10b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model matsuo-lab/weblab-10b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T01:19:08.917295(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
80f7c8451bf787b19ea69a5a258c7cad0f806921
|
# Dataset Card for Evaluation run of Undi95/CodeEngine
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Undi95/CodeEngine
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Undi95/CodeEngine](https://huggingface.co/Undi95/CodeEngine) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Undi95__CodeEngine",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T07:16:21.496689](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__CodeEngine/blob/main/results_2023-10-25T07-16-21.496689.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.31008808724832215,
"em_stderr": 0.004736734191590966,
"f1": 0.4059154781879224,
"f1_stderr": 0.004594505528583743,
"acc": 0.38050967793280527,
"acc_stderr": 0.00780116508471732
},
"harness|drop|3": {
"em": 0.31008808724832215,
"em_stderr": 0.004736734191590966,
"f1": 0.4059154781879224,
"f1_stderr": 0.004594505528583743
},
"harness|gsm8k|5": {
"acc": 0.015163002274450341,
"acc_stderr": 0.0033660229497263702
},
"harness|winogrande|5": {
"acc": 0.7458563535911602,
"acc_stderr": 0.012236307219708269
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Undi95__CodeEngine
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[
"region:us"
] |
2023-09-12T10:51:47+00:00
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{"pretty_name": "Evaluation run of Undi95/CodeEngine", "dataset_summary": "Dataset automatically created during the evaluation run of model [Undi95/CodeEngine](https://huggingface.co/Undi95/CodeEngine) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Undi95__CodeEngine\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T07:16:21.496689](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__CodeEngine/blob/main/results_2023-10-25T07-16-21.496689.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.31008808724832215,\n \"em_stderr\": 0.004736734191590966,\n \"f1\": 0.4059154781879224,\n \"f1_stderr\": 0.004594505528583743,\n \"acc\": 0.38050967793280527,\n \"acc_stderr\": 0.00780116508471732\n },\n \"harness|drop|3\": {\n \"em\": 0.31008808724832215,\n \"em_stderr\": 0.004736734191590966,\n \"f1\": 0.4059154781879224,\n \"f1_stderr\": 0.004594505528583743\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.015163002274450341,\n \"acc_stderr\": 0.0033660229497263702\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7458563535911602,\n \"acc_stderr\": 0.012236307219708269\n }\n}\n```", "repo_url": "https://huggingface.co/Undi95/CodeEngine", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T11_51_31.235775", "path": 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2023-10-25T06:16:33+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of Undi95/CodeEngine
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Undi95/CodeEngine on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T07:16:21.496689(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Undi95/CodeEngine",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/CodeEngine on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T07:16:21.496689(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Undi95/CodeEngine",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/CodeEngine on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T07:16:21.496689(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
16,
31,
164,
66,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Undi95/CodeEngine## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Undi95/CodeEngine on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T07:16:21.496689(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
56b04acb03480a7dca68dfb8e18ab63a5ed7d47e
|
# Dataset Card for "korean_STT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pratik33/korean_STT
|
[
"region:us"
] |
2023-09-12T10:52:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 155417701.0, "num_examples": 200}], "download_size": 152729272, "dataset_size": 155417701.0}}
|
2023-09-12T11:07:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "korean_STT"
More Information needed
|
[
"# Dataset Card for \"korean_STT\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"korean_STT\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"korean_STT\"\n\nMore Information needed"
] |
793a5f915c964df600921bb79d0ef053c7e28182
|
# Kanade audio dataset
|
trojblue/rvc-kanade-dataset
|
[
"size_categories:1K<n<10K",
"language:ja",
"license:bigscience-openrail-m",
"audio",
"region:us"
] |
2023-09-12T10:57:53+00:00
|
{"language": ["ja"], "license": "bigscience-openrail-m", "size_categories": ["1K<n<10K"], "tags": ["audio"]}
|
2023-09-13T01:34:45+00:00
|
[] |
[
"ja"
] |
TAGS
#size_categories-1K<n<10K #language-Japanese #license-bigscience-openrail-m #audio #region-us
|
# Kanade audio dataset
|
[
"# Kanade audio dataset"
] |
[
"TAGS\n#size_categories-1K<n<10K #language-Japanese #license-bigscience-openrail-m #audio #region-us \n",
"# Kanade audio dataset"
] |
[
38,
6
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #language-Japanese #license-bigscience-openrail-m #audio #region-us \n# Kanade audio dataset"
] |
69d9713a49e095607641cbf9e846245cc9cad35f
|
# Dataset Card for "wiki5m_ind_bloomz"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ze-lin/wiki5m_ind_bloomz
|
[
"region:us"
] |
2023-09-12T10:58:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "head_id", "dtype": "string"}, {"name": "head", "dtype": "string"}, {"name": "relation", "dtype": "string"}, {"name": "tail_id", "dtype": "string"}, {"name": "tail", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 9623749501, "num_examples": 20481546}, {"name": "valid", "num_bytes": 3185389, "num_examples": 6699}, {"name": "test", "num_bytes": 3273249, "num_examples": 6894}], "download_size": 4266415829, "dataset_size": 9630208139}}
|
2023-09-12T11:04:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wiki5m_ind_bloomz"
More Information needed
|
[
"# Dataset Card for \"wiki5m_ind_bloomz\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki5m_ind_bloomz\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki5m_ind_bloomz\"\n\nMore Information needed"
] |
6d4c6bf598544aea6aba410f76a496e216caa814
|
# Dataset of kris/クリス (Pokémon)
This is the dataset of kris/クリス (Pokémon), containing 425 images and their tags.
The core tags of this character are `twintails, hat, bangs, long_hair, blue_hair, green_hair, yellow_headwear, blue_eyes, green_eyes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 425 | 289.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kris_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 425 | 209.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kris_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 683 | 354.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kris_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 425 | 271.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kris_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 683 | 440.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kris_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kris_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, bike_shorts, cropped_jacket, holding_poke_ball, long_sleeves, poke_ball_(basic), red_shirt, white_jacket, open_jacket, open_mouth, solo, :d, pokemon_(creature) |
| 1 | 13 |  |  |  |  |  | 1girl, red_shirt, white_jacket, simple_background, upper_body, eyelashes, open_jacket, white_background, cropped_jacket, solo, :d, blush, open_mouth, long_sleeves, ^_^, closed_mouth, tongue |
| 2 | 8 |  |  |  |  |  | 1girl, bike_shorts, holding_poke_ball, poke_ball_(basic), pokemon_(creature) |
| 3 | 11 |  |  |  |  |  | 1girl, bike_shorts, smile, pokemon_(creature), open_mouth |
| 4 | 7 |  |  |  |  |  | 1girl, cosplay, hat_ribbon, overalls, red_ribbon, star_earrings, solo, cabbie_hat, smile, blush, poke_ball_(basic), thighhighs |
| 5 | 5 |  |  |  |  |  | 1girl, blush, solo, bike_shorts, one_eye_closed |
| 6 | 8 |  |  |  |  |  | 1girl, hetero, penis, completely_nude, vaginal, 1boy, ass, blush, open_mouth, anus, medium_breasts, nipples, testicles, barefoot, bestiality, cum_in_pussy, pokemon_(creature), pokephilia, solo_focus, looking_back, sex_from_behind |
| 7 | 27 |  |  |  |  |  | official_alternate_costume, 1girl, aqua_eyes, aqua_hair, aqua_dress, bare_shoulders, choker, smile, wrist_cuffs, small_breasts, medium_hair, hair_ornament, halter_dress, shorts_under_dress, collarbone, side_slit, pokemon_(creature), looking_at_viewer, sandals, solo, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bike_shorts | cropped_jacket | holding_poke_ball | long_sleeves | poke_ball_(basic) | red_shirt | white_jacket | open_jacket | open_mouth | solo | :d | pokemon_(creature) | simple_background | upper_body | eyelashes | white_background | blush | ^_^ | closed_mouth | tongue | smile | cosplay | hat_ribbon | overalls | red_ribbon | star_earrings | cabbie_hat | thighhighs | one_eye_closed | hetero | penis | completely_nude | vaginal | 1boy | ass | anus | medium_breasts | nipples | testicles | barefoot | bestiality | cum_in_pussy | pokephilia | solo_focus | looking_back | sex_from_behind | official_alternate_costume | aqua_eyes | aqua_hair | aqua_dress | bare_shoulders | choker | wrist_cuffs | small_breasts | medium_hair | hair_ornament | halter_dress | shorts_under_dress | collarbone | side_slit | looking_at_viewer | sandals |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-----------------|:--------------------|:---------------|:--------------------|:------------|:---------------|:--------------|:-------------|:-------|:-----|:---------------------|:--------------------|:-------------|:------------|:-------------------|:--------|:------|:---------------|:---------|:--------|:----------|:-------------|:-----------|:-------------|:----------------|:-------------|:-------------|:-----------------|:---------|:--------|:------------------|:----------|:-------|:------|:-------|:-----------------|:----------|:------------|:-----------|:-------------|:---------------|:-------------|:-------------|:---------------|:------------------|:-----------------------------|:------------|:------------|:-------------|:-----------------|:---------|:--------------|:----------------|:--------------|:----------------|:---------------|:---------------------|:-------------|:------------|:--------------------|:----------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | | X | | X | | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | | X | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | | | | | | | | X | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | | | | | X | | | | | X | | | | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | | | | | | | | | X | | | X | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 7 | 27 |  |  |  |  |  | X | | | | | | | | | | X | | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/kris_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T10:59:22+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T22:23:27+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kris/クリス (Pokémon)
=============================
This is the dataset of kris/クリス (Pokémon), containing 425 images and their tags.
The core tags of this character are 'twintails, hat, bangs, long\_hair, blue\_hair, green\_hair, yellow\_headwear, blue\_eyes, green\_eyes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
fb2f71692518c186e34c40613e508f2500b7b906
|
# Dataset Card for Evaluation run of ahnyeonchan/OpenOrca-AYT-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ahnyeonchan/OpenOrca-AYT-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ahnyeonchan/OpenOrca-AYT-13B](https://huggingface.co/ahnyeonchan/OpenOrca-AYT-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ahnyeonchan__OpenOrca-AYT-13B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-06T23:00:43.582887](https://huggingface.co/datasets/open-llm-leaderboard/details_ahnyeonchan__OpenOrca-AYT-13B_public/blob/main/results_2023-11-06T23-00-43.582887.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 8.074664429530201e-05,
"f1_stderr": 4.224084408137966e-05,
"acc": 0.24861878453038674,
"acc_stderr": 0.007026135605808218
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 8.074664429530201e-05,
"f1_stderr": 4.224084408137966e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4972375690607735,
"acc_stderr": 0.014052271211616436
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_ahnyeonchan__OpenOrca-AYT-13B
|
[
"region:us"
] |
2023-09-12T10:59:58+00:00
|
{"pretty_name": "Evaluation run of ahnyeonchan/OpenOrca-AYT-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [ahnyeonchan/OpenOrca-AYT-13B](https://huggingface.co/ahnyeonchan/OpenOrca-AYT-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ahnyeonchan__OpenOrca-AYT-13B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-06T23:00:43.582887](https://huggingface.co/datasets/open-llm-leaderboard/details_ahnyeonchan__OpenOrca-AYT-13B_public/blob/main/results_2023-11-06T23-00-43.582887.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 8.074664429530201e-05,\n \"f1_stderr\": 4.224084408137966e-05,\n \"acc\": 0.24861878453038674,\n \"acc_stderr\": 0.007026135605808218\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 8.074664429530201e-05,\n \"f1_stderr\": 4.224084408137966e-05\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.4972375690607735,\n \"acc_stderr\": 0.014052271211616436\n }\n}\n```", "repo_url": "https://huggingface.co/ahnyeonchan/OpenOrca-AYT-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_05T03_37_23.978983", "path": ["**/details_harness|drop|3_2023-11-05T03-37-23.978983.parquet"]}, {"split": "2023_11_06T23_00_43.582887", "path": ["**/details_harness|drop|3_2023-11-06T23-00-43.582887.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-06T23-00-43.582887.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_05T03_37_23.978983", "path": ["**/details_harness|gsm8k|5_2023-11-05T03-37-23.978983.parquet"]}, {"split": "2023_11_06T23_00_43.582887", "path": ["**/details_harness|gsm8k|5_2023-11-06T23-00-43.582887.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-06T23-00-43.582887.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_05T03_37_23.978983", "path": ["**/details_harness|winogrande|5_2023-11-05T03-37-23.978983.parquet"]}, {"split": "2023_11_06T23_00_43.582887", "path": ["**/details_harness|winogrande|5_2023-11-06T23-00-43.582887.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-06T23-00-43.582887.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_05T03_37_23.978983", "path": ["results_2023-11-05T03-37-23.978983.parquet"]}, {"split": "2023_11_06T23_00_43.582887", "path": ["results_2023-11-06T23-00-43.582887.parquet"]}, {"split": "latest", "path": ["results_2023-11-06T23-00-43.582887.parquet"]}]}]}
|
2023-12-01T14:16:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of ahnyeonchan/OpenOrca-AYT-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ahnyeonchan/OpenOrca-AYT-13B on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-06T23:00:43.582887(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of ahnyeonchan/OpenOrca-AYT-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ahnyeonchan/OpenOrca-AYT-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-06T23:00:43.582887(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of ahnyeonchan/OpenOrca-AYT-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ahnyeonchan/OpenOrca-AYT-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-06T23:00:43.582887(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ahnyeonchan/OpenOrca-AYT-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ahnyeonchan/OpenOrca-AYT-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-06T23:00:43.582887(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
3114b0aa665d35d98d331c4add53a948c9cd0b05
|
# Dataset Card for Evaluation run of TFLai/Orca-Nova-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Orca-Nova-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TFLai/Orca-Nova-13B](https://huggingface.co/TFLai/Orca-Nova-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TFLai__Orca-Nova-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T09:46:46.108882](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Orca-Nova-13B/blob/main/results_2023-10-28T09-46-46.108882.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003984899328859061,
"em_stderr": 0.0006451805848102392,
"f1": 0.07519924496644283,
"f1_stderr": 0.0016030527256702374,
"acc": 0.46032756632616734,
"acc_stderr": 0.010706817769913408
},
"harness|drop|3": {
"em": 0.003984899328859061,
"em_stderr": 0.0006451805848102392,
"f1": 0.07519924496644283,
"f1_stderr": 0.0016030527256702374
},
"harness|gsm8k|5": {
"acc": 0.14480667172100076,
"acc_stderr": 0.009693234799052708
},
"harness|winogrande|5": {
"acc": 0.7758484609313339,
"acc_stderr": 0.011720400740774108
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_TFLai__Orca-Nova-13B
|
[
"region:us"
] |
2023-09-12T11:06:06+00:00
|
{"pretty_name": "Evaluation run of TFLai/Orca-Nova-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/Orca-Nova-13B](https://huggingface.co/TFLai/Orca-Nova-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TFLai__Orca-Nova-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T09:46:46.108882](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Orca-Nova-13B/blob/main/results_2023-10-28T09-46-46.108882.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003984899328859061,\n \"em_stderr\": 0.0006451805848102392,\n \"f1\": 0.07519924496644283,\n \"f1_stderr\": 0.0016030527256702374,\n \"acc\": 0.46032756632616734,\n \"acc_stderr\": 0.010706817769913408\n },\n \"harness|drop|3\": {\n \"em\": 0.003984899328859061,\n \"em_stderr\": 0.0006451805848102392,\n \"f1\": 0.07519924496644283,\n \"f1_stderr\": 0.0016030527256702374\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14480667172100076,\n \"acc_stderr\": 0.009693234799052708\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7758484609313339,\n \"acc_stderr\": 0.011720400740774108\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/Orca-Nova-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T12_05_50.844177", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-05-50.844177.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-05-50.844177.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T09_46_46.108882", "path": ["**/details_harness|drop|3_2023-10-28T09-46-46.108882.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T09-46-46.108882.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T09_46_46.108882", "path": ["**/details_harness|gsm8k|5_2023-10-28T09-46-46.108882.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T09-46-46.108882.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T12_05_50.844177", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-05-50.844177.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-05-50.844177.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T12_05_50.844177", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T12-05-50.844177.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T12-05-50.844177.parquet", 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|
2023-10-28T08:46:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/Orca-Nova-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/Orca-Nova-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T09:46:46.108882(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of TFLai/Orca-Nova-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model TFLai/Orca-Nova-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T09:46:46.108882(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of TFLai/Orca-Nova-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model TFLai/Orca-Nova-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T09:46:46.108882(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TFLai/Orca-Nova-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model TFLai/Orca-Nova-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T09:46:46.108882(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
03b19a361495e554eeb6aabd4962d29ae6e52523
|
# Dataset of nitta_minami/新田美波 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of nitta_minami/新田美波 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, brown_eyes, breasts, bangs, medium_breasts, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 732.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nitta_minami_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 401.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nitta_minami_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1244 | 866.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nitta_minami_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 637.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nitta_minami_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1244 | 1.23 GiB | [Download](https://huggingface.co/datasets/CyberHarem/nitta_minami_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nitta_minami_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, earrings, hair_flower, bare_shoulders, cleavage, collarbone, open_mouth, sleeveless_dress, white_dress, blue_dress, blush, braid, choker, :d, white_background |
| 1 | 9 |  |  |  |  |  | 1girl, bikini, cleavage, smile, solo, navel, blush, open_mouth, looking_at_viewer, necklace |
| 2 | 9 |  |  |  |  |  | 1girl, blue_skirt, cleavage, looking_at_viewer, midriff, navel, solo, visor_cap, blush, short_sleeves, blue_bikini, collarbone, cowboy_shot, crop_top, miniskirt, necklace, open_mouth, :d, headset, stomach, white_background, belt, bracelet, heart_tattoo, simple_background, wrist_cuffs, cropped_jacket, floating_hair, highleg, standing |
| 3 | 15 |  |  |  |  |  | 1girl, blush, looking_at_viewer, nude, smile, solo, nipples, collarbone, navel, water |
| 4 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, competition_swimsuit, solo, blue_one-piece_swimsuit, covered_navel, smile, collarbone, blush, simple_background, white_background, cleavage, cowboy_shot, highleg_swimsuit, open_mouth, wet |
| 5 | 6 |  |  |  |  |  | 1girl, ass, competition_swimsuit, from_behind, looking_at_viewer, looking_back, solo, blue_one-piece_swimsuit, blush, wet, parted_lips, simple_background |
| 6 | 18 |  |  |  |  |  | 1girl, blush, hetero, nipples, sex, solo_focus, 1boy, vaginal, sweat, navel, open_mouth, penis, spread_legs, mosaic_censoring, completely_nude, looking_at_viewer, pov, collarbone, girl_on_top, cowgirl_position, cum_in_pussy, female_pubic_hair, on_back |
| 7 | 6 |  |  |  |  |  | 1girl, blush, collarbone, looking_at_viewer, solo, navel, panties, smile, cleavage, underwear_only, white_bra |
| 8 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, simple_background, solo, sweat, white_background, blue_pants, open_mouth, blue_jacket, cleavage, collarbone, track_jacket, track_pants, :o, ponytail, smile |
| 9 | 5 |  |  |  |  |  | 1girl, blush, earrings, looking_at_viewer, maid_headdress, solo, black_thighhighs, cleavage, corset, smile, whip, apron, frills, bare_shoulders, fishnet_thighhighs, garter_straps, skirt |
| 10 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, necklace, shiny_hair, simple_background, solo, straight_hair, white_background, blush, collarbone, smile, closed_mouth, earrings, upper_body, very_long_hair, white_shirt, blue_shirt, blue_skirt, bracelet, collared_shirt, dress_shirt, full_body, high_heels, open_mouth, short_sleeves, signature, standing, striped_skirt, vertical_stripes |
| 11 | 5 |  |  |  |  |  | 1girl, blush, crown_braid, heart_earrings, looking_at_viewer, nail_polish, red_nails, solo, bare_shoulders, cleavage, detached_sleeves, black_bow, bowtie, collarbone, dress, tattoo, black_thighhighs, double_bun, hair_ribbon, long_sleeves, sitting, skirt, smile, upper_body |
| 12 | 6 |  |  |  |  |  | 1girl, bare_shoulders, blush, looking_at_viewer, playboy_bunny, solo, black_leotard, fake_animal_ears, rabbit_ears, black_pantyhose, open_mouth, smile, strapless_leotard, ass, cowboy_shot, detached_collar, looking_back, simple_background, white_background, wrist_cuffs |
| 13 | 7 |  |  |  |  |  | 1girl, epaulettes, naval_uniform, solo, looking_at_viewer, military_hat, peaked_cap, torn_pants, aiguillette, smile, flag |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | earrings | hair_flower | bare_shoulders | cleavage | collarbone | open_mouth | sleeveless_dress | white_dress | blue_dress | blush | braid | choker | :d | white_background | bikini | smile | navel | necklace | blue_skirt | midriff | visor_cap | short_sleeves | blue_bikini | cowboy_shot | crop_top | miniskirt | headset | stomach | belt | bracelet | heart_tattoo | simple_background | wrist_cuffs | cropped_jacket | floating_hair | highleg | standing | nude | nipples | water | competition_swimsuit | blue_one-piece_swimsuit | covered_navel | highleg_swimsuit | wet | ass | from_behind | looking_back | parted_lips | hetero | sex | solo_focus | 1boy | vaginal | sweat | penis | spread_legs | mosaic_censoring | completely_nude | pov | girl_on_top | cowgirl_position | cum_in_pussy | female_pubic_hair | on_back | panties | underwear_only | white_bra | blue_pants | blue_jacket | track_jacket | track_pants | :o | ponytail | maid_headdress | black_thighhighs | corset | whip | apron | frills | fishnet_thighhighs | garter_straps | skirt | shiny_hair | straight_hair | closed_mouth | upper_body | very_long_hair | white_shirt | blue_shirt | collared_shirt | dress_shirt | full_body | high_heels | signature | striped_skirt | vertical_stripes | crown_braid | heart_earrings | nail_polish | red_nails | detached_sleeves | black_bow | bowtie | dress | tattoo | double_bun | hair_ribbon | long_sleeves | sitting | playboy_bunny | black_leotard | fake_animal_ears | rabbit_ears | black_pantyhose | strapless_leotard | detached_collar | epaulettes | naval_uniform | military_hat | peaked_cap | torn_pants | aiguillette | flag |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:-------|:-----------|:--------------|:-----------------|:-----------|:-------------|:-------------|:-------------------|:--------------|:-------------|:--------|:--------|:---------|:-----|:-------------------|:---------|:--------|:--------|:-----------|:-------------|:----------|:------------|:----------------|:--------------|:--------------|:-----------|:------------|:----------|:----------|:-------|:-----------|:---------------|:--------------------|:--------------|:-----------------|:----------------|:----------|:-----------|:-------|:----------|:--------|:-----------------------|:--------------------------|:----------------|:-------------------|:------|:------|:--------------|:---------------|:--------------|:---------|:------|:-------------|:-------|:----------|:--------|:--------|:--------------|:-------------------|:------------------|:------|:--------------|:-------------------|:---------------|:--------------------|:----------|:----------|:-----------------|:------------|:-------------|:--------------|:---------------|:--------------|:-----|:-----------|:-----------------|:-------------------|:---------|:-------|:--------|:---------|:---------------------|:----------------|:--------|:-------------|:----------------|:---------------|:-------------|:-----------------|:--------------|:-------------|:-----------------|:--------------|:------------|:-------------|:------------|:----------------|:-------------------|:--------------|:-----------------|:--------------|:------------|:-------------------|:------------|:---------|:--------|:---------|:-------------|:--------------|:---------------|:----------|:----------------|:----------------|:-------------------|:--------------|:------------------|:--------------------|:------------------|:-------------|:----------------|:---------------|:-------------|:-------------|:--------------|:-------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | | | | X | | X | | | | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | X | | | | X | X | X | | | | X | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 15 |  |  |  |  |  | X | X | X | | | | | X | | | | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 16 |  |  |  |  |  | X | X | X | | | | X | X | X | | | | X | | | | X | | X | | | | | | | | X | | | | | | | | X | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 18 |  |  |  |  |  | X | X | | | | | | X | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | X | X | | | | X | X | | | | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | X | X | | | | X | X | X | | | | X | | | | X | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | X | X | | | | X | X | | | | X | | | | X | | X | | X | X | | | X | | | | | | | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 5 |  |  |  |  |  | X | X | X | | | X | X | X | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 12 | 6 |  |  |  |  |  | X | X | X | | | X | | | X | | | | X | | | | X | | X | | | | | | | | X | | | | | | | | X | X | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | |
| 13 | 7 |  |  |  |  |  | X | X | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X |
|
CyberHarem/nitta_minami_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T11:08:32+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T12:34:33+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of nitta\_minami/新田美波 (THE iDOLM@STER: Cinderella Girls)
================================================================
This is the dataset of nitta\_minami/新田美波 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, breasts, bangs, medium\_breasts, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
4da71616b56993bf080c592ce87528f86f01c576
|
# Dataset of jeanne_d_arc_alter_santa_lily/ジャンヌ・ダルク・オルタ・サンタ・リリィ/贞德·Alter·Santa·Lily (Fate/Grand Order)
This is the dataset of jeanne_d_arc_alter_santa_lily/ジャンヌ・ダルク・オルタ・サンタ・リリィ/贞德·Alter·Santa·Lily (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `yellow_eyes, breasts, bangs, ahoge, large_breasts, white_hair, long_hair, very_long_hair, short_hair, hair_between_eyes, grey_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:------------|:-----------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 851.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_alter_santa_lily_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 478.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_alter_santa_lily_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1291 | 1009.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_alter_santa_lily_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 755.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_alter_santa_lily_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1291 | 1.38 GiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_alter_santa_lily_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/jeanne_d_arc_alter_santa_lily_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, bare_shoulders, chain, headpiece, solo, armored_dress, fur_trim, gauntlets, looking_at_viewer, smile, black_gloves, navel_cutout, thighhighs, cleavage, elbow_gloves, holding_sword, parted_lips, blonde_hair, armored_boots, black_dress, flag |
| 1 | 5 |  |  |  |  |  | 1girl, armored_dress, black_thighhighs, cape, gauntlets, headpiece, looking_at_viewer, solo, black_dress, blonde_hair, flag, holding_sword, parted_lips, fur_trim, smile, standing |
| 2 | 10 |  |  |  |  |  | 1girl, armored_dress, black_dress, black_thighhighs, chain, flag, gauntlets, headpiece, solo, looking_at_viewer, smile, fur-trimmed_cape, fur_collar, holding_sword, black_cape |
| 3 | 9 |  |  |  |  |  | 1girl, black_dress, coat, fur_trim, jacket, looking_at_viewer, official_alternate_costume, solo, short_dress, open_clothes, grin, long_sleeves, holding_sword, necklace, thighs |
| 4 | 5 |  |  |  |  |  | 1girl, black_dress, black_footwear, knee_boots, long_sleeves, looking_at_viewer, official_alternate_costume, short_dress, sitting, solo, crossed_legs, fur-trimmed_coat, fur-trimmed_sleeves, jacket, smile, blue_coat, necklace, open_coat, thighs |
| 5 | 6 |  |  |  |  |  | 1girl, black_dress, cleavage, collarbone, long_sleeves, official_alternate_costume, solo, looking_at_viewer, necklace, short_dress, blue_coat, blush, cowboy_shot, fur-trimmed_coat, fur-trimmed_sleeves, jacket, open_coat, open_mouth, simple_background, white_background |
| 6 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, bare_shoulders, black_gloves, elbow_gloves, blush, cleavage, hair_flower, black_thighhighs, official_alternate_costume, strapless_dress, drinking_glass, choker, holding, ribbon, smile, black_dress, collarbone, purple_dress, sitting |
| 7 | 6 |  |  |  |  |  | 1girl, black_bra, cleavage, collarbone, looking_at_viewer, solo, blush, black_panties, closed_mouth, navel |
| 8 | 13 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, bare_shoulders, collarbone, lingerie, navel, thighs, black_panties, blush, choker, garter_belt, black_gloves, black_thighhighs, bra, babydoll, closed_mouth, jewelry, pale_skin, simple_background, smile, underwear_only, cosplay, see-through, white_background |
| 9 | 8 |  |  |  |  |  | 1girl, bare_shoulders, black_bikini, blush, cleavage, looking_at_viewer, solo, blue_sky, choker, collarbone, day, navel, outdoors, thighs, beach, closed_mouth, o-ring_bikini, ocean, armpits, arms_behind_head, arms_up, barefoot, braid, kneeling, necklace |
| 10 | 5 |  |  |  |  |  | 1girl, bare_shoulders, black_bikini, collarbone, solo, thighs, blush, cleavage, looking_at_viewer, necklace, official_alternate_costume, bracelet, navel, sarong, twin_braids, multiple_braids, sitting, smile |
| 11 | 10 |  |  |  |  |  | 1girl, black_bikini, black_gloves, black_jacket, cleavage, katana, looking_at_viewer, navel, o-ring_bikini, shrug_(clothing), solo, simple_background, white_background, black_choker, long_sleeves, o-ring_top, holding_sword, o-ring_bottom, smile, thigh_strap, collarbone, cowboy_shot, cropped_jacket, standing, closed_mouth, sheathed |
| 12 | 6 |  |  |  |  |  | 1girl, black_bikini, black_gloves, black_jacket, choker, cleavage, katana, looking_at_viewer, o-ring_bikini, red_thighhighs, shrug_(clothing), single_thighhigh, solo, thigh_strap, cropped_jacket, sheath, holding_sword, navel, o-ring_bottom, thighs |
| 13 | 6 |  |  |  |  |  | 1girl, hair_flower, looking_at_viewer, solo, upper_body, black_kimono, obi, wide_sleeves, closed_mouth, floral_print, fur_collar, fur_trim, holding |
| 14 | 6 |  |  |  |  |  | 1girl, bare_shoulders, black_leotard, cleavage, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, solo, blush, strapless_leotard, covered_navel, detached_collar, fishnet_pantyhose, highleg_leotard, simple_background, white_background, wrist_cuffs, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | chain | headpiece | solo | armored_dress | fur_trim | gauntlets | looking_at_viewer | smile | black_gloves | navel_cutout | thighhighs | cleavage | elbow_gloves | holding_sword | parted_lips | blonde_hair | armored_boots | black_dress | flag | black_thighhighs | cape | standing | fur-trimmed_cape | fur_collar | black_cape | coat | jacket | official_alternate_costume | short_dress | open_clothes | grin | long_sleeves | necklace | thighs | black_footwear | knee_boots | sitting | crossed_legs | fur-trimmed_coat | fur-trimmed_sleeves | blue_coat | open_coat | collarbone | blush | cowboy_shot | open_mouth | simple_background | white_background | hair_flower | strapless_dress | drinking_glass | choker | holding | ribbon | purple_dress | black_bra | black_panties | closed_mouth | navel | lingerie | garter_belt | bra | babydoll | jewelry | pale_skin | underwear_only | cosplay | see-through | black_bikini | blue_sky | day | outdoors | beach | o-ring_bikini | ocean | armpits | arms_behind_head | arms_up | barefoot | braid | kneeling | bracelet | sarong | twin_braids | multiple_braids | black_jacket | katana | shrug_(clothing) | black_choker | o-ring_top | o-ring_bottom | thigh_strap | cropped_jacket | sheathed | red_thighhighs | single_thighhigh | sheath | upper_body | black_kimono | obi | wide_sleeves | floral_print | black_leotard | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | covered_navel | detached_collar | fishnet_pantyhose | highleg_leotard | wrist_cuffs |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------------|:--------|:------------|:-------|:----------------|:-----------|:------------|:--------------------|:--------|:---------------|:---------------|:-------------|:-----------|:---------------|:----------------|:--------------|:--------------|:----------------|:--------------|:-------|:-------------------|:-------|:-----------|:-------------------|:-------------|:-------------|:-------|:---------|:-----------------------------|:--------------|:---------------|:-------|:---------------|:-----------|:---------|:-----------------|:-------------|:----------|:---------------|:-------------------|:----------------------|:------------|:------------|:-------------|:--------|:--------------|:-------------|:--------------------|:-------------------|:--------------|:------------------|:-----------------|:---------|:----------|:---------|:---------------|:------------|:----------------|:---------------|:--------|:-----------|:--------------|:------|:-----------|:----------|:------------|:-----------------|:----------|:--------------|:---------------|:-----------|:------|:-----------|:--------|:----------------|:--------|:----------|:-------------------|:----------|:-----------|:--------|:-----------|:-----------|:---------|:--------------|:------------------|:---------------|:---------|:-------------------|:---------------|:-------------|:----------------|:--------------|:-----------------|:-----------|:-----------------|:-------------------|:---------|:-------------|:---------------|:------|:---------------|:---------------|:----------------|:-------------------|:----------------|:--------------|:--------------------|:----------------|:------------------|:--------------------|:------------------|:--------------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | | X | X | X | X | X | X | X | | | | | | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | | X | X | X | X | | X | X | X | | | | | | X | | | | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | | | | X | | X | | X | | | | | | | X | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | | X | | | | X | X | | | | | | | | | | X | | | | | | | | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | | | X | | | | X | | | | | X | | | | | | X | | | | | | | | | X | X | X | | | X | X | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 13 |  |  |  |  |  | X | X | | | X | | | | X | X | X | | | X | X | | | | | X | | X | | | | | | | | X | | | | | | | | | X | | | | | | X | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | | | | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 13 |  |  |  |  |  | X | X | | | X | | | | X | X | X | | | X | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | X | X | | | X | X | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 8 |  |  |  |  |  | X | X | | | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | X | X | | | | | | | | X | | | | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | | | X | | | | X | X | | | | X | | | | | | | | | | | | | | | | X | | | | | X | X | | | X | | | | | | X | X | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 10 |  |  |  |  |  | X | | | | X | | | | X | X | X | | | X | | X | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | X | | X | | X | X | | | | | | | | | | X | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 12 | 6 |  |  |  |  |  | X | | | | X | | | | X | | X | | | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | X | X | X | | | X | X | X | | X | X | X | | | | | | | | | | | | | | | |
| 13 | 6 |  |  |  |  |  | X | | | | X | | X | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | |
| 14 | 6 |  |  |  |  |  | X | X | | | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/jeanne_d_arc_alter_santa_lily_fgo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T11:09:34+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-12T03:05:23+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of jeanne\_d\_arc\_alter\_santa\_lily/ジャンヌ・ダルク・オルタ・サンタ・リリィ/贞德·Alter·Santa·Lily (Fate/Grand Order)
=========================================================================================================
This is the dataset of jeanne\_d\_arc\_alter\_santa\_lily/ジャンヌ・ダルク・オルタ・サンタ・リリィ/贞德·Alter·Santa·Lily (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are 'yellow\_eyes, breasts, bangs, ahoge, large\_breasts, white\_hair, long\_hair, very\_long\_hair, short\_hair, hair\_between\_eyes, grey\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
e113ce1c7b749dd7d34e2651acbbd45fe1180017
|
# RuBQ 2.0
## Dataset Description
- **Repository:** https://github.com/vladislavneon/RuBQ/tree/master/RuBQ_2.0
- **Paper:** [RuBQ: A Russian Dataset for Question Answering over Wikidata](https://arxiv.org/abs/2005.10659)
For training data see [d0rj/RuBQ_2.0-paragraphs](https://huggingface.co/datasets/d0rj/RuBQ_2.0-paragraphs).
|
d0rj/RuBQ_2.0
|
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"language:en",
"license:cc-by-sa-4.0",
"qa",
"machine reading",
"arxiv:2005.10659",
"region:us"
] |
2023-09-12T11:13:09+00:00
|
{"language": ["ru", "en"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "paperswithcode_id": "rubq", "pretty_name": "RuBQ 2.0", "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "uid", "dtype": "int64"}, {"name": "question_text", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer_text", "dtype": "string"}, {"name": "question_uris", "sequence": "string"}, {"name": "question_props", "sequence": "string"}, {"name": "answers", "list": [{"name": "datatype", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "wd_names", "struct": [{"name": "en", "sequence": "string"}, {"name": "ru", "sequence": "string"}]}, {"name": "wp_names", "sequence": "string"}, {"name": "xml:lang", "dtype": "string"}]}, {"name": "paragraphs_uids", "struct": [{"name": "all_related", "sequence": "int64"}, {"name": "with_answer", "sequence": "int64"}]}, {"name": "tags", "sequence": "string"}, {"name": "RuBQ_version", "dtype": "string"}, {"name": "question_eng", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1992076, "num_examples": 2330}, {"name": "dev", "num_bytes": 488914, "num_examples": 580}], "download_size": 0, "dataset_size": 2480990}, "tags": ["qa", "machine reading"]}
|
2023-09-15T11:15:09+00:00
|
[
"2005.10659"
] |
[
"ru",
"en"
] |
TAGS
#task_categories-question-answering #size_categories-1K<n<10K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us
|
# RuBQ 2.0
## Dataset Description
- Repository: URL
- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata
For training data see d0rj/RuBQ_2.0-paragraphs.
|
[
"# RuBQ 2.0",
"## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata\n\nFor training data see d0rj/RuBQ_2.0-paragraphs."
] |
[
"TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us \n",
"# RuBQ 2.0",
"## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata\n\nFor training data see d0rj/RuBQ_2.0-paragraphs."
] |
[
71,
5,
47
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us \n# RuBQ 2.0## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata\n\nFor training data see d0rj/RuBQ_2.0-paragraphs."
] |
dbe7252739e9c4121fe6c818bb8629f9513de70e
|
# RuBQ 1.0
## Dataset Description
- **Repository:** https://github.com/vladislavneon/RuBQ/tree/master/RuBQ_1.0
- **Paper:** [RuBQ: A Russian Dataset for Question Answering over Wikidata](https://arxiv.org/abs/2005.10659)
|
d0rj/RuBQ_1.0
|
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"language:en",
"license:cc-by-sa-4.0",
"qa",
"machine reading",
"arxiv:2005.10659",
"region:us"
] |
2023-09-12T11:21:56+00:00
|
{"language": ["ru", "en"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "paperswithcode_id": "rubq", "pretty_name": "RuBQ 1.0", "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "dev", "path": "data/dev-*"}]}], "dataset_info": {"features": [{"name": "uid", "dtype": "int64"}, {"name": "question_text", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer_text", "dtype": "string"}, {"name": "question_uris", "sequence": "string"}, {"name": "question_props", "sequence": "string"}, {"name": "answers", "list": [{"name": "datatype", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "xml:lang", "dtype": "string"}]}, {"name": "tags", "sequence": "string"}, {"name": "question_eng", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 472281, "num_examples": 1200}, {"name": "dev", "num_bytes": 115029, "num_examples": 300}], "download_size": 249954, "dataset_size": 587310}, "tags": ["qa", "machine reading"]}
|
2023-09-12T11:31:33+00:00
|
[
"2005.10659"
] |
[
"ru",
"en"
] |
TAGS
#task_categories-question-answering #size_categories-1K<n<10K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us
|
# RuBQ 1.0
## Dataset Description
- Repository: URL
- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata
|
[
"# RuBQ 1.0",
"## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata"
] |
[
"TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us \n",
"# RuBQ 1.0",
"## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata"
] |
[
71,
5,
28
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us \n# RuBQ 1.0## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata"
] |
383cf3ba0395865ada07329c8601bee840cbeb2b
|
# RuBQ_2.0-paragraphs
## Dataset Description
- **Repository:** https://github.com/vladislavneon/RuBQ/tree/master/RuBQ_2.0
- **Paper:** [RuBQ: A Russian Dataset for Question Answering over Wikidata](https://arxiv.org/abs/2005.10659)
For **test** and **dev** data see [d0rj/RuBQ_2.0](https://huggingface.co/datasets/d0rj/RuBQ_2.0)
|
d0rj/RuBQ_2.0-paragraphs
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"language:en",
"license:cc-by-sa-4.0",
"qa",
"machine reading",
"arxiv:2005.10659",
"region:us"
] |
2023-09-12T11:25:40+00:00
|
{"language": ["ru", "en"], "license": "cc-by-sa-4.0", "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "paperswithcode_id": "rubq", "pretty_name": "RuBQ 2.0", "configs": [{"config_name": "default", "data_files": [{"split": "paragraphs", "path": "data/paragraphs-*"}]}], "dataset_info": {"features": [{"name": "uid", "dtype": "int64"}, {"name": "ru_wiki_pageid", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "paragraphs", "num_bytes": 47303369, "num_examples": 56952}], "download_size": 24269133, "dataset_size": 47303369}, "tags": ["qa", "machine reading"]}
|
2023-09-15T11:16:41+00:00
|
[
"2005.10659"
] |
[
"ru",
"en"
] |
TAGS
#task_categories-question-answering #size_categories-10K<n<100K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us
|
# RuBQ_2.0-paragraphs
## Dataset Description
- Repository: URL
- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata
For test and dev data see d0rj/RuBQ_2.0
|
[
"# RuBQ_2.0-paragraphs",
"## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata\n\n\nFor test and dev data see d0rj/RuBQ_2.0"
] |
[
"TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us \n",
"# RuBQ_2.0-paragraphs",
"## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata\n\n\nFor test and dev data see d0rj/RuBQ_2.0"
] |
[
71,
10,
44
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #source_datasets-original #language-Russian #language-English #license-cc-by-sa-4.0 #qa #machine reading #arxiv-2005.10659 #region-us \n# RuBQ_2.0-paragraphs## Dataset Description\n\n- Repository: URL\n- Paper: RuBQ: A Russian Dataset for Question Answering over Wikidata\n\n\nFor test and dev data see d0rj/RuBQ_2.0"
] |
aa73cedb231515d89e1fb137df3074bafd421168
|
# Dataset Card for Evaluation run of _fsx_shared-falcon-180B_converted_safetensors
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/_fsx_shared-falcon-180B_converted_safetensors
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [_fsx_shared-falcon-180B_converted_safetensors](https://huggingface.co/_fsx_shared-falcon-180B_converted_safetensors) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details__fsx_shared-falcon-180B_converted_safetensors",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-12T12:25:36.361219](https://huggingface.co/datasets/open-llm-leaderboard/details__fsx_shared-falcon-180B_converted_safetensors/blob/main/results_2023-09-12T12-25-36.361219.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6616129047646827,
"acc_stderr": 0.032465318041276114,
"acc_norm": 0.6652364807936633,
"acc_norm_stderr": 0.032437975492240805,
"mc1": 0.43451652386780903,
"mc1_stderr": 0.017352738749259564,
"mc2": 0.6219407369869773,
"mc2_stderr": 0.015400116321762768
},
"harness|arc:challenge|25": {
"acc": 0.6791808873720137,
"acc_stderr": 0.013640943091946524,
"acc_norm": 0.7090443686006825,
"acc_norm_stderr": 0.013273077865907588
},
"harness|hellaswag|10": {
"acc": 0.681736705835491,
"acc_stderr": 0.004648503177353969,
"acc_norm": 0.86566421031667,
"acc_norm_stderr": 0.0034031580103095565
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5333333333333333,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.5333333333333333,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7302631578947368,
"acc_stderr": 0.03611780560284898,
"acc_norm": 0.7302631578947368,
"acc_norm_stderr": 0.03611780560284898
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.67,
"acc_stderr": 0.047258156262526094,
"acc_norm": 0.67,
"acc_norm_stderr": 0.047258156262526094
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7169811320754716,
"acc_stderr": 0.027724236492700914,
"acc_norm": 0.7169811320754716,
"acc_norm_stderr": 0.027724236492700914
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.034765901043041336,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.034765901043041336
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.42,
"acc_stderr": 0.04960449637488584,
"acc_norm": 0.42,
"acc_norm_stderr": 0.04960449637488584
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.0355068398916558,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.0355068398916558
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.047840607041056527,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.047840607041056527
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.041633319989322626,
"acc_norm": 0.78,
"acc_norm_stderr": 0.041633319989322626
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6212765957446809,
"acc_stderr": 0.031709956060406545,
"acc_norm": 0.6212765957446809,
"acc_norm_stderr": 0.031709956060406545
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.38596491228070173,
"acc_stderr": 0.045796394220704334,
"acc_norm": 0.38596491228070173,
"acc_norm_stderr": 0.045796394220704334
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6068965517241379,
"acc_stderr": 0.040703290137070705,
"acc_norm": 0.6068965517241379,
"acc_norm_stderr": 0.040703290137070705
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4894179894179894,
"acc_stderr": 0.02574554227604549,
"acc_norm": 0.4894179894179894,
"acc_norm_stderr": 0.02574554227604549
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.373015873015873,
"acc_stderr": 0.04325506042017086,
"acc_norm": 0.373015873015873,
"acc_norm_stderr": 0.04325506042017086
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8096774193548387,
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"acc_norm": 0.8096774193548387,
"acc_norm_stderr": 0.022331707611823078
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5270935960591133,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.5270935960591133,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.047258156262526066,
"acc_norm": 0.67,
"acc_norm_stderr": 0.047258156262526066
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8232323232323232,
"acc_stderr": 0.027178752639044915,
"acc_norm": 0.8232323232323232,
"acc_norm_stderr": 0.027178752639044915
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.927461139896373,
"acc_stderr": 0.01871899852067817,
"acc_norm": 0.927461139896373,
"acc_norm_stderr": 0.01871899852067817
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6461538461538462,
"acc_stderr": 0.024243783994062157,
"acc_norm": 0.6461538461538462,
"acc_norm_stderr": 0.024243783994062157
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_stderr": 0.028493465091028597,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028597
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.726890756302521,
"acc_stderr": 0.028942004040998167,
"acc_norm": 0.726890756302521,
"acc_norm_stderr": 0.028942004040998167
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.39072847682119205,
"acc_stderr": 0.03983798306659808,
"acc_norm": 0.39072847682119205,
"acc_norm_stderr": 0.03983798306659808
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8678899082568807,
"acc_stderr": 0.014517801914598236,
"acc_norm": 0.8678899082568807,
"acc_norm_stderr": 0.014517801914598236
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8284313725490197,
"acc_stderr": 0.026460569561240644,
"acc_norm": 0.8284313725490197,
"acc_norm_stderr": 0.026460569561240644
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944846,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944846
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7488789237668162,
"acc_stderr": 0.029105220833224605,
"acc_norm": 0.7488789237668162,
"acc_norm_stderr": 0.029105220833224605
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8091603053435115,
"acc_stderr": 0.03446513350752598,
"acc_norm": 0.8091603053435115,
"acc_norm_stderr": 0.03446513350752598
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
"acc_stderr": 0.03581796951709282,
"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_stderr": 0.0335195387952127,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.0335195387952127
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5982142857142857,
"acc_stderr": 0.04653333146973646,
"acc_norm": 0.5982142857142857,
"acc_norm_stderr": 0.04653333146973646
},
"harness|hendrycksTest-management|5": {
"acc": 0.8349514563106796,
"acc_stderr": 0.036756688322331886,
"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.036756688322331886
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8454661558109834,
"acc_stderr": 0.012925773495095957,
"acc_norm": 0.8454661558109834,
"acc_norm_stderr": 0.012925773495095957
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7427745664739884,
"acc_stderr": 0.023532925431044297,
"acc_norm": 0.7427745664739884,
"acc_norm_stderr": 0.023532925431044297
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4860335195530726,
"acc_stderr": 0.016715976410744515,
"acc_norm": 0.4860335195530726,
"acc_norm_stderr": 0.016715976410744515
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7450980392156863,
"acc_stderr": 0.024954184324879912,
"acc_norm": 0.7450980392156863,
"acc_norm_stderr": 0.024954184324879912
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.729903536977492,
"acc_stderr": 0.02521804037341062,
"acc_norm": 0.729903536977492,
"acc_norm_stderr": 0.02521804037341062
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7654320987654321,
"acc_stderr": 0.023576881744005716,
"acc_norm": 0.7654320987654321,
"acc_norm_stderr": 0.023576881744005716
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.49645390070921985,
"acc_stderr": 0.02982674915328092,
"acc_norm": 0.49645390070921985,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5097783572359843,
"acc_stderr": 0.012767793787729341,
"acc_norm": 0.5097783572359843,
"acc_norm_stderr": 0.012767793787729341
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.027678468642144703,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.027678468642144703
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6862745098039216,
"acc_stderr": 0.01877168389352818,
"acc_norm": 0.6862745098039216,
"acc_norm_stderr": 0.01877168389352818
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7,
"acc_stderr": 0.04389311454644287,
"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644287
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6612244897959184,
"acc_stderr": 0.030299506562154185,
"acc_norm": 0.6612244897959184,
"acc_norm_stderr": 0.030299506562154185
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.025196929874827075,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.025196929874827075
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8421052631578947,
"acc_stderr": 0.027966785859160875,
"acc_norm": 0.8421052631578947,
"acc_norm_stderr": 0.027966785859160875
},
"harness|truthfulqa:mc|0": {
"mc1": 0.43451652386780903,
"mc1_stderr": 0.017352738749259564,
"mc2": 0.6219407369869773,
"mc2_stderr": 0.015400116321762768
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details__fsx_shared-falcon-180B_converted_safetensors
|
[
"region:us"
] |
2023-09-12T11:25:47+00:00
|
{"pretty_name": "Evaluation run of _fsx_shared-falcon-180B_converted_safetensors", "dataset_summary": "Dataset automatically created during the evaluation run of model [_fsx_shared-falcon-180B_converted_safetensors](https://huggingface.co/_fsx_shared-falcon-180B_converted_safetensors) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details__fsx_shared-falcon-180B_converted_safetensors\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-12T12:25:36.361219](https://huggingface.co/datasets/open-llm-leaderboard/details__fsx_shared-falcon-180B_converted_safetensors/blob/main/results_2023-09-12T12-25-36.361219.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6616129047646827,\n \"acc_stderr\": 0.032465318041276114,\n \"acc_norm\": 0.6652364807936633,\n \"acc_norm_stderr\": 0.032437975492240805,\n \"mc1\": 0.43451652386780903,\n \"mc1_stderr\": 0.017352738749259564,\n \"mc2\": 0.6219407369869773,\n \"mc2_stderr\": 0.015400116321762768\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6791808873720137,\n \"acc_stderr\": 0.013640943091946524,\n \"acc_norm\": 0.7090443686006825,\n \"acc_norm_stderr\": 0.013273077865907588\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.681736705835491,\n \"acc_stderr\": 0.004648503177353969,\n \"acc_norm\": 0.86566421031667,\n \"acc_norm_stderr\": 0.0034031580103095565\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700914,\n \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700914\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.034765901043041336,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.034765901043041336\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322626\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6212765957446809,\n \"acc_stderr\": 0.031709956060406545,\n \"acc_norm\": 0.6212765957446809,\n \"acc_norm_stderr\": 0.031709956060406545\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n \"acc_stderr\": 0.045796394220704334,\n \"acc_norm\": 0.38596491228070173,\n \"acc_norm_stderr\": 0.045796394220704334\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6068965517241379,\n \"acc_stderr\": 0.040703290137070705,\n \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.040703290137070705\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4894179894179894,\n \"acc_stderr\": 0.02574554227604549,\n \"acc_norm\": 0.4894179894179894,\n \"acc_norm_stderr\": 0.02574554227604549\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8096774193548387,\n \"acc_stderr\": 0.022331707611823078,\n \"acc_norm\": 0.8096774193548387,\n \"acc_norm_stderr\": 0.022331707611823078\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8232323232323232,\n \"acc_stderr\": 0.027178752639044915,\n \"acc_norm\": 0.8232323232323232,\n \"acc_norm_stderr\": 0.027178752639044915\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.01871899852067817,\n \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.01871899852067817\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.024243783994062157,\n \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.024243783994062157\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.726890756302521,\n \"acc_stderr\": 0.028942004040998167,\n \"acc_norm\": 0.726890756302521,\n \"acc_norm_stderr\": 0.028942004040998167\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.39072847682119205,\n \"acc_stderr\": 0.03983798306659808,\n \"acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.03983798306659808\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8678899082568807,\n \"acc_stderr\": 0.014517801914598236,\n \"acc_norm\": 0.8678899082568807,\n \"acc_norm_stderr\": 0.014517801914598236\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944846,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944846\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7488789237668162,\n \"acc_stderr\": 0.029105220833224605,\n \"acc_norm\": 0.7488789237668162,\n \"acc_norm_stderr\": 0.029105220833224605\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5982142857142857,\n \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.5982142857142857,\n \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8454661558109834,\n \"acc_stderr\": 0.012925773495095957,\n \"acc_norm\": 0.8454661558109834,\n \"acc_norm_stderr\": 0.012925773495095957\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044297,\n \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044297\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4860335195530726,\n \"acc_stderr\": 0.016715976410744515,\n \"acc_norm\": 0.4860335195530726,\n \"acc_norm_stderr\": 0.016715976410744515\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.024954184324879912,\n \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.024954184324879912\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7654320987654321,\n \"acc_stderr\": 0.023576881744005716,\n \"acc_norm\": 0.7654320987654321,\n \"acc_norm_stderr\": 0.023576881744005716\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5097783572359843,\n \"acc_stderr\": 0.012767793787729341,\n \"acc_norm\": 0.5097783572359843,\n \"acc_norm_stderr\": 0.012767793787729341\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.027678468642144703,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.027678468642144703\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.01877168389352818,\n \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.01877168389352818\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6612244897959184,\n \"acc_stderr\": 0.030299506562154185,\n \"acc_norm\": 0.6612244897959184,\n \"acc_norm_stderr\": 0.030299506562154185\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n \"acc_norm_stderr\": 0.025196929874827075\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160875,\n \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160875\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.43451652386780903,\n \"mc1_stderr\": 0.017352738749259564,\n \"mc2\": 0.6219407369869773,\n \"mc2_stderr\": 0.015400116321762768\n }\n}\n```", "repo_url": "https://huggingface.co/_fsx_shared-falcon-180B_converted_safetensors", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T12-25-36.361219.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T12-25-36.361219.parquet", 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["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T12-25-36.361219.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_12T12_25_36.361219", "path": ["results_2023-09-12T12-25-36.361219.parquet"]}, {"split": "latest", "path": ["results_2023-09-12T12-25-36.361219.parquet"]}]}]}
|
2023-09-12T11:26:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of _fsx_shared-falcon-180B_converted_safetensors
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model _fsx_shared-falcon-180B_converted_safetensors on the Open LLM Leaderboard.
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-09-12T12:25:36.361219(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of _fsx_shared-falcon-180B_converted_safetensors",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model _fsx_shared-falcon-180B_converted_safetensors on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-12T12:25:36.361219(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model _fsx_shared-falcon-180B_converted_safetensors on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-12T12:25:36.361219(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
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"#### Who are the source language producers?",
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"#### Who are the annotators?",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
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] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of _fsx_shared-falcon-180B_converted_safetensors## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model _fsx_shared-falcon-180B_converted_safetensors on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-12T12:25:36.361219(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
ff93093917ab8bbbd8a7859f42936c231ef05c74
|
# Dataset Card for "test-llama2-nuv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hans12Wurst123/test-llama2-nuv
|
[
"region:us"
] |
2023-09-12T11:26:49+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 60718, "num_examples": 331}], "download_size": 10794, "dataset_size": 60718}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-12T11:59:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test-llama2-nuv"
More Information needed
|
[
"# Dataset Card for \"test-llama2-nuv\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test-llama2-nuv\"\n\nMore Information needed"
] |
[
6,
17
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"test-llama2-nuv\"\n\nMore Information needed"
] |
f64f0492179af274b83e18ba62dd8482ce4079c1
|
# Dataset Card for "some_chives"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TristanPermentier/some_chives
|
[
"region:us"
] |
2023-09-12T11:28:18+00:00
|
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 21643481.0, "num_examples": 29}], "download_size": 0, "dataset_size": 21643481.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-15T08:07:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "some_chives"
More Information needed
|
[
"# Dataset Card for \"some_chives\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"some_chives\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"some_chives\"\n\nMore Information needed"
] |
751655c5f3bf89deddb40eea14c813160277f916
|
# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-step-50K-105b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [PY007/TinyLlama-1.1B-step-50K-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PY007__TinyLlama-1.1B-step-50K-105b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T19:38:12.477684](https://huggingface.co/datasets/open-llm-leaderboard/details_PY007__TinyLlama-1.1B-step-50K-105b/blob/main/results_2023-10-24T19-38-12.477684.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857113,
"f1": 0.039109689597315506,
"f1_stderr": 0.001116278403063508,
"acc": 0.27455565641618196,
"acc_stderr": 0.0079998796659362
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857113,
"f1": 0.039109689597315506,
"f1_stderr": 0.001116278403063508
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.0020013057209480774
},
"harness|winogrande|5": {
"acc": 0.5438042620363063,
"acc_stderr": 0.013998453610924324
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_PY007__TinyLlama-1.1B-step-50K-105b
|
[
"region:us"
] |
2023-09-12T11:30:17+00:00
|
{"pretty_name": "Evaluation run of PY007/TinyLlama-1.1B-step-50K-105b", "dataset_summary": "Dataset automatically created during the evaluation run of model [PY007/TinyLlama-1.1B-step-50K-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PY007__TinyLlama-1.1B-step-50K-105b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T19:38:12.477684](https://huggingface.co/datasets/open-llm-leaderboard/details_PY007__TinyLlama-1.1B-step-50K-105b/blob/main/results_2023-10-24T19-38-12.477684.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857113,\n \"f1\": 0.039109689597315506,\n \"f1_stderr\": 0.001116278403063508,\n \"acc\": 0.27455565641618196,\n \"acc_stderr\": 0.0079998796659362\n },\n \"harness|drop|3\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857113,\n \"f1\": 0.039109689597315506,\n \"f1_stderr\": 0.001116278403063508\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \"acc_stderr\": 0.0020013057209480774\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5438042620363063,\n \"acc_stderr\": 0.013998453610924324\n }\n}\n```", "repo_url": "https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T12_30_04.204611", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-30-04.204611.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-30-04.204611.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T19_38_12.477684", "path": ["**/details_harness|drop|3_2023-10-24T19-38-12.477684.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T19-38-12.477684.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T19_38_12.477684", "path": ["**/details_harness|gsm8k|5_2023-10-24T19-38-12.477684.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T19-38-12.477684.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T12_30_04.204611", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-30-04.204611.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-09-12T12-30-04.204611.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T12-30-04.204611.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T19_38_12.477684", "path": ["**/details_harness|winogrande|5_2023-10-24T19-38-12.477684.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-24T19-38-12.477684.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_12T12_30_04.204611", "path": ["results_2023-09-12T12-30-04.204611.parquet"]}, {"split": "2023_10_24T19_38_12.477684", "path": ["results_2023-10-24T19-38-12.477684.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T19-38-12.477684.parquet"]}]}]}
|
2023-10-24T18:38:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-step-50K-105b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-step-50K-105b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T19:38:12.477684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-step-50K-105b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-step-50K-105b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T19:38:12.477684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-step-50K-105b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-step-50K-105b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T19:38:12.477684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
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"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
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"### Annotations",
"#### Annotation process",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PY007/TinyLlama-1.1B-step-50K-105b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PY007/TinyLlama-1.1B-step-50K-105b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T19:38:12.477684(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
59d6ba61003cd9bdda173160b525f8b03e3de350
|
# Dataset Card for "art_defect_inpainting"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
johanneskpp/art_defect_inpainting
|
[
"region:us"
] |
2023-09-12T11:30:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 256960027.414, "num_examples": 2002}, {"name": "validation", "num_bytes": 72498827.0, "num_examples": 570}, {"name": "test", "num_bytes": 36507597.0, "num_examples": 285}], "download_size": 365119883, "dataset_size": 365966451.41400003}}
|
2023-09-12T21:34:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "art_defect_inpainting"
More Information needed
|
[
"# Dataset Card for \"art_defect_inpainting\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"art_defect_inpainting\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"art_defect_inpainting\"\n\nMore Information needed"
] |
856167589d9501254578f385c48ea85e03f0cd10
|
# Dataset of tsutsuji (Pokémon)
This is the dataset of tsutsuji (Pokémon), containing 225 images and their tags.
The core tags of this character are `long_hair, brown_hair, twintails, red_eyes, breasts, ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 225 | 186.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsutsuji_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 225 | 121.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsutsuji_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 482 | 231.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsutsuji_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 225 | 172.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsutsuji_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 482 | 303.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsutsuji_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tsutsuji_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, collared_shirt, grey_dress, pink_pantyhose, short_sleeves, solo, white_shirt, full_body, looking_at_viewer, open_mouth, pink_ascot, simple_background, blush, eyelashes, hand_up, mary_janes, standing, white_background, black_footwear, hair_rings, hand_on_hip, index_finger_raised, necktie |
| 1 | 17 |  |  |  |  |  | 1girl, pantyhose, grey_dress, ascot, pokemon_(creature), short_sleeves, hair_pulled_back, looking_at_viewer, mary_janes, smile, blush, white_shirt, closed_mouth, collared_shirt, full_body, hand_on_hip, open_mouth |
| 2 | 5 |  |  |  |  |  | 1girl, blush, hair_pulled_back, pink_pantyhose, solo, ascot, grey_dress, open_mouth, necktie, looking_at_viewer, panties_under_pantyhose |
| 3 | 16 |  |  |  |  |  | elbow_gloves, 1girl, looking_at_viewer, witch_hat, earrings, black_dress, black_gloves, black_headwear, smile, blush, solo, halloween, eyelashes, pokemon_(creature), pantyhose |
| 4 | 15 |  |  |  |  |  | 1girl, blush, hetero, 1boy, nipples, solo_focus, penis, sex, nude, open_mouth, vaginal, hair_pulled_back, pink_eyes, pussy, spread_legs, sweat, censored, lying, navel, medium_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | collared_shirt | grey_dress | pink_pantyhose | short_sleeves | solo | white_shirt | full_body | looking_at_viewer | open_mouth | pink_ascot | simple_background | blush | eyelashes | hand_up | mary_janes | standing | white_background | black_footwear | hair_rings | hand_on_hip | index_finger_raised | necktie | pantyhose | ascot | pokemon_(creature) | hair_pulled_back | smile | closed_mouth | panties_under_pantyhose | elbow_gloves | witch_hat | earrings | black_dress | black_gloves | black_headwear | halloween | hetero | 1boy | nipples | solo_focus | penis | sex | nude | vaginal | pink_eyes | pussy | spread_legs | sweat | censored | lying | navel | medium_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------------|:-----------------|:----------------|:-------|:--------------|:------------|:--------------------|:-------------|:-------------|:--------------------|:--------|:------------|:----------|:-------------|:-----------|:-------------------|:-----------------|:-------------|:--------------|:----------------------|:----------|:------------|:--------|:---------------------|:-------------------|:--------|:---------------|:--------------------------|:---------------|:------------|:-----------|:--------------|:---------------|:-----------------|:------------|:---------|:-------|:----------|:-------------|:--------|:------|:-------|:----------|:------------|:--------|:--------------|:--------|:-----------|:--------|:--------|:-----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 17 |  |  |  |  |  | X | X | X | | X | | X | X | X | X | | | X | | | X | | | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | | X | X | | X | | | X | X | | | X | | | | | | | | | | X | | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | | | | | X | | | X | | | | X | X | | | | | | | | | | X | | X | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 4 | 15 |  |  |  |  |  | X | | | | | | | | | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/tsutsuji_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T11:43:26+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T21:09:33+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of tsutsuji (Pokémon)
=============================
This is the dataset of tsutsuji (Pokémon), containing 225 images and their tags.
The core tags of this character are 'long\_hair, brown\_hair, twintails, red\_eyes, breasts, ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
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[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
47e1f65e5ad90908df67959a051f4bbc102ed6fc
|
Please refer to https://github.com/zhuang-li/FACTUAL for a detailed description of this dataset.
|
lizhuang144/FACTUAL_Scene_Graph_ID
|
[
"region:us"
] |
2023-09-12T11:49:01+00:00
|
{}
|
2023-11-11T09:53:43+00:00
|
[] |
[] |
TAGS
#region-us
|
Please refer to URL for a detailed description of this dataset.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
a7062fe1d190864d5ef9a186849237418fe4816a
|
# Dataset Card for Evaluation run of Yehoon/yehoon_llama2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Yehoon/yehoon_llama2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Yehoon/yehoon_llama2](https://huggingface.co/Yehoon/yehoon_llama2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Yehoon__yehoon_llama2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T20:19:53.869610](https://huggingface.co/datasets/open-llm-leaderboard/details_Yehoon__yehoon_llama2/blob/main/results_2023-10-24T20-19-53.869610.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.008598993288590604,
"em_stderr": 0.0009455579144542034,
"f1": 0.0916033976510068,
"f1_stderr": 0.0018917747787763773,
"acc": 0.4101086482368971,
"acc_stderr": 0.009683376605280791
},
"harness|drop|3": {
"em": 0.008598993288590604,
"em_stderr": 0.0009455579144542034,
"f1": 0.0916033976510068,
"f1_stderr": 0.0018917747787763773
},
"harness|gsm8k|5": {
"acc": 0.07278241091736164,
"acc_stderr": 0.007155604761167479
},
"harness|winogrande|5": {
"acc": 0.7474348855564326,
"acc_stderr": 0.012211148449394105
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Yehoon__yehoon_llama2
|
[
"region:us"
] |
2023-09-12T11:52:28+00:00
|
{"pretty_name": "Evaluation run of Yehoon/yehoon_llama2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Yehoon/yehoon_llama2](https://huggingface.co/Yehoon/yehoon_llama2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Yehoon__yehoon_llama2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T20:19:53.869610](https://huggingface.co/datasets/open-llm-leaderboard/details_Yehoon__yehoon_llama2/blob/main/results_2023-10-24T20-19-53.869610.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.008598993288590604,\n \"em_stderr\": 0.0009455579144542034,\n \"f1\": 0.0916033976510068,\n \"f1_stderr\": 0.0018917747787763773,\n \"acc\": 0.4101086482368971,\n \"acc_stderr\": 0.009683376605280791\n },\n \"harness|drop|3\": {\n \"em\": 0.008598993288590604,\n \"em_stderr\": 0.0009455579144542034,\n \"f1\": 0.0916033976510068,\n \"f1_stderr\": 0.0018917747787763773\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07278241091736164,\n \"acc_stderr\": 0.007155604761167479\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7474348855564326,\n \"acc_stderr\": 0.012211148449394105\n }\n}\n```", "repo_url": "https://huggingface.co/Yehoon/yehoon_llama2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T12_52_12.986563", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-52-12.986563.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-52-12.986563.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T20_19_53.869610", "path": ["**/details_harness|drop|3_2023-10-24T20-19-53.869610.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T20-19-53.869610.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T20_19_53.869610", "path": ["**/details_harness|gsm8k|5_2023-10-24T20-19-53.869610.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T20-19-53.869610.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T12_52_12.986563", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-52-12.986563.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-52-12.986563.parquet"]}]}, 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|
2023-10-24T19:20:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Yehoon/yehoon_llama2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Yehoon/yehoon_llama2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T20:19:53.869610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Yehoon/yehoon_llama2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Yehoon/yehoon_llama2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T20:19:53.869610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of Yehoon/yehoon_llama2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Yehoon/yehoon_llama2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T20:19:53.869610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Yehoon/yehoon_llama2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Yehoon/yehoon_llama2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T20:19:53.869610(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
4ac3fc3781453fc4046fc1ee433dabb2bbaaf859
|
# Dataset Card for Evaluation run of matsuo-lab/weblab-10b-instruction-sft
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [matsuo-lab/weblab-10b-instruction-sft](https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_matsuo-lab__weblab-10b-instruction-sft",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T03:21:34.217143](https://huggingface.co/datasets/open-llm-leaderboard/details_matsuo-lab__weblab-10b-instruction-sft/blob/main/results_2023-10-26T03-21-34.217143.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.25796979865771813,
"em_stderr": 0.004480586588383818,
"f1": 0.29560088087248326,
"f1_stderr": 0.0044784123554922695,
"acc": 0.32953979031494646,
"acc_stderr": 0.008582363548097051
},
"harness|drop|3": {
"em": 0.25796979865771813,
"em_stderr": 0.004480586588383818,
"f1": 0.29560088087248326,
"f1_stderr": 0.0044784123554922695
},
"harness|gsm8k|5": {
"acc": 0.01819560272934041,
"acc_stderr": 0.0036816118940738705
},
"harness|winogrande|5": {
"acc": 0.6408839779005525,
"acc_stderr": 0.013483115202120232
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_matsuo-lab__weblab-10b-instruction-sft
|
[
"region:us"
] |
2023-09-12T11:58:44+00:00
|
{"pretty_name": "Evaluation run of matsuo-lab/weblab-10b-instruction-sft", "dataset_summary": "Dataset automatically created during the evaluation run of model [matsuo-lab/weblab-10b-instruction-sft](https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_matsuo-lab__weblab-10b-instruction-sft\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-26T03:21:34.217143](https://huggingface.co/datasets/open-llm-leaderboard/details_matsuo-lab__weblab-10b-instruction-sft/blob/main/results_2023-10-26T03-21-34.217143.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.25796979865771813,\n \"em_stderr\": 0.004480586588383818,\n \"f1\": 0.29560088087248326,\n \"f1_stderr\": 0.0044784123554922695,\n \"acc\": 0.32953979031494646,\n \"acc_stderr\": 0.008582363548097051\n },\n \"harness|drop|3\": {\n \"em\": 0.25796979865771813,\n \"em_stderr\": 0.004480586588383818,\n \"f1\": 0.29560088087248326,\n \"f1_stderr\": 0.0044784123554922695\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01819560272934041,\n \"acc_stderr\": 0.0036816118940738705\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6408839779005525,\n \"acc_stderr\": 0.013483115202120232\n }\n}\n```", "repo_url": "https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T12_58_31.709829", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-58-31.709829.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T12-58-31.709829.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_26T03_21_34.217143", "path": ["**/details_harness|drop|3_2023-10-26T03-21-34.217143.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-26T03-21-34.217143.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_26T03_21_34.217143", "path": ["**/details_harness|gsm8k|5_2023-10-26T03-21-34.217143.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-26T03-21-34.217143.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T12_58_31.709829", "path": ["**/details_harness|hellaswag|10_2023-09-12T12-58-31.709829.parquet"]}, {"split": "latest", "path": 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|
2023-10-26T02:21:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of matsuo-lab/weblab-10b-instruction-sft
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model matsuo-lab/weblab-10b-instruction-sft on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-26T03:21:34.217143(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"# Dataset Card for Evaluation run of matsuo-lab/weblab-10b-instruction-sft",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model matsuo-lab/weblab-10b-instruction-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T03:21:34.217143(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of matsuo-lab/weblab-10b-instruction-sft",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model matsuo-lab/weblab-10b-instruction-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T03:21:34.217143(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of matsuo-lab/weblab-10b-instruction-sft## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model matsuo-lab/weblab-10b-instruction-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-26T03:21:34.217143(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
0ade7e85f8de6b87f54cae4689f3948eff071ba3
|
# Dataset Card for PAWS-ca: Paraphrase Adversaries from Word Scrambling in Catalan
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [PAWS-ca](https://zenodo.org/record/)
- **Point of Contact:** [email protected]
### Dataset Summary
The PAWS-ca dataset (Paraphrase Adversaries from Word Scrambling in Catalan) is a translation of the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit.
The dataset contains 4,000 human translated PAWS pairs and 49,000 machine translated pairs.
### Supported Tasks and Leaderboards
Paraphrase Identification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three JSON files, one for each split.
### Example:
<pre>
{
"id": 38
"sentence1": Holly estava influenciat musicalment per Elton John.
"sentence2": Holly Holly va ser influenciada musicalment per Elton John.
"label": 1
}
{
"id":
"sentence1": L’equip va respondre als canvis en el següent partit el mateix vespre del 19 de febrer.
"sentence2": L'equip va respondre als canvis en el mateix partit d’aquell següent 19 de febrer al vespre.
"label": 0
}
</pre>
### Data Fields
- id: An ID that matches the ID of the source pair of the English PAWS dataset
- sentence1: The first sentence
- sentence2: The second sentence
- label: Label for each pair
### Data Splits
* paws-ca.train.jsonl: 49,401 examples
* paws-ca.val.jsonl: 2,000 examples
* paws-ca.test.jsonl: 2,000 examples
> **Caveat**: please note that the dev and test sets of PAWS-X are both sourced
> from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may
> appear in both the dev and test sets. Nevertheless our data split guarantees
> that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`)
> between dev and test.
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
PAWS (Paraphrase Adversaries from Word Scrambling)
#### Initial Data Collection and Normalization
This dataset is a translation the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.
#### Who are the source language producers?
For more information on how PAWS was created, refer to the paper (), or visit the [PAWS's webpage]().
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
This is a translation of the English PAWS dataset and its annotations.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
Original PAWS-X License:
The dataset may be freely used for any purpose, with acknowledgment of Google LLC as the data source being appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
PAWS-ca:
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
### Contributions
[N/A]
|
projecte-aina/PAWS-ca
|
[
"task_categories:text-classification",
"annotations_creators:professional translators",
"annotations_creators:machine-generated",
"multilinguality:monolingual",
"language:ca",
"license:other",
"paraphrase-identification",
"region:us"
] |
2023-09-12T12:23:01+00:00
|
{"annotations_creators": ["professional translators", "machine-generated"], "language": ["ca"], "license": ["other"], "multilinguality": ["monolingual"], "task_categories": ["text-classification"], "pretty_name": "paws-ca", "tags": ["paraphrase-identification"]}
|
2023-11-27T09:24:40+00:00
|
[] |
[
"ca"
] |
TAGS
#task_categories-text-classification #annotations_creators-professional translators #annotations_creators-machine-generated #multilinguality-monolingual #language-Catalan #license-other #paraphrase-identification #region-us
|
# Dataset Card for PAWS-ca: Paraphrase Adversaries from Word Scrambling in Catalan
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage: PAWS-ca
- Point of Contact: langtech@URL
### Dataset Summary
The PAWS-ca dataset (Paraphrase Adversaries from Word Scrambling in Catalan) is a translation of the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit.
The dataset contains 4,000 human translated PAWS pairs and 49,000 machine translated pairs.
### Supported Tasks and Leaderboards
Paraphrase Identification, Language Model
### Languages
The dataset is in Catalan ('ca-ES').
## Dataset Structure
### Data Instances
Three JSON files, one for each split.
### Example:
<pre>
{
"id": 38
"sentence1": Holly estava influenciat musicalment per Elton John.
"sentence2": Holly Holly va ser influenciada musicalment per Elton John.
"label": 1
}
{
"id":
"sentence1": L’equip va respondre als canvis en el següent partit el mateix vespre del 19 de febrer.
"sentence2": L'equip va respondre als canvis en el mateix partit d’aquell següent 19 de febrer al vespre.
"label": 0
}
</pre>
### Data Fields
- id: An ID that matches the ID of the source pair of the English PAWS dataset
- sentence1: The first sentence
- sentence2: The second sentence
- label: Label for each pair
### Data Splits
* URL: 49,401 examples
* URL: 2,000 examples
* URL: 2,000 examples
> Caveat: please note that the dev and test sets of PAWS-X are both sourced
> from the dev set of PAWS-Wiki. As a consequence, the same 'sentence 1' may
> appear in both the dev and test sets. Nevertheless our data split guarantees
> that there is no overlap on sentence pairs ('sentence 1' + 'sentence 2')
> between dev and test.
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
PAWS (Paraphrase Adversaries from Word Scrambling)
#### Initial Data Collection and Normalization
This dataset is a translation the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.
#### Who are the source language producers?
For more information on how PAWS was created, refer to the paper (), or visit the [PAWS's webpage]().
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
This is a translation of the English PAWS dataset and its annotations.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)
This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.
### Licensing Information
Original PAWS-X License:
The dataset may be freely used for any purpose, with acknowledgment of Google LLC as the data source being appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
PAWS-ca:
Creative Commons Attribution 4.0 International.
### Contributions
[N/A]
|
[
"# Dataset Card for PAWS-ca: Paraphrase Adversaries from Word Scrambling in Catalan",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: PAWS-ca\n- Point of Contact: langtech@URL",
"### Dataset Summary\n\nThe PAWS-ca dataset (Paraphrase Adversaries from Word Scrambling in Catalan) is a translation of the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit.\n\nThe dataset contains 4,000 human translated PAWS pairs and 49,000 machine translated pairs.",
"### Supported Tasks and Leaderboards\n\nParaphrase Identification, Language Model",
"### Languages\n\nThe dataset is in Catalan ('ca-ES').",
"## Dataset Structure",
"### Data Instances\n\nThree JSON files, one for each split.",
"### Example:\n<pre>\n \n {\n \"id\": 38\n \"sentence1\": Holly estava influenciat musicalment per Elton John.\n \"sentence2\": Holly Holly va ser influenciada musicalment per Elton John.\n \"label\": 1\n }\n \n {\n \"id\":\n \"sentence1\": L’equip va respondre als canvis en el següent partit el mateix vespre del 19 de febrer.\n \"sentence2\": L'equip va respondre als canvis en el mateix partit d’aquell següent 19 de febrer al vespre.\n \"label\": 0\n }\n \n</pre>",
"### Data Fields\n\n- id: An ID that matches the ID of the source pair of the English PAWS dataset\n- sentence1: The first sentence\n- sentence2: The second sentence\n- label: Label for each pair",
"### Data Splits\n\n* URL: 49,401 examples\n* URL: 2,000 examples\n* URL: 2,000 examples\n\n\n> Caveat: please note that the dev and test sets of PAWS-X are both sourced\n> from the dev set of PAWS-Wiki. As a consequence, the same 'sentence 1' may\n> appear in both the dev and test sets. Nevertheless our data split guarantees\n> that there is no overlap on sentence pairs ('sentence 1' + 'sentence 2')\n> between dev and test.",
"## Dataset Creation",
"### Curation Rationale\n\nWe created this dataset to contribute to the development of language models in Catalan, a low-resource language.",
"### Source Data\n\nPAWS (Paraphrase Adversaries from Word Scrambling)",
"#### Initial Data Collection and Normalization\n\nThis dataset is a translation the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.",
"#### Who are the source language producers?\n\nFor more information on how PAWS was created, refer to the paper (), or visit the [PAWS's webpage]().",
"### Annotations",
"#### Annotation process\n\n[N/A]",
"#### Who are the annotators?\n\nThis is a translation of the English PAWS dataset and its annotations.",
"### Personal and Sensitive Information\n\nNo personal or sensitive information included.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe hope this dataset contributes to the development of language models in Catalan, a low-resource language.",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nLanguage Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.",
"### Licensing Information\n\nOriginal PAWS-X License:\n\nThe dataset may be freely used for any purpose, with acknowledgment of Google LLC as the data source being appreciated. The dataset is provided \"AS IS\" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.\n\nPAWS-ca: \n\nCreative Commons Attribution 4.0 International.",
"### Contributions\n\n[N/A]"
] |
[
"TAGS\n#task_categories-text-classification #annotations_creators-professional translators #annotations_creators-machine-generated #multilinguality-monolingual #language-Catalan #license-other #paraphrase-identification #region-us \n",
"# Dataset Card for PAWS-ca: Paraphrase Adversaries from Word Scrambling in Catalan",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: PAWS-ca\n- Point of Contact: langtech@URL",
"### Dataset Summary\n\nThe PAWS-ca dataset (Paraphrase Adversaries from Word Scrambling in Catalan) is a translation of the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit.\n\nThe dataset contains 4,000 human translated PAWS pairs and 49,000 machine translated pairs.",
"### Supported Tasks and Leaderboards\n\nParaphrase Identification, Language Model",
"### Languages\n\nThe dataset is in Catalan ('ca-ES').",
"## Dataset Structure",
"### Data Instances\n\nThree JSON files, one for each split.",
"### Example:\n<pre>\n \n {\n \"id\": 38\n \"sentence1\": Holly estava influenciat musicalment per Elton John.\n \"sentence2\": Holly Holly va ser influenciada musicalment per Elton John.\n \"label\": 1\n }\n \n {\n \"id\":\n \"sentence1\": L’equip va respondre als canvis en el següent partit el mateix vespre del 19 de febrer.\n \"sentence2\": L'equip va respondre als canvis en el mateix partit d’aquell següent 19 de febrer al vespre.\n \"label\": 0\n }\n \n</pre>",
"### Data Fields\n\n- id: An ID that matches the ID of the source pair of the English PAWS dataset\n- sentence1: The first sentence\n- sentence2: The second sentence\n- label: Label for each pair",
"### Data Splits\n\n* URL: 49,401 examples\n* URL: 2,000 examples\n* URL: 2,000 examples\n\n\n> Caveat: please note that the dev and test sets of PAWS-X are both sourced\n> from the dev set of PAWS-Wiki. As a consequence, the same 'sentence 1' may\n> appear in both the dev and test sets. Nevertheless our data split guarantees\n> that there is no overlap on sentence pairs ('sentence 1' + 'sentence 2')\n> between dev and test.",
"## Dataset Creation",
"### Curation Rationale\n\nWe created this dataset to contribute to the development of language models in Catalan, a low-resource language.",
"### Source Data\n\nPAWS (Paraphrase Adversaries from Word Scrambling)",
"#### Initial Data Collection and Normalization\n\nThis dataset is a translation the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.",
"#### Who are the source language producers?\n\nFor more information on how PAWS was created, refer to the paper (), or visit the [PAWS's webpage]().",
"### Annotations",
"#### Annotation process\n\n[N/A]",
"#### Who are the annotators?\n\nThis is a translation of the English PAWS dataset and its annotations.",
"### Personal and Sensitive Information\n\nNo personal or sensitive information included.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe hope this dataset contributes to the development of language models in Catalan, a low-resource language.",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nLanguage Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.",
"### Licensing Information\n\nOriginal PAWS-X License:\n\nThe dataset may be freely used for any purpose, with acknowledgment of Google LLC as the data source being appreciated. The dataset is provided \"AS IS\" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.\n\nPAWS-ca: \n\nCreative Commons Attribution 4.0 International.",
"### Contributions\n\n[N/A]"
] |
[
68,
23,
120,
20,
75,
18,
17,
6,
16,
132,
46,
122,
5,
30,
19,
39,
39,
5,
10,
26,
15,
8,
30,
13,
12,
5,
61,
98,
10
] |
[
"passage: TAGS\n#task_categories-text-classification #annotations_creators-professional translators #annotations_creators-machine-generated #multilinguality-monolingual #language-Catalan #license-other #paraphrase-identification #region-us \n# Dataset Card for PAWS-ca: Paraphrase Adversaries from Word Scrambling in Catalan## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: PAWS-ca\n- Point of Contact: langtech@URL### Dataset Summary\n\nThe PAWS-ca dataset (Paraphrase Adversaries from Word Scrambling in Catalan) is a translation of the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit.\n\nThe dataset contains 4,000 human translated PAWS pairs and 49,000 machine translated pairs.### Supported Tasks and Leaderboards\n\nParaphrase Identification, Language Model### Languages\n\nThe dataset is in Catalan ('ca-ES').## Dataset Structure### Data Instances\n\nThree JSON files, one for each split.### Example:\n<pre>\n \n {\n \"id\": 38\n \"sentence1\": Holly estava influenciat musicalment per Elton John.\n \"sentence2\": Holly Holly va ser influenciada musicalment per Elton John.\n \"label\": 1\n }\n \n {\n \"id\":\n \"sentence1\": L’equip va respondre als canvis en el següent partit el mateix vespre del 19 de febrer.\n \"sentence2\": L'equip va respondre als canvis en el mateix partit d’aquell següent 19 de febrer al vespre.\n \"label\": 0\n }\n \n</pre>",
"passage: ### Data Fields\n\n- id: An ID that matches the ID of the source pair of the English PAWS dataset\n- sentence1: The first sentence\n- sentence2: The second sentence\n- label: Label for each pair### Data Splits\n\n* URL: 49,401 examples\n* URL: 2,000 examples\n* URL: 2,000 examples\n\n\n> Caveat: please note that the dev and test sets of PAWS-X are both sourced\n> from the dev set of PAWS-Wiki. As a consequence, the same 'sentence 1' may\n> appear in both the dev and test sets. Nevertheless our data split guarantees\n> that there is no overlap on sentence pairs ('sentence 1' + 'sentence 2')\n> between dev and test.## Dataset Creation### Curation Rationale\n\nWe created this dataset to contribute to the development of language models in Catalan, a low-resource language.### Source Data\n\nPAWS (Paraphrase Adversaries from Word Scrambling)#### Initial Data Collection and Normalization\n\nThis dataset is a translation the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.#### Who are the source language producers?\n\nFor more information on how PAWS was created, refer to the paper (), or visit the [PAWS's webpage]().### Annotations#### Annotation process\n\n[N/A]#### Who are the annotators?\n\nThis is a translation of the English PAWS dataset and its annotations.### Personal and Sensitive Information\n\nNo personal or sensitive information included.## Considerations for Using the Data### Social Impact of Dataset\n\nWe hope this dataset contributes to the development of language models in Catalan, a low-resource language.### Discussion of Biases\n\n[N/A]### Other Known Limitations\n\n[N/A]## Additional Information### Dataset Curators\n\nLanguage Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA."
] |
2e01b019dddccc1917e66db7a8aa1a514ef5d593
|
# Dataset of gorgon/ゴルゴーン/戈耳工 (Fate/Grand Order)
This is the dataset of gorgon/ゴルゴーン/戈耳工 (Fate/Grand Order), containing 242 images and their tags.
The core tags of this character are `long_hair, purple_hair, breasts, very_long_hair, purple_eyes, monster_girl, large_breasts, huge_breasts, snake_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 242 | 340.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gorgon_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 242 | 192.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gorgon_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 516 | 365.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gorgon_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 242 | 302.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gorgon_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 516 | 522.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gorgon_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/gorgon_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1boy, 1girl, hetero, penis, scales, blush, nipples, solo_focus, parted_bangs, sex, sweat, vaginal, claws, navel, nude, thighs, censored, cowgirl_position, forehead, girl_on_top, heavy_breathing, open_mouth, slit_pupils, snake_tail, cum_in_pussy, looking_at_viewer, parted_lips |
| 1 | 11 |  |  |  |  |  | 1girl, bustier, claws, scales, snake, solo, cleavage_cutout, looking_at_viewer, slit_pupils, navel, parted_lips, smile, thighs, forehead, wings |
| 2 | 5 |  |  |  |  |  | 1girl, grey_background, portrait, scales, simple_background, sketch, solo, parted_lips, looking_at_viewer, parted_bangs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | penis | scales | blush | nipples | solo_focus | parted_bangs | sex | sweat | vaginal | claws | navel | nude | thighs | censored | cowgirl_position | forehead | girl_on_top | heavy_breathing | open_mouth | slit_pupils | snake_tail | cum_in_pussy | looking_at_viewer | parted_lips | bustier | snake | solo | cleavage_cutout | smile | wings | grey_background | portrait | simple_background | sketch |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:--------|:---------|:--------|:----------|:-------------|:---------------|:------|:--------|:----------|:--------|:--------|:-------|:---------|:-----------|:-------------------|:-----------|:--------------|:------------------|:-------------|:--------------|:-------------|:---------------|:--------------------|:--------------|:----------|:--------|:-------|:------------------|:--------|:--------|:------------------|:-----------|:--------------------|:---------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | | X | | | X | | | | | | | | X | X | | X | | | X | | | | X | | | X | X | X | X | X | X | X | X | | | | |
| 2 | 5 |  |  |  |  |  | | X | | | X | | | | X | | | | | | | | | | | | | | | | | X | X | | | X | | | | X | X | X | X |
|
CyberHarem/gorgon_fgo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T12:31:29+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-13T03:14:58+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of gorgon/ゴルゴーン/戈耳工 (Fate/Grand Order)
==============================================
This is the dataset of gorgon/ゴルゴーン/戈耳工 (Fate/Grand Order), containing 242 images and their tags.
The core tags of this character are 'long\_hair, purple\_hair, breasts, very\_long\_hair, purple\_eyes, monster\_girl, large\_breasts, huge\_breasts, snake\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
62d6a88b2543d35c6cb2e7fa53ca525262f7922e
|
# Dataset Card for caBREU
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Website:** https://zenodo.org/record/
- **Point of Contact:** [email protected]
### Dataset Summary
caBreu is a summarization dataset. It consists of 3,000 articles, each averaging about 700 words in length, along with extreme, abstractive and extractive summaries,
manually generated by three annotators.
The source material for the articles was gathered from various Catalan news sources, including the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)),
[VilaWeb](https://www.vilaweb.cat/) and [NacióDigital](https://www.naciodigital.cat/).
This work is licensed under a [Creative Commons Attribution Non-commercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Supported Tasks and Leaderboards
Summarization
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
"id": "219",
"title": "Un estudi revela que el risc de morir després d’un ictus es multiplica si l’edat biològica és superior a la cronològica",
"subtitle": "El treball, realitzat per investigadors de l'Institut Hospital del Mar i publicat a 'Scientific Reports', ha analitzat dades de 600 pacients",
"content": "El risc de morir després de patir un ictus isquèmic es multiplica si l’edat biològica, que ve marcada pels hàbits de vida o el lloc de residència, entre altres factors, és superior a l’edat cronològica, marcada per la data de naixement.\nAixí ho constata un estudi realitzat per investigadors del Grup de recerca Neurovascular de l’Institut Hospital del Mar d’Investigacions Mèdiques (IMIM).\nSegons els resultats obtinguts, cada any d’edat biològica acumulat per sobre de l’edat cronològica, augmenta un 6% el risc de morir en un període de tres mesos després de patir un ictus.\nAlhora, també el grau de severitat de les seqüeles és més important.\nEl treball, que s’ha publicat a la revista ‘Scientific Reports’, ha analitzat les dades de gairebé 600 pacients.\nLa investigadora principal de l’estudi, la doctora Carolina Soriano-Tárraga, explica que es van analitzar gairebé 600 pacients atesos a l’Hospital del Mar per un ictus isquèmic i que en aquests casos, la mortalitat mitjana al cap de tres mesos es va situar entre el 15 i el 20%.\nEls investigadors van determinar la seva edat biològica a partir de marcadors epigenètics (canvis en els gens causats per factors externs), concretament, la metilació de l’ADN (el principal mecanisme epigenètic).\nAixí van comprovar, segons comenta Soriano-Tárraga, que l’edat biològica aporta informació extra.\n‘Es correlaciona molt bé amb l’edat cronològica, és molt similar, però té informació extra sobre l’estat funcional de la persona’, apunta Soriano-Tárraga.\nPer tant, afegeix, ‘és un millor predictor de mortalitat a tres mesos en comparació amb l’edat cronològica’, fins i tot sense tenir en compte altres factors externs, la gravetat de l’ictus o l’estat funcional previ del pacient.\nL’estudi també va analitzar el pes de l’edat biològica tenint en compte el tipus d’ictus.\nAixí, en els ictus aterotrombòtics, que acostumen a donar-se en pacients més joves -entre 55 i 60 anys de mitjana-, va ser en els que es va mostrar com un millor indicador de mortalitat.\nEn canvi, en els cardioembòlics, més habituals en pacients de més edat, l’efecte de l’edat biològica no era evident.\nAixò confirma, segons la investigadora principal de l’estudi, que l’edat biològica és un bon biomarcador.\n‘En un pacient jove l’edat biològica, l’estil de vida, té un major impacte, indica un envelliment més gran’ i un risc més elevat de mortalitat després de patir un atac, així com una major severitat de les seqüeles, destaca Soriano-Tárraga.\nÉs a dir, ‘no són tan joves’ com indica la seva edat cronològica, afegeix.\nEls investigadors assenyalen que estudis anteriors que comparaven pacients que havien patit un ictus amb pacients que no n’havien patit, indicaven que per una mateixa edat cronològica, el grau d’envelliment dels ictus era de 2,5 anys més gran de mitjana, sent de fins a 7 anys en els casos més joves.\nLa utilització d’aquest marcador ‘pot servir per detectar els pacients que tenen un risc més elevat de morir després de patir un ictus’, segons Soriano-Tárraga.\nPer la seva banda, el doctor Jordi Jiménez-Conde, neuròleg de l’Hospital del Mar responsable de la línia de recerca, ha remarcat que aquest estudi ‘és molt congruent amb les troballes dels nostres estudis previs, corroborant que l’edat biològica té un gran valor informatiu sobre l’estat d’envelliment real de les persones, sobre el seu risc de patir malalties associades a l’edat, i sobre la capacitat de l’individu d’afrontar-les’.\nHi ha dos tipus d’ictus principals, els hemorràgics (quan es trenca un vas sanguini al cervell) i els isquèmics (quan s’obtura una de les artèries del cervell).\nL’ictus aterotrombòtic és el tercer més freqüent i la seva causa subjacent, l’ateroesclerosi és tractable.\nAquesta patologia està associada a alts nivells de colesterol, a l’hàbit del tabac i la diabetis.\nEn general, el 90% dels ictus estan relacionats amb factors de risc com aquests.",
"category": [
"societat",
"sanitat"
],
"source": "vilaweb",
"summaries": {
"extreme": {
"a1": "El risc de morir en patir un ictus és més alt si els hàbits o la residència habitual no són favorables.",
"a2": "Un estudi confirma que el risc de morir després d'un ictus augmenta si l'edat biològica és superior a l'edat cronològica.",
"a3": "El risc de mort després d'un ictus isquèmic puja si l'edat biològica és superior a l'edat cronològica."
},
"abstractive": {
"a1": "Segons els resultats obtinguts del grup de recerca IMIM, el risc de morir per patir un ictus isquèmic es multiplica si l’edat biològica és superior a l’edat cronològica. La doctora Carolina Soriano-Tárraga afirma que es van analitzar 600 pacients i que la mortalitat al cap de tres mesos era del 15 i el 20%.",
"a2": "L'edat biològica ve marcada pel lloc de residència o pels hàbits de vida. Un estudi realitzat a l'Hospital del Mar amb uns 600 pacients confirma que el risc de mortaldat després d'un ictus és més gran si l'edat biològica del pacient supera la cronològica, que és la que ve marcada per la data de naixement.",
"a3": "Segons un estudi del Grup de Recerca Neurovascular de l'Institut Hospital del Mar d'Investigacions Mèdiques, la possibilitat de morir després d'haver patit un ictus isquèmic és superior quan l'edat biològica del pacient (determinada pels hàbits) és superior a l'edat cronològica (determinada pel naixement). Així, l'edat biològica és un marcador més fiable que la cronològica a l'hora de preveure la mortalitat."
},
"extractive": {
"a1": "El risc de morir després de patir un ictus isquèmic es multiplica si l’edat biològica, que ve marcada pels hàbits de vida o el lloc de residència, entre altres factors, és superior a l’edat cronològica, marcada per la data de naixement.\nSegons els resultats obtinguts, cada any d’edat biològica acumulat per sobre de l’edat cronològica, augmenta un 6% el risc de morir en un període de tres mesos després de patir un ictus.\nLa investigadora principal de l’estudi, la doctora Carolina Soriano-Tárraga, explica que es van analitzar gairebé 600 pacients atesos a l’Hospital del Mar per un ictus isquèmic i que en aquests casos, la mortalitat mitjana al cap de tres mesos es va situar entre el 15 i el 20%. \nPer tant, afegeix, ‘és un millor predictor de mortalitat a tres mesos en comparació amb l’edat cronològica’, fins i tot sense tenir en compte altres factors externs, la gravetat de l’ictus o l’estat funcional previ del pacient. ",
"a2": "El risc de morir després de patir un ictus isquèmic es multiplica si l’edat biològica, que ve marcada pels hàbits de vida o el lloc de residència, entre altres factors, és superior a l’edat cronològica, marcada per la data de naixement. \nSegons els resultats obtinguts, cada any d’edat biològica acumulat per sobre de l’edat cronològica, augmenta un 6% el risc de morir en un període de tres mesos després de patir un ictus. \nEl treball, que s’ha publicat a la revista ‘Scientific Reports’, ha analitzat les dades de gairebé 600 pacients.\nPer tant, afegeix, ‘és un millor predictor de mortalitat a tres mesos en comparació amb l’edat cronològica’, fins i tot sense tenir en compte altres factors externs, la gravetat de l’ictus o l’estat funcional previ del pacient.",
"a3": "El risc de morir després de patir un ictus isquèmic es multiplica si l’edat biològica, que ve marcada pels hàbits de vida o el lloc de residència, entre altres factors, és superior a l’edat cronològica, marcada per la data de naixement.\nSegons els resultats obtinguts, cada any d’edat biològica acumulat per sobre de l’edat cronològica, augmenta un 6% el risc de morir en un període de tres mesos després de patir un ictus. \nAixí ho constata un estudi realitzat per investigadors del Grup de recerca Neurovascular de l’Institut Hospital del Mar d’Investigacions Mèdiques (IMIM). \n‘En un pacient jove l’edat biològica, l’estil de vida, té un major impacte, indica un envelliment més gran’ i un risc més elevat de mortalitat després de patir un atac, així com una major severitat de les seqüeles, destaca Soriano-Tárraga. "
}
}
}
```
### Data Fields
- `id` (str): The id of the piece of news
- `title` (str): The title of the piece of news
- `subtitle` (str): The subtitle of the piece of news
- `content` (str): The text of the piece of news
- `category` (str): The category of the piece of news
- `source` (list): The source of the piece of news
- `summaries` (str): The summaries of the piece of news
- `extreme` (str): The extreme summaries of the piece of news
- `abstractive` (str): The abstractive summaries of the piece of news
- `extractive` (str): The extractive summaries of the piece of news
### Data Splits
We split our dataset into train, dev and test splits:
- train: 2,399 documents
- validation: 299 documents
- test: 301 documents
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
The Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)), [VilaWeb](https://www.vilaweb.cat/) and [NacióDigital](https://www.naciodigital.cat/).
### Annotations
Extractive, abstractive and extremes summaries.
#### Annotation process
Summaries were manually generated by three annotators, in accordance with explicit instructions.
For the extractive summaries, annotators were asked to select four sentences from the original text, encapsulating its most relevant information.
In the case of extreme summaries, annotators composed a concise 15 to 20-word sentence that encapsulated the text's primary theme, addressing the question of "What is this text about?"
Lastly, the abstractive summaries required annotators to generate a 50 to 60-word abstract, offering a succinct overview of the
text's key information in their own words. It was imperative that these summaries remained clear, objective, and devoid of personal opinions, ideas, or interpretations, while conforming to the text's tense, structure, and avoiding overly lengthy sentences.
#### Who are the annotators?
All the annotators are native speakers of Catalan.
### Personal and Sensitive Information
Since all data comes from public websites, no anonymization process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing information
This work is licensed under a [Creative Commons Attribution Non-commercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
[N/A]
### Contributions
[N/A]
|
projecte-aina/caBreu
|
[
"task_categories:summarization",
"annotations_creators:human-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-09-12T12:48:46+00:00
|
{"annotations_creators": ["human-generated"], "language_creators": ["expert-generated"], "language": ["ca"], "license": ["cc-by-nc-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["summarization"]}
|
2023-11-25T05:43:57+00:00
|
[] |
[
"ca"
] |
TAGS
#task_categories-summarization #annotations_creators-human-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #language-Catalan #license-cc-by-nc-4.0 #region-us
|
# Dataset Card for caBREU
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Website: URL
- Point of Contact: langtech@URL
### Dataset Summary
caBreu is a summarization dataset. It consists of 3,000 articles, each averaging about 700 words in length, along with extreme, abstractive and extractive summaries,
manually generated by three annotators.
The source material for the articles was gathered from various Catalan news sources, including the Catalan News Agency (Agència Catalana de Notícies; ACN),
VilaWeb and NacióDigital.
This work is licensed under a Creative Commons Attribution Non-commercial 4.0 International.
### Supported Tasks and Leaderboards
Summarization
### Languages
The dataset is in Catalan ('ca-ES').
## Dataset Structure
### Data Instances
### Data Fields
- 'id' (str): The id of the piece of news
- 'title' (str): The title of the piece of news
- 'subtitle' (str): The subtitle of the piece of news
- 'content' (str): The text of the piece of news
- 'category' (str): The category of the piece of news
- 'source' (list): The source of the piece of news
- 'summaries' (str): The summaries of the piece of news
- 'extreme' (str): The extreme summaries of the piece of news
- 'abstractive' (str): The abstractive summaries of the piece of news
- 'extractive' (str): The extractive summaries of the piece of news
### Data Splits
We split our dataset into train, dev and test splits:
- train: 2,399 documents
- validation: 299 documents
- test: 301 documents
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
The Catalan News Agency (Agència Catalana de Notícies; ACN), VilaWeb and NacióDigital.
### Annotations
Extractive, abstractive and extremes summaries.
#### Annotation process
Summaries were manually generated by three annotators, in accordance with explicit instructions.
For the extractive summaries, annotators were asked to select four sentences from the original text, encapsulating its most relevant information.
In the case of extreme summaries, annotators composed a concise 15 to 20-word sentence that encapsulated the text's primary theme, addressing the question of "What is this text about?"
Lastly, the abstractive summaries required annotators to generate a 50 to 60-word abstract, offering a succinct overview of the
text's key information in their own words. It was imperative that these summaries remained clear, objective, and devoid of personal opinions, ideas, or interpretations, while conforming to the text's tense, structure, and avoiding overly lengthy sentences.
#### Who are the annotators?
All the annotators are native speakers of Catalan.
### Personal and Sensitive Information
Since all data comes from public websites, no anonymization process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)
This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.
### Licensing information
This work is licensed under a Creative Commons Attribution Non-commercial 4.0 International.
[N/A]
### Contributions
[N/A]
|
[
"# Dataset Card for caBREU",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Website: URL\n- Point of Contact: langtech@URL",
"### Dataset Summary\n\ncaBreu is a summarization dataset. It consists of 3,000 articles, each averaging about 700 words in length, along with extreme, abstractive and extractive summaries, \nmanually generated by three annotators.\n\nThe source material for the articles was gathered from various Catalan news sources, including the Catalan News Agency (Agència Catalana de Notícies; ACN), \n VilaWeb and NacióDigital.\n\nThis work is licensed under a Creative Commons Attribution Non-commercial 4.0 International.",
"### Supported Tasks and Leaderboards\n\nSummarization",
"### Languages\n\nThe dataset is in Catalan ('ca-ES').",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- 'id' (str): The id of the piece of news\n- 'title' (str): The title of the piece of news\n- 'subtitle' (str): The subtitle of the piece of news\n- 'content' (str): The text of the piece of news\n- 'category' (str): The category of the piece of news\n- 'source' (list): The source of the piece of news\n- 'summaries' (str): The summaries of the piece of news\n- 'extreme' (str): The extreme summaries of the piece of news\n- 'abstractive' (str): The abstractive summaries of the piece of news\n- 'extractive' (str): The extractive summaries of the piece of news",
"### Data Splits\n\nWe split our dataset into train, dev and test splits:\n\n- train: 2,399 documents\n- validation: 299 documents\n- test: 301 documents",
"## Dataset Creation",
"### Curation Rationale\n\nWe created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?\n\nThe Catalan News Agency (Agència Catalana de Notícies; ACN), VilaWeb and NacióDigital.",
"### Annotations\n\nExtractive, abstractive and extremes summaries.",
"#### Annotation process\n\nSummaries were manually generated by three annotators, in accordance with explicit instructions.\n\nFor the extractive summaries, annotators were asked to select four sentences from the original text, encapsulating its most relevant information. \n\nIn the case of extreme summaries, annotators composed a concise 15 to 20-word sentence that encapsulated the text's primary theme, addressing the question of \"What is this text about?\" \n\nLastly, the abstractive summaries required annotators to generate a 50 to 60-word abstract, offering a succinct overview of the \ntext's key information in their own words. It was imperative that these summaries remained clear, objective, and devoid of personal opinions, ideas, or interpretations, while conforming to the text's tense, structure, and avoiding overly lengthy sentences.",
"#### Who are the annotators?\n\nAll the annotators are native speakers of Catalan.",
"### Personal and Sensitive Information\n\nSince all data comes from public websites, no anonymization process was performed.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nLanguage Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.",
"### Licensing information\n\nThis work is licensed under a Creative Commons Attribution Non-commercial 4.0 International.\n\n\n\n[N/A]",
"### Contributions\n\n[N/A]"
] |
[
"TAGS\n#task_categories-summarization #annotations_creators-human-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #language-Catalan #license-cc-by-nc-4.0 #region-us \n",
"# Dataset Card for caBREU",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Website: URL\n- Point of Contact: langtech@URL",
"### Dataset Summary\n\ncaBreu is a summarization dataset. It consists of 3,000 articles, each averaging about 700 words in length, along with extreme, abstractive and extractive summaries, \nmanually generated by three annotators.\n\nThe source material for the articles was gathered from various Catalan news sources, including the Catalan News Agency (Agència Catalana de Notícies; ACN), \n VilaWeb and NacióDigital.\n\nThis work is licensed under a Creative Commons Attribution Non-commercial 4.0 International.",
"### Supported Tasks and Leaderboards\n\nSummarization",
"### Languages\n\nThe dataset is in Catalan ('ca-ES').",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- 'id' (str): The id of the piece of news\n- 'title' (str): The title of the piece of news\n- 'subtitle' (str): The subtitle of the piece of news\n- 'content' (str): The text of the piece of news\n- 'category' (str): The category of the piece of news\n- 'source' (list): The source of the piece of news\n- 'summaries' (str): The summaries of the piece of news\n- 'extreme' (str): The extreme summaries of the piece of news\n- 'abstractive' (str): The abstractive summaries of the piece of news\n- 'extractive' (str): The extractive summaries of the piece of news",
"### Data Splits\n\nWe split our dataset into train, dev and test splits:\n\n- train: 2,399 documents\n- validation: 299 documents\n- test: 301 documents",
"## Dataset Creation",
"### Curation Rationale\n\nWe created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?\n\nThe Catalan News Agency (Agència Catalana de Notícies; ACN), VilaWeb and NacióDigital.",
"### Annotations\n\nExtractive, abstractive and extremes summaries.",
"#### Annotation process\n\nSummaries were manually generated by three annotators, in accordance with explicit instructions.\n\nFor the extractive summaries, annotators were asked to select four sentences from the original text, encapsulating its most relevant information. \n\nIn the case of extreme summaries, annotators composed a concise 15 to 20-word sentence that encapsulated the text's primary theme, addressing the question of \"What is this text about?\" \n\nLastly, the abstractive summaries required annotators to generate a 50 to 60-word abstract, offering a succinct overview of the \ntext's key information in their own words. It was imperative that these summaries remained clear, objective, and devoid of personal opinions, ideas, or interpretations, while conforming to the text's tense, structure, and avoiding overly lengthy sentences.",
"#### Who are the annotators?\n\nAll the annotators are native speakers of Catalan.",
"### Personal and Sensitive Information\n\nSince all data comes from public websites, no anonymization process was performed.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nWe hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.",
"### Discussion of Biases\n\n[N/A]",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nLanguage Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.",
"### Licensing information\n\nThis work is licensed under a Creative Commons Attribution Non-commercial 4.0 International.\n\n\n\n[N/A]",
"### Contributions\n\n[N/A]"
] |
[
73,
8,
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17,
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[
"passage: TAGS\n#task_categories-summarization #annotations_creators-human-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-unknown #language-Catalan #license-cc-by-nc-4.0 #region-us \n# Dataset Card for caBREU## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Website: URL\n- Point of Contact: langtech@URL### Dataset Summary\n\ncaBreu is a summarization dataset. It consists of 3,000 articles, each averaging about 700 words in length, along with extreme, abstractive and extractive summaries, \nmanually generated by three annotators.\n\nThe source material for the articles was gathered from various Catalan news sources, including the Catalan News Agency (Agència Catalana de Notícies; ACN), \n VilaWeb and NacióDigital.\n\nThis work is licensed under a Creative Commons Attribution Non-commercial 4.0 International.### Supported Tasks and Leaderboards\n\nSummarization### Languages\n\nThe dataset is in Catalan ('ca-ES').## Dataset Structure### Data Instances",
"passage: ### Data Fields\n\n- 'id' (str): The id of the piece of news\n- 'title' (str): The title of the piece of news\n- 'subtitle' (str): The subtitle of the piece of news\n- 'content' (str): The text of the piece of news\n- 'category' (str): The category of the piece of news\n- 'source' (list): The source of the piece of news\n- 'summaries' (str): The summaries of the piece of news\n- 'extreme' (str): The extreme summaries of the piece of news\n- 'abstractive' (str): The abstractive summaries of the piece of news\n- 'extractive' (str): The extractive summaries of the piece of news### Data Splits\n\nWe split our dataset into train, dev and test splits:\n\n- train: 2,399 documents\n- validation: 299 documents\n- test: 301 documents## Dataset Creation### Curation Rationale\n\nWe created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?\n\nThe Catalan News Agency (Agència Catalana de Notícies; ACN), VilaWeb and NacióDigital.### Annotations\n\nExtractive, abstractive and extremes summaries.#### Annotation process\n\nSummaries were manually generated by three annotators, in accordance with explicit instructions.\n\nFor the extractive summaries, annotators were asked to select four sentences from the original text, encapsulating its most relevant information. \n\nIn the case of extreme summaries, annotators composed a concise 15 to 20-word sentence that encapsulated the text's primary theme, addressing the question of \"What is this text about?\" \n\nLastly, the abstractive summaries required annotators to generate a 50 to 60-word abstract, offering a succinct overview of the \ntext's key information in their own words. It was imperative that these summaries remained clear, objective, and devoid of personal opinions, ideas, or interpretations, while conforming to the text's tense, structure, and avoiding overly lengthy sentences.#### Who are the annotators?\n\nAll the annotators are native speakers of Catalan.### Personal and Sensitive Information\n\nSince all data comes from public websites, no anonymization process was performed.## Considerations for Using the Data### Social Impact of Dataset\n\nWe hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.### Discussion of Biases\n\n[N/A]### Other Known Limitations\n\n[N/A]## Additional Information"
] |
11ec7e79d40ee77400b667b7bc5d8e88e4f0c26c
|
# Dataset Card for Evaluation run of posicube/Llama2-chat-AYT-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/posicube/Llama2-chat-AYT-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [posicube/Llama2-chat-AYT-13B](https://huggingface.co/posicube/Llama2-chat-AYT-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_posicube__Llama2-chat-AYT-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T23:47:31.356201](https://huggingface.co/datasets/open-llm-leaderboard/details_posicube__Llama2-chat-AYT-13B/blob/main/results_2023-10-25T23-47-31.356201.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.02380453020134228,
"em_stderr": 0.0015611256256327542,
"f1": 0.12621224832214753,
"f1_stderr": 0.002357573309097525,
"acc": 0.4247779852833908,
"acc_stderr": 0.009910000290951314
},
"harness|drop|3": {
"em": 0.02380453020134228,
"em_stderr": 0.0015611256256327542,
"f1": 0.12621224832214753,
"f1_stderr": 0.002357573309097525
},
"harness|gsm8k|5": {
"acc": 0.0887035633055345,
"acc_stderr": 0.007831458737058714
},
"harness|winogrande|5": {
"acc": 0.760852407261247,
"acc_stderr": 0.011988541844843915
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_posicube__Llama2-chat-AYT-13B
|
[
"region:us"
] |
2023-09-12T12:56:59+00:00
|
{"pretty_name": "Evaluation run of posicube/Llama2-chat-AYT-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [posicube/Llama2-chat-AYT-13B](https://huggingface.co/posicube/Llama2-chat-AYT-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_posicube__Llama2-chat-AYT-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T23:47:31.356201](https://huggingface.co/datasets/open-llm-leaderboard/details_posicube__Llama2-chat-AYT-13B/blob/main/results_2023-10-25T23-47-31.356201.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.02380453020134228,\n \"em_stderr\": 0.0015611256256327542,\n \"f1\": 0.12621224832214753,\n \"f1_stderr\": 0.002357573309097525,\n \"acc\": 0.4247779852833908,\n \"acc_stderr\": 0.009910000290951314\n },\n \"harness|drop|3\": {\n \"em\": 0.02380453020134228,\n \"em_stderr\": 0.0015611256256327542,\n \"f1\": 0.12621224832214753,\n \"f1_stderr\": 0.002357573309097525\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0887035633055345,\n \"acc_stderr\": 0.007831458737058714\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.760852407261247,\n \"acc_stderr\": 0.011988541844843915\n }\n}\n```", "repo_url": "https://huggingface.co/posicube/Llama2-chat-AYT-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T13_56_43.141895", "path": ["**/details_harness|arc:challenge|25_2023-09-12T13-56-43.141895.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T13-56-43.141895.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T23_47_31.356201", "path": ["**/details_harness|drop|3_2023-10-25T23-47-31.356201.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T23-47-31.356201.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T23_47_31.356201", "path": ["**/details_harness|gsm8k|5_2023-10-25T23-47-31.356201.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T23-47-31.356201.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T13_56_43.141895", "path": ["**/details_harness|hellaswag|10_2023-09-12T13-56-43.141895.parquet"]}, {"split": "latest", "path": 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|
2023-10-25T22:47:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of posicube/Llama2-chat-AYT-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model posicube/Llama2-chat-AYT-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T23:47:31.356201(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of posicube/Llama2-chat-AYT-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model posicube/Llama2-chat-AYT-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T23:47:31.356201(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of posicube/Llama2-chat-AYT-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model posicube/Llama2-chat-AYT-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T23:47:31.356201(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
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"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of posicube/Llama2-chat-AYT-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model posicube/Llama2-chat-AYT-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T23:47:31.356201(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
abfabbc4779f3248a9d740abc125f7869934dc3a
|
# Dataset Card for "description"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rshrott/description
|
[
"region:us"
] |
2023-09-12T12:59:04+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 91160798, "num_examples": 24489}], "download_size": 19465126, "dataset_size": 91160798}}
|
2023-09-12T13:19:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "description"
More Information needed
|
[
"# Dataset Card for \"description\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"description\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"description\"\n\nMore Information needed"
] |
5d6963cc9170b9405ff8ee9c2506f00fa4d668ce
|
# Hotspot disambiguation dataset
This repository contains the dataset assembled as part of a work **A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe**. Tha paper has been presented at the International Geoscience and Remote Sensing Symposium (**IGARSS**) 2023.
The full paper is available at https://arxiv.org/abs/2308.02508 .
The data folder contains two files:
- `dataset.csv`: this file contains the full cross-referenced dataset, obtained by conducing a temporal and spatial data intersection between the EFFIS burned areas and the MODIS/VIIRS hotspots.
- `dataset_500.csv`: this file contains a subset of the previous dataset (~500k data points), subsampled to obtain a dataset stratified with respect to the spatial distribution, and with a positive-negative proportion of 10%-90%. In addition to MODIS/VIIRS data points, additional columns have been added to improve the models' performances. This file is the one used to obtain the results showed in the paper.
## Code
The code and models used in this work are available at https://github.com/links-ads/hotspot-disambiguation .
## Contributions
- Angelica Urbanelli ([email protected])
- Luca Barco ([email protected])
- Edoardo Arnaudo ([email protected] | linksfoundation.com)
- Claudio Rossi ([email protected])
## BibTex
```
@inproceedings{urbanelli2023hotspot,
title={A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe},
author={Urbanelli, Angelica and Barco, Luca and Arnaudo, Edoardo and Rossi, Claudio},
booktitle={2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS},
year={2023}
}
```
## Licence
cc-by-4.0
## Acknowledgments
This work was carried out in the context of two H2020 projects: SAFERS (GA n.869353) and OVERWATCH (GA n.101082320), and presented at IGARSS 2023.
|
links-ads/hotspot-dataset
|
[
"arxiv:2308.02508",
"region:us"
] |
2023-09-12T13:04:38+00:00
|
{}
|
2023-09-13T15:13:14+00:00
|
[
"2308.02508"
] |
[] |
TAGS
#arxiv-2308.02508 #region-us
|
# Hotspot disambiguation dataset
This repository contains the dataset assembled as part of a work A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe. Tha paper has been presented at the International Geoscience and Remote Sensing Symposium (IGARSS) 2023.
The full paper is available at URL .
The data folder contains two files:
- 'URL': this file contains the full cross-referenced dataset, obtained by conducing a temporal and spatial data intersection between the EFFIS burned areas and the MODIS/VIIRS hotspots.
- 'dataset_500.csv': this file contains a subset of the previous dataset (~500k data points), subsampled to obtain a dataset stratified with respect to the spatial distribution, and with a positive-negative proportion of 10%-90%. In addition to MODIS/VIIRS data points, additional columns have been added to improve the models' performances. This file is the one used to obtain the results showed in the paper.
## Code
The code and models used in this work are available at URL .
## Contributions
- Angelica Urbanelli (angelica.urbanelli@URL)
- Luca Barco (URL@URL)
- Edoardo Arnaudo (edoardo.arnaudo@URL | URL)
- Claudio Rossi (URL@URL)
## BibTex
## Licence
cc-by-4.0
## Acknowledgments
This work was carried out in the context of two H2020 projects: SAFERS (GA n.869353) and OVERWATCH (GA n.101082320), and presented at IGARSS 2023.
|
[
"# Hotspot disambiguation dataset\n\nThis repository contains the dataset assembled as part of a work A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe. Tha paper has been presented at the International Geoscience and Remote Sensing Symposium (IGARSS) 2023. \nThe full paper is available at URL . \n\nThe data folder contains two files:\n- 'URL': this file contains the full cross-referenced dataset, obtained by conducing a temporal and spatial data intersection between the EFFIS burned areas and the MODIS/VIIRS hotspots.\n- 'dataset_500.csv': this file contains a subset of the previous dataset (~500k data points), subsampled to obtain a dataset stratified with respect to the spatial distribution, and with a positive-negative proportion of 10%-90%. In addition to MODIS/VIIRS data points, additional columns have been added to improve the models' performances. This file is the one used to obtain the results showed in the paper.",
"## Code\nThe code and models used in this work are available at URL .",
"## Contributions\n- Angelica Urbanelli (angelica.urbanelli@URL)\n- Luca Barco (URL@URL)\n- Edoardo Arnaudo (edoardo.arnaudo@URL | URL)\n- Claudio Rossi (URL@URL)",
"## BibTex",
"## Licence\ncc-by-4.0",
"## Acknowledgments\nThis work was carried out in the context of two H2020 projects: SAFERS (GA n.869353) and OVERWATCH (GA n.101082320), and presented at IGARSS 2023."
] |
[
"TAGS\n#arxiv-2308.02508 #region-us \n",
"# Hotspot disambiguation dataset\n\nThis repository contains the dataset assembled as part of a work A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe. Tha paper has been presented at the International Geoscience and Remote Sensing Symposium (IGARSS) 2023. \nThe full paper is available at URL . \n\nThe data folder contains two files:\n- 'URL': this file contains the full cross-referenced dataset, obtained by conducing a temporal and spatial data intersection between the EFFIS burned areas and the MODIS/VIIRS hotspots.\n- 'dataset_500.csv': this file contains a subset of the previous dataset (~500k data points), subsampled to obtain a dataset stratified with respect to the spatial distribution, and with a positive-negative proportion of 10%-90%. In addition to MODIS/VIIRS data points, additional columns have been added to improve the models' performances. This file is the one used to obtain the results showed in the paper.",
"## Code\nThe code and models used in this work are available at URL .",
"## Contributions\n- Angelica Urbanelli (angelica.urbanelli@URL)\n- Luca Barco (URL@URL)\n- Edoardo Arnaudo (edoardo.arnaudo@URL | URL)\n- Claudio Rossi (URL@URL)",
"## BibTex",
"## Licence\ncc-by-4.0",
"## Acknowledgments\nThis work was carried out in the context of two H2020 projects: SAFERS (GA n.869353) and OVERWATCH (GA n.101082320), and presented at IGARSS 2023."
] |
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[
"passage: TAGS\n#arxiv-2308.02508 #region-us \n# Hotspot disambiguation dataset\n\nThis repository contains the dataset assembled as part of a work A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe. Tha paper has been presented at the International Geoscience and Remote Sensing Symposium (IGARSS) 2023. \nThe full paper is available at URL . \n\nThe data folder contains two files:\n- 'URL': this file contains the full cross-referenced dataset, obtained by conducing a temporal and spatial data intersection between the EFFIS burned areas and the MODIS/VIIRS hotspots.\n- 'dataset_500.csv': this file contains a subset of the previous dataset (~500k data points), subsampled to obtain a dataset stratified with respect to the spatial distribution, and with a positive-negative proportion of 10%-90%. In addition to MODIS/VIIRS data points, additional columns have been added to improve the models' performances. This file is the one used to obtain the results showed in the paper.## Code\nThe code and models used in this work are available at URL .## Contributions\n- Angelica Urbanelli (angelica.urbanelli@URL)\n- Luca Barco (URL@URL)\n- Edoardo Arnaudo (edoardo.arnaudo@URL | URL)\n- Claudio Rossi (URL@URL)## BibTex## Licence\ncc-by-4.0## Acknowledgments\nThis work was carried out in the context of two H2020 projects: SAFERS (GA n.869353) and OVERWATCH (GA n.101082320), and presented at IGARSS 2023."
] |
b276915b7c3e527f5ac15b66829572f8b1c4e40b
|
# Dataset of suzuna/スズナ (Pokémon)
This is the dataset of suzuna/スズナ (Pokémon), containing 475 images and their tags.
The core tags of this character are `black_hair, twintails, hair_ornament, long_hair, breasts, multi-tied_hair, hairclip, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 475 | 406.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuna_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 475 | 240.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuna_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 975 | 457.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuna_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 475 | 360.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuna_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 975 | 620.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suzuna_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/suzuna_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, solo, nipples, nude, medium_breasts, blush, navel, yellow_eyes, large_breasts, pussy, smile |
| 1 | 11 |  |  |  |  |  | 1girl, blush, solo, sweat, lactation, alternate_breast_size, heart, open_mouth, gigantic_breasts, nude, huge_breasts, pussy_juice, restrained, tears, tentacles, tongue, huge_nipples, large_areolae, saliva |
| 2 | 34 |  |  |  |  |  | 1girl, white_shirt, brown_skirt, miniskirt, sweater_around_waist, open_mouth, kneehighs, striped_socks, solo, shoes, eyelashes, looking_at_viewer, tongue, :d, brown_footwear, collared_shirt, bowtie, simple_background, white_background, full_body, standing |
| 3 | 5 |  |  |  |  |  | 1girl, pokemon_(creature), skirt, striped, sweater_around_waist, kneehighs, smile, bow, shoes, blue_eyes, open_mouth |
| 4 | 9 |  |  |  |  |  | 1girl, hetero, nipples, open_shirt, blush, sex, censored, medium_breasts, penis, socks, sweater_around_waist, vaginal, 1boy, open_mouth, solo_focus, striped, cum_in_pussy, skirt, grabbing |
| 5 | 5 |  |  |  |  |  | 1girl, blush, large_breasts, open_shirt, skirt, striped_panties, no_bra, pokemon_(creature), sweater_around_waist, yellow_eyes, covered_nipples, pantyshot, socks, solo |
| 6 | 7 |  |  |  |  |  | 1girl, alternate_breast_size, blush, gigantic_breasts, solo, cleavage, looking_at_viewer, open_mouth, collarbone, :d, blue_bikini, eyelashes, shiny_skin, thighs, standing, thick_eyebrows, tongue, very_long_hair |
| 7 | 5 |  |  |  |  |  | 1girl, simple_background, solo, blush, large_breasts, white_background, cleavage, smile, blue_bikini, collarbone, eyelashes, looking_at_viewer, open_mouth, shiny_skin, white_bikini, yellow_eyes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | nipples | nude | medium_breasts | blush | navel | yellow_eyes | large_breasts | pussy | smile | sweat | lactation | alternate_breast_size | heart | open_mouth | gigantic_breasts | huge_breasts | pussy_juice | restrained | tears | tentacles | tongue | huge_nipples | large_areolae | saliva | white_shirt | brown_skirt | miniskirt | sweater_around_waist | kneehighs | striped_socks | shoes | eyelashes | looking_at_viewer | :d | brown_footwear | collared_shirt | bowtie | simple_background | white_background | full_body | standing | pokemon_(creature) | skirt | striped | bow | blue_eyes | hetero | open_shirt | sex | censored | penis | socks | vaginal | 1boy | solo_focus | cum_in_pussy | grabbing | striped_panties | no_bra | covered_nipples | pantyshot | cleavage | collarbone | blue_bikini | shiny_skin | thighs | thick_eyebrows | very_long_hair | white_bikini |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:-------|:-----------------|:--------|:--------|:--------------|:----------------|:--------|:--------|:--------|:------------|:------------------------|:--------|:-------------|:-------------------|:---------------|:--------------|:-------------|:--------|:------------|:---------|:---------------|:----------------|:---------|:--------------|:--------------|:------------|:-----------------------|:------------|:----------------|:--------|:------------|:--------------------|:-----|:-----------------|:-----------------|:---------|:--------------------|:-------------------|:------------|:-----------|:---------------------|:--------|:----------|:------|:------------|:---------|:-------------|:------|:-----------|:--------|:--------|:----------|:-------|:-------------|:---------------|:-----------|:------------------|:---------|:------------------|:------------|:-----------|:-------------|:--------------|:-------------|:---------|:-----------------|:-----------------|:---------------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | | X | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 34 |  |  |  |  |  | X | X | | | | | | | | | | | | | | X | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | X | X | | X | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | X | | X | X | | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | | | | | X | | | | X | | | | | | X | X | X | X | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | | | | X | | | | | | | | X | | X | X | | | | | | X | | | | | | | | | | | X | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | |
| 7 | 5 |  |  |  |  |  | X | X | | | | X | | X | X | | X | | | | | X | | | | | | | | | | | | | | | | | | X | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | X |
|
CyberHarem/suzuna_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T13:05:02+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T22:08:46+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of suzuna/スズナ (Pokémon)
===============================
This is the dataset of suzuna/スズナ (Pokémon), containing 475 images and their tags.
The core tags of this character are 'black\_hair, twintails, hair\_ornament, long\_hair, breasts, multi-tied\_hair, hairclip, brown\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
5fa671c9237eeea307b4d45678c9f8f601e7264f
|
# Dataset of miyamoto_frederica/宮本フレデリカ/미야모토프레데리카 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of miyamoto_frederica/宮本フレデリカ/미야모토프레데리카 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `blonde_hair, short_hair, green_eyes, bangs, breasts, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 754.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 419.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1169 | 876.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 659.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1169 | 1.26 GiB | [Download](https://huggingface.co/datasets/CyberHarem/miyamoto_frederica_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/miyamoto_frederica_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, simple_background, smile, bare_shoulders, collarbone, upper_body, white_background, off_shoulder, sweater, :3, closed_mouth |
| 1 | 5 |  |  |  |  |  | 1girl, blush, bridal_veil, collarbone, earrings, looking_at_viewer, smile, solo, wedding_dress, white_dress, braid, bride, cleavage, pearl_necklace, upper_body, asymmetrical_hair, bouquet, closed_mouth, tiara, white_background, white_rose, bare_shoulders, floral_print, hair_between_eyes, open_mouth, see-through |
| 2 | 6 |  |  |  |  |  | black_gloves, blush, brooch, butterfly_hair_ornament, long_sleeves, looking_at_viewer, 1girl, ascot, braid, corset, frills, smile, solo, bow, jacket, lace_gloves, parted_lips, ribbon, sitting, thigh_strap, belt, heart_hair_ornament, lace_trim, shiny_hair, shirt, short_shorts |
| 3 | 15 |  |  |  |  |  | 1girl, black_gloves, hat, looking_at_viewer, solo, smile, sleeveless, striped, blush, dress, skirt, corset, pink_headwear, heart_hair_ornament, open_mouth, black_necktie, floral_print, frills, garter_straps, thighhighs |
| 4 | 10 |  |  |  |  |  | 1girl, black_gloves, head_wings, looking_at_viewer, solo, bare_shoulders, frills, maid_headdress, apron, arm_garter, center_opening, smile, blush, lace_trim, black_ribbon, bow, cleavage_cutout, lace_gloves, large_breasts, open_mouth, parted_lips, pink_wings, ribbon_trim, simple_background, sleeveless_dress, upper_body, white_background, chocolate, cross-laced_clothes, demon_wings, hair_ribbon, heart_hair_ornament |
| 5 | 6 |  |  |  |  |  | 1girl, heart, solo, cleavage, detached_collar, frills, hair_bow, looking_at_viewer, maid_headdress, smile, wrist_cuffs, apron, blush, braid, detached_sleeves, pink_bow, neck_ribbon, one_eye_closed, simple_background |
| 6 | 5 |  |  |  |  |  | 1girl, apron, maid_headdress, smile, solo, cleavage, thighhighs, tongue_out, blush, one_eye_closed, bow, cupcake, garter_straps, looking_at_viewer |
| 7 | 6 |  |  |  |  |  | 1girl, bare_shoulders, blush, braided_bangs, earrings, looking_at_viewer, sleeveless_dress, solo, feathers, frills, plaid_dress, smile, beret, black_headwear, black_dress, bow, closed_mouth, hair_ornament |
| 8 | 6 |  |  |  |  |  | 1girl, beret, bow, bracelet, earrings, looking_at_viewer, nail_polish, sleeveless_dress, smile, solo, armlet, bare_shoulders, black_headwear, blush, braid, grey_dress, fishnet_pantyhose, high_heels, pink_nails, plaid_dress, arm_up, armpits, frills, standing, wrist_cuffs |
| 9 | 7 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, smile, solo, necklace, open_mouth, pink_dress, bare_shoulders, blush, rose, collarbone, frills, one_eye_closed, pink_flower, strapless_dress, white_gloves, ;d, elbow_gloves, feathers, hat_flower, petals |
| 10 | 5 |  |  |  |  |  | 1girl, earrings, hair_flower, looking_at_viewer, blush, bracelet, necklace, cleavage, nail_polish, red_dress, rose, smile, upper_body, coat, collarbone, heart, holding, one_eye_closed, pink_nails, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | blush | simple_background | smile | bare_shoulders | collarbone | upper_body | white_background | off_shoulder | sweater | :3 | closed_mouth | bridal_veil | earrings | wedding_dress | white_dress | braid | bride | cleavage | pearl_necklace | asymmetrical_hair | bouquet | tiara | white_rose | floral_print | hair_between_eyes | open_mouth | see-through | black_gloves | brooch | butterfly_hair_ornament | long_sleeves | ascot | corset | frills | bow | jacket | lace_gloves | parted_lips | ribbon | sitting | thigh_strap | belt | heart_hair_ornament | lace_trim | shiny_hair | shirt | short_shorts | hat | sleeveless | striped | dress | skirt | pink_headwear | black_necktie | garter_straps | thighhighs | head_wings | maid_headdress | apron | arm_garter | center_opening | black_ribbon | cleavage_cutout | large_breasts | pink_wings | ribbon_trim | sleeveless_dress | chocolate | cross-laced_clothes | demon_wings | hair_ribbon | heart | detached_collar | hair_bow | wrist_cuffs | detached_sleeves | pink_bow | neck_ribbon | one_eye_closed | tongue_out | cupcake | braided_bangs | feathers | plaid_dress | beret | black_headwear | black_dress | hair_ornament | bracelet | nail_polish | armlet | grey_dress | fishnet_pantyhose | high_heels | pink_nails | arm_up | armpits | standing | necklace | pink_dress | rose | pink_flower | strapless_dress | white_gloves | ;d | elbow_gloves | hat_flower | petals | hair_flower | red_dress | coat | holding | solo_focus |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:-------|:--------|:--------------------|:--------|:-----------------|:-------------|:-------------|:-------------------|:---------------|:----------|:-----|:---------------|:--------------|:-----------|:----------------|:--------------|:--------|:--------|:-----------|:-----------------|:--------------------|:----------|:--------|:-------------|:---------------|:--------------------|:-------------|:--------------|:---------------|:---------|:--------------------------|:---------------|:--------|:---------|:---------|:------|:---------|:--------------|:--------------|:---------|:----------|:--------------|:-------|:----------------------|:------------|:-------------|:--------|:---------------|:------|:-------------|:----------|:--------|:--------|:----------------|:----------------|:----------------|:-------------|:-------------|:-----------------|:--------|:-------------|:-----------------|:---------------|:------------------|:----------------|:-------------|:--------------|:-------------------|:------------|:----------------------|:--------------|:--------------|:--------|:------------------|:-----------|:--------------|:-------------------|:-----------|:--------------|:-----------------|:-------------|:----------|:----------------|:-----------|:--------------|:--------|:-----------------|:--------------|:----------------|:-----------|:--------------|:---------|:-------------|:--------------------|:-------------|:-------------|:---------|:----------|:-----------|:-----------|:-------------|:-------|:--------------|:------------------|:---------------|:-----|:---------------|:-------------|:---------|:--------------|:------------|:-------|:----------|:-------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | | X | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | | X | | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 15 |  |  |  |  |  | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | X | | X | | X | | | | | X | X | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | X | | X | | | | | | X | X | | X | X | | | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | X | X | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 9 | 7 |  |  |  |  |  | X | X | X | X | | X | X | X | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | | X | | X | | X | X | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | X | X | | | | | X | | | | X | | X | | | | | | | | X | X | X | X | X |
|
CyberHarem/miyamoto_frederica_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T13:11:09+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T12:27:22+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of miyamoto\_frederica/宮本フレデリカ/미야모토프레데리카 (THE iDOLM@STER: Cinderella Girls)
===================================================================================
This is the dataset of miyamoto\_frederica/宮本フレデリカ/미야모토프레데리카 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'blonde\_hair, short\_hair, green\_eyes, bangs, breasts, medium\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
5d918c5e27f43e2f4013125cb929a6792b7c0131
|
# Dataset Card for "wiki5m_trans_bloomz"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ze-lin/wiki5m_trans_bloomz
|
[
"region:us"
] |
2023-09-12T13:16:09+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "head_id", "dtype": "string"}, {"name": "head", "dtype": "string"}, {"name": "relation", "dtype": "string"}, {"name": "tail_id", "dtype": "string"}, {"name": "tail", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 9678522818, "num_examples": 20599278}, {"name": "valid", "num_bytes": 2429054, "num_examples": 5163}, {"name": "test", "num_bytes": 2407123, "num_examples": 5133}], "download_size": 4290706751, "dataset_size": 9683358995}}
|
2023-09-12T13:22:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wiki5m_trans_bloomz"
More Information needed
|
[
"# Dataset Card for \"wiki5m_trans_bloomz\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki5m_trans_bloomz\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki5m_trans_bloomz\"\n\nMore Information needed"
] |
d23a3934d037b5ead2b391f3e5478d522b01860e
|
Dataset for training MiniGPT4 from scratch in MMPretrain
---
More information and guide can be found in docs of [MMPretrain](https://mmpretrain.readthedocs.io/en/latest/).
license: cc-by-nc-4.0
|
deepHug/minigpt4_training_for_MMPretrain
|
[
"task_categories:text-retrieval",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-09-12T13:21:00+00:00
|
{"language": ["en", "zh"], "license": "cc-by-nc-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-retrieval", "conversational"]}
|
2023-09-13T06:48:26+00:00
|
[] |
[
"en",
"zh"
] |
TAGS
#task_categories-text-retrieval #task_categories-conversational #size_categories-1K<n<10K #language-English #language-Chinese #license-cc-by-nc-4.0 #region-us
|
Dataset for training MiniGPT4 from scratch in MMPretrain
---
More information and guide can be found in docs of MMPretrain.
license: cc-by-nc-4.0
|
[] |
[
"TAGS\n#task_categories-text-retrieval #task_categories-conversational #size_categories-1K<n<10K #language-English #language-Chinese #license-cc-by-nc-4.0 #region-us \n"
] |
[
60
] |
[
"passage: TAGS\n#task_categories-text-retrieval #task_categories-conversational #size_categories-1K<n<10K #language-English #language-Chinese #license-cc-by-nc-4.0 #region-us \n"
] |
0fdf31a75a376c3205ba8aa12531741d91f9d232
|
# Dataset Card for LAION-2B-multi Vietnamese subset
### Dataset Summary
Filter the Vietnamese subset from [Laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi)
To get the subset of your language, check out [this notebook](https://colab.research.google.com/drive/1bPvgFPKEIjzw7wT_9GwlDPvgTYDFdblr?usp=sharing)
|
imthanhlv/laion2B-multi-Vietnamese-subset
|
[
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:vi",
"license:cc-by-4.0",
"region:us"
] |
2023-09-12T13:28:42+00:00
|
{"language": ["vi"], "license": "cc-by-4.0", "task_categories": ["text-to-image", "image-to-text"]}
|
2023-09-12T18:51:20+00:00
|
[] |
[
"vi"
] |
TAGS
#task_categories-text-to-image #task_categories-image-to-text #language-Vietnamese #license-cc-by-4.0 #region-us
|
# Dataset Card for LAION-2B-multi Vietnamese subset
### Dataset Summary
Filter the Vietnamese subset from Laion2B-multi
To get the subset of your language, check out this notebook
|
[
"# Dataset Card for LAION-2B-multi Vietnamese subset",
"### Dataset Summary\n\nFilter the Vietnamese subset from Laion2B-multi\n\nTo get the subset of your language, check out this notebook"
] |
[
"TAGS\n#task_categories-text-to-image #task_categories-image-to-text #language-Vietnamese #license-cc-by-4.0 #region-us \n",
"# Dataset Card for LAION-2B-multi Vietnamese subset",
"### Dataset Summary\n\nFilter the Vietnamese subset from Laion2B-multi\n\nTo get the subset of your language, check out this notebook"
] |
[
46,
15,
32
] |
[
"passage: TAGS\n#task_categories-text-to-image #task_categories-image-to-text #language-Vietnamese #license-cc-by-4.0 #region-us \n# Dataset Card for LAION-2B-multi Vietnamese subset### Dataset Summary\n\nFilter the Vietnamese subset from Laion2B-multi\n\nTo get the subset of your language, check out this notebook"
] |
d458f3eeee1dec432d149af2c3a99150c2d3f068
|
# Dataset of Sakurajima Mai
This is the dataset of Sakurajima Mai, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 693 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 693 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 693 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 693 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/sakurajima_mai_seishunbutayarou
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T13:34:23+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:34:06+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Sakurajima Mai
=========================
This is the dataset of Sakurajima Mai, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
fa971f7373968e93844d4807ea7c24384c54d54d
|
This datasets contain two parts:
**occupancies**:
This part includes occupancies and point clouds. For specific usage, you can refer to [Occupancy Networks](https://github.com/autonomousvision/occupancy_networks).
**rendered_images**:
This part is a supplementary rendering dataset of [Objaverse dataset](https://huggingface.co/datasets/allenai/objaverse). The rendering code is sourced from [zero123](https://github.com/cvlab-columbia/zero123), with the difference being the use of the Eevee renderer, and the camera positions are fixed at 12 locations on a sphere with a radius of 2. They are numbered from 12 to 23, corresponding to:
- 12: Front view
- 13: Side view (left)
- 14: Top view
- 15: Back view
- 16: Side view (right)
- 17: Bottom view
- 18-20: Three equidistant points on the polar angle of 45°
- 21-23: Three equidistant points on the polar angle of 135°
|
BAAI/Objaverse-MIX
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-09-12T13:41:26+00:00
|
{"license": "cc-by-4.0"}
|
2023-10-10T23:59:42+00:00
|
[] |
[] |
TAGS
#license-cc-by-4.0 #region-us
|
This datasets contain two parts:
occupancies:
This part includes occupancies and point clouds. For specific usage, you can refer to Occupancy Networks.
rendered_images:
This part is a supplementary rendering dataset of Objaverse dataset. The rendering code is sourced from zero123, with the difference being the use of the Eevee renderer, and the camera positions are fixed at 12 locations on a sphere with a radius of 2. They are numbered from 12 to 23, corresponding to:
- 12: Front view
- 13: Side view (left)
- 14: Top view
- 15: Back view
- 16: Side view (right)
- 17: Bottom view
- 18-20: Three equidistant points on the polar angle of 45°
- 21-23: Three equidistant points on the polar angle of 135°
|
[] |
[
"TAGS\n#license-cc-by-4.0 #region-us \n"
] |
[
15
] |
[
"passage: TAGS\n#license-cc-by-4.0 #region-us \n"
] |
bf24da20c68d1522a46da72a0a67e71b0906c91d
|
# Dataset Card for "TruckDet2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
malteee/TruckDet2
|
[
"region:us"
] |
2023-09-12T13:47:09+00:00
|
{"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}, {"name": "id", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 78780289.0, "num_examples": 651}, {"name": "test", "num_bytes": 3798987.0, "num_examples": 82}], "download_size": 82582528, "dataset_size": 82579276.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-09-12T13:59:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TruckDet2"
More Information needed
|
[
"# Dataset Card for \"TruckDet2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TruckDet2\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TruckDet2\"\n\nMore Information needed"
] |
116e1821e29bc4a864a31b1509331053392b8b41
|
# Dataset of Koga Tomoe
This is the dataset of Koga Tomoe, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 439 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 439 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 439 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 439 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/koga_tomoe_seishunbutayarou
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T13:49:25+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:34:08+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Koga Tomoe
=====================
This is the dataset of Koga Tomoe, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
e3caeb60ac6a54dad89f4c2bc00d85a394806ea1
|
# Dataset Card for Evaluation run of Enno-Ai/vigogne2-enno-13b-sft-lora-4bit
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Enno-Ai/vigogne2-enno-13b-sft-lora-4bit
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Enno-Ai/vigogne2-enno-13b-sft-lora-4bit](https://huggingface.co/Enno-Ai/vigogne2-enno-13b-sft-lora-4bit) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Enno-Ai__vigogne2-enno-13b-sft-lora-4bit",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T10:29:28.223248](https://huggingface.co/datasets/open-llm-leaderboard/details_Enno-Ai__vigogne2-enno-13b-sft-lora-4bit/blob/main/results_2023-10-23T10-29-28.223248.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.38370385906040266,
"em_stderr": 0.00498003573381493,
"f1": 0.4364649748322163,
"f1_stderr": 0.004838389403253292,
"acc": 0.3855253166488449,
"acc_stderr": 0.006453825756692964
},
"harness|drop|3": {
"em": 0.38370385906040266,
"em_stderr": 0.00498003573381493,
"f1": 0.4364649748322163,
"f1_stderr": 0.004838389403253292
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492625
},
"harness|winogrande|5": {
"acc": 0.7695343330702447,
"acc_stderr": 0.011835872164836666
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
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## Additional Information
### Dataset Curators
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### Licensing Information
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### Citation Information
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### Contributions
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|
open-llm-leaderboard/details_Enno-Ai__vigogne2-enno-13b-sft-lora-4bit
|
[
"region:us"
] |
2023-09-12T13:54:04+00:00
|
{"pretty_name": "Evaluation run of Enno-Ai/vigogne2-enno-13b-sft-lora-4bit", "dataset_summary": "Dataset automatically created during the evaluation run of model [Enno-Ai/vigogne2-enno-13b-sft-lora-4bit](https://huggingface.co/Enno-Ai/vigogne2-enno-13b-sft-lora-4bit) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Enno-Ai__vigogne2-enno-13b-sft-lora-4bit\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T10:29:28.223248](https://huggingface.co/datasets/open-llm-leaderboard/details_Enno-Ai__vigogne2-enno-13b-sft-lora-4bit/blob/main/results_2023-10-23T10-29-28.223248.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.38370385906040266,\n \"em_stderr\": 0.00498003573381493,\n \"f1\": 0.4364649748322163,\n \"f1_stderr\": 0.004838389403253292,\n \"acc\": 0.3855253166488449,\n \"acc_stderr\": 0.006453825756692964\n },\n \"harness|drop|3\": {\n \"em\": 0.38370385906040266,\n \"em_stderr\": 0.00498003573381493,\n \"f1\": 0.4364649748322163,\n \"f1_stderr\": 0.004838389403253292\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \"acc_stderr\": 0.0010717793485492625\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836666\n }\n}\n```", "repo_url": "https://huggingface.co/Enno-Ai/vigogne2-enno-13b-sft-lora-4bit", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-23T09:29:40+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of Enno-Ai/vigogne2-enno-13b-sft-lora-4bit
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Enno-Ai/vigogne2-enno-13b-sft-lora-4bit on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T10:29:28.223248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Enno-Ai/vigogne2-enno-13b-sft-lora-4bit",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Enno-Ai/vigogne2-enno-13b-sft-lora-4bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T10:29:28.223248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Enno-Ai/vigogne2-enno-13b-sft-lora-4bit",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Enno-Ai/vigogne2-enno-13b-sft-lora-4bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T10:29:28.223248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
30,
31,
178,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Enno-Ai/vigogne2-enno-13b-sft-lora-4bit## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Enno-Ai/vigogne2-enno-13b-sft-lora-4bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T10:29:28.223248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
e7535693294913e3b3a94f3922139d397e715eb0
|
# Dataset of Futaba Rio
This is the dataset of Futaba Rio, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 448 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 448 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 448 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 448 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/futaba_rio_seishunbutayarou
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T14:04:58+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:34:10+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Futaba Rio
=====================
This is the dataset of Futaba Rio, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
bfff134260f277e06a46299b367b6fc1ffbeb806
|
card
|
Cloroform/dataset
|
[
"region:us"
] |
2023-09-12T14:07:21+00:00
|
{}
|
2023-09-12T14:08:30+00:00
|
[] |
[] |
TAGS
#region-us
|
card
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
945b0b453e3a7707f4a3b2c6f192d8474a438f98
|
# Dataset Card for "Emociones-BackTranslated-Preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
alexcom/Emociones-BackTranslated-Preprocessed
|
[
"region:us"
] |
2023-09-12T14:09:01+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "Tweet", "dtype": "string"}, {"name": "Emocion", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1937330, "num_examples": 11544}, {"name": "validation", "num_bytes": 884924, "num_examples": 5638}, {"name": "test", "num_bytes": 869707, "num_examples": 5542}], "download_size": 1441495, "dataset_size": 3691961}}
|
2023-10-31T20:47:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Emociones-BackTranslated-Preprocessed"
More Information needed
|
[
"# Dataset Card for \"Emociones-BackTranslated-Preprocessed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Emociones-BackTranslated-Preprocessed\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Emociones-BackTranslated-Preprocessed\"\n\nMore Information needed"
] |
530b8e9442b5babc04b48b8bcde9a02c8d09dc56
|
# Dataset of passionlip/パッションリップ/Passionlip (Fate/Grand Order)
This is the dataset of passionlip/パッションリップ/Passionlip (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `purple_hair, breasts, long_hair, ribbon, hair_ribbon, huge_breasts, very_long_hair, pink_eyes, purple_eyes, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 748.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/passionlip_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 407.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/passionlip_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1206 | 831.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/passionlip_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 654.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/passionlip_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1206 | 1.17 GiB | [Download](https://huggingface.co/datasets/CyberHarem/passionlip_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/passionlip_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, bare_shoulders, blush, claws, looking_at_viewer, o-ring_top, solo, belt_collar, pink_ribbon, smile |
| 1 | 7 |  |  |  |  |  | 1girl, bare_shoulders, belt_collar, hair_bow, looking_at_viewer, o-ring_top, solo, upper_body, claws, parted_lips, blush, simple_background, white_background, pink_ribbon |
| 2 | 5 |  |  |  |  |  | 1boy, 1girl, cum_on_breasts, looking_at_viewer, open_mouth, solo_focus, bare_shoulders, belt_collar, blush, o-ring_top, claws, ejaculation, paizuri_under_clothes, pink_ribbon, gigantic_breasts, inverted_nipples, nude, pov |
| 3 | 9 |  |  |  |  |  | 1boy, 1girl, blush, gigantic_breasts, hetero, nipples, paizuri, solo_focus, breast_grab, ejaculation, grabbing, penis, claws, open_mouth, mosaic_censoring, bare_shoulders, breasts_squeezed_together, cum_on_breasts, looking_at_viewer, pink_ribbon, sweat, collar, lactation, pov_crotch |
| 4 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, nipples, nude, solo, claws, blush, gigantic_breasts, pink_ribbon, claw_(weapon), collarbone, parted_lips |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, sex, vaginal, claws, nipples, open_mouth, solo_focus, ass, bar_censor, collar, cum_in_pussy, looking_at_viewer, nude, navel, o-ring_top, pink_ribbon, spread_legs, sweat, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | blush | claws | looking_at_viewer | o-ring_top | solo | belt_collar | pink_ribbon | smile | hair_bow | upper_body | parted_lips | simple_background | white_background | 1boy | cum_on_breasts | open_mouth | solo_focus | ejaculation | paizuri_under_clothes | gigantic_breasts | inverted_nipples | nude | pov | hetero | nipples | paizuri | breast_grab | grabbing | penis | mosaic_censoring | breasts_squeezed_together | sweat | collar | lactation | pov_crotch | claw_(weapon) | collarbone | sex | vaginal | ass | bar_censor | cum_in_pussy | navel | spread_legs | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:--------|:--------------------|:-------------|:-------|:--------------|:--------------|:--------|:-----------|:-------------|:--------------|:--------------------|:-------------------|:-------|:-----------------|:-------------|:-------------|:--------------|:------------------------|:-------------------|:-------------------|:-------|:------|:---------|:----------|:----------|:--------------|:-----------|:--------|:-------------------|:----------------------------|:--------|:---------|:------------|:-------------|:----------------|:-------------|:------|:----------|:------|:-------------|:---------------|:--------|:--------------|:-------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | | X | X | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | X | X | X | | | | X | | | | | | | X | X | X | X | X | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | | X | X | X | | X | | X | | | | X | | | | | | | | | X | | X | | | X | | | | | | | | | | | X | X | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | X | X | X | X | | | X | | | | | | | X | | X | X | | | | | X | | X | X | | | | X | | | X | X | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/passionlip_fgo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T14:19:08+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-12T09:30:54+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of passionlip/パッションリップ/Passionlip (Fate/Grand Order)
============================================================
This is the dataset of passionlip/パッションリップ/Passionlip (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are 'purple\_hair, breasts, long\_hair, ribbon, hair\_ribbon, huge\_breasts, very\_long\_hair, pink\_eyes, purple\_eyes, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
b9d1b6436382ba2360985e0fae1efcbfd95ffc7f
|
# Dataset of white (Pokémon)
This is the dataset of white (Pokémon), containing 103 images and their tags.
The core tags of this character are `brown_hair, long_hair, hat, baseball_cap, blue_eyes, ponytail, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 103 | 68.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/white_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 103 | 51.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/white_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 198 | 90.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/white_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 103 | 64.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/white_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 198 | 111.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/white_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/white_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 45 |  |  |  |  |  | 1girl, denim_shorts, solo, vest, short_shorts, wristband, ass, smile, looking_back, blue_shorts, blush, looking_at_viewer, simple_background, exposed_pocket, white_background |
| 1 | 9 |  |  |  |  |  | 1girl, hetero, blush, penis, sex_from_behind, solo_focus, nude, pussy, uncensored, 1boy, anal, doggystyle, nipples, sweat, 2boys, cum_in_ass, double_penetration, mmf_threesome, open_mouth, tongue |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | denim_shorts | solo | vest | short_shorts | wristband | ass | smile | looking_back | blue_shorts | blush | looking_at_viewer | simple_background | exposed_pocket | white_background | hetero | penis | sex_from_behind | solo_focus | nude | pussy | uncensored | 1boy | anal | doggystyle | nipples | sweat | 2boys | cum_in_ass | double_penetration | mmf_threesome | open_mouth | tongue |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:-------|:---------------|:------------|:------|:--------|:---------------|:--------------|:--------|:--------------------|:--------------------|:-----------------|:-------------------|:---------|:--------|:------------------|:-------------|:-------|:--------|:-------------|:-------|:-------|:-------------|:----------|:--------|:--------|:-------------|:---------------------|:----------------|:-------------|:---------|
| 0 | 45 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/white_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T14:19:40+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T21:24:36+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of white (Pokémon)
==========================
This is the dataset of white (Pokémon), containing 103 images and their tags.
The core tags of this character are 'brown\_hair, long\_hair, hat, baseball\_cap, blue\_eyes, ponytail, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
5353dc09b0871b1ef24f9781822538ccf764925d
|
# Dataset of Toyohama Nodoka
This is the dataset of Toyohama Nodoka, containing 119 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 119 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 290 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 324 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 119 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 119 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 119 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 290 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 290 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-eyes-640 | 324 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 324 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
CyberHarem/toyohama_nodoka_seishunbutayarou
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T14:29:44+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-26T08:33:31+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Toyohama Nodoka
==========================
This is the dataset of Toyohama Nodoka, containing 119 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
c1a43d2c3a52cf337e8f768198958b48ef175ebb
|
# Dataset Card for "babylm-10M-gutenberg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-10M-gutenberg
|
[
"region:us"
] |
2023-09-12T14:35:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6995971, "num_examples": 106624}, {"name": "valid", "num_bytes": 5158276, "num_examples": 80469}, {"name": "test", "num_bytes": 6995971, "num_examples": 106624}], "download_size": 12436522, "dataset_size": 19150218}}
|
2023-09-12T14:35:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-10M-gutenberg"
More Information needed
|
[
"# Dataset Card for \"babylm-10M-gutenberg\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-10M-gutenberg\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-10M-gutenberg\"\n\nMore Information needed"
] |
4b2e270a54ffe668024b97f7175b178aebf21fb7
|
# Dataset Card for CoQCat
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
CoQCat is a dataset for Conversational Question Answering in Catalan. It is based on [CoQA dataset](https://stanfordnlp.github.io/coqa/).
CoQCat comprises 89,364 question-answer pairs, sourced from conversations related to 6,000 text passages from six different domains.
The questions and responses are designed to maintain a conversational tone.
The answers are presented in a free-form text format, with evidence highlighted from the passage.
For the development and test sets, an additional 2 responses to each question have been collected.
This work is licensed under a [CC BY-NC-ND 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en_GB).
### Supported Tasks and Leaderboards
Conversational models, question answering.
### Languages
The dataset is in Catalan (ca-ES).
## Dataset Structure
### Data Instances
Three JSON files, one for each split.
An example of 'test' looks as follows:
```
{
"source": "petites_histories",
"id": "petites_histories_100",
"filename": "coqcat_batch1",
"title": "Superant la crisi dels 40",
"story": "“Què collons estic fent aquí” es preguntà, “…fotent bots i suant a raig als meus quaranta tacos en una classe de body-step. Si no he estat mai un amant del gimnàs! Tot això és culpa del club de ‘singles', els desaparellats desesperats per trobar algú que ens escolti una mica, mentre calculem les possibilitats de fotre un clau”. Pensant tot això es va descomptar i anava a contratemps, un altre cop.\nHo acaba de decidir: sortint de la classe es donarà de baixa. Sí, serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions.\nUn cop dutxat s'apropà al mostrador de l'entrada. El noi el va fer passar a l'oficina per omplir el full de baixa. Dins, una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut, després va ser el Jaume. Va mirar la dona que li somreia.:\n-“Estàs igual que fa vint-i-cinc anys! Has fet un pacte amb el diable?”\n-“… Alba?? Ets l'Alba?”- la seva cara li recordava a la de la noia grassoneta de la classe, però ara no ho estava pas de grassa! -“Tu sí que hi has pactat! Estàs molt millor… bé, vull dir que…”\n-“Sí, em vaig posar les piles.”\n-“Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig, just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà. Van xerrar, van riure, van escoltar relats i poemes, van reviure les aventures de l'institut amb música de jazz de fons, … Hi estava a gust, i s'adonà que, per primer cop en molt temps, no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva.",
"questions": [
{"input_text": "Està satisfet del que està fent?","turn_id": 1},
{"input_text": "Sua?","turn_id": 2},
{"input_text": "Quants anys té?","turn_id": 3},
{"input_text": "Li agrada anar al gimnàs?","turn_id": 4},
{"input_text": "Està solter?","turn_id": 5},
{"input_text": "Segueix bé el ritme de la música?","turn_id": 6},
{"input_text": "Continuarà apuntat al gimnàs?","turn_id": 7},
{"input_text": "Què farà els caps de setmana?","turn_id": 8},
{"input_text": "On li deien Txume?","turn_id": 9},
{"input_text": "Com l'anomenaven després?","turn_id": 10},
{"input_text": "En Jaume ha envellit bé?","turn_id": 11},
{"input_text": "L'Alba s'havia aprimat?","turn_id": 12},
{"input_text": "A què es dedica l'Alba?","turn_id": 13},
{"input_text": "On van anar plegats?","turn_id": 14},
{"input_text": "Volia que acabessin anant a la seva llar?","turn_id": 15}
],
"answers": [
{"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28,"turn_id": 1},
{"input_text": "Sí, a raig.", "span_text": "fotent bots i suant a raig", "span_start": 45, "span_end": 71,"turn_id": 2},
{"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95,"turn_id": 3},
{"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162,"turn_id": 4},
{"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles'", "span_start": 164, "span_end": 203,"turn_id": 5},
{"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399,"turn_id": 6},
{"input_text": "No, quan surti de la classe es donarà de baixa.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461,"turn_id": 7},
{"input_text": "Farà sortides amb el seu germà i els seus nebots.", "span_text": "dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions", "span_start": 503, "span_end": 659,"turn_id": 8},
{"input_text": "A l'institut.", "span_text": "Dins, una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 776, "span_end": 871,"turn_id": 9},
{"input_text": "Jaume.", "span_text": "que li deien així només a l'institut, després va ser el Jaume", "span_start": 835, "span_end": 896,"turn_id": 10},
{"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969,"turn_id": 11},
{"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131,"turn_id": 12},
{"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341,"turn_id": 13},
{"input_text": "A una sessió literària musical.", "span_text": "just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1343, "span_end": 1453,"turn_id": 14},
{"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715,"turn_id": 15}
],
"additional_answers": {
"0": [
{"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28, "turn_id": 1},
{"input_text": "Sí, molt.", "span_text": "fotent bots i suant a raig", "span_start": 45, "span_end": 71, "turn_id": 2},
{"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95, "turn_id": 3},
{"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162, "turn_id": 4},
{"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles'", "span_start": 164, "span_end": 203, "turn_id": 5},
{"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399, "turn_id": 6},
{"input_text": "No.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461, "turn_id": 7},
{"input_text": "Farà sortides amb el seu germà i els seus nebots.", "span_text": "serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions", "span_start": 467, "span_end": 659, "turn_id": 8},
{"input_text": "A l'institut.", "span_text": "“Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 804, "span_end": 871, "turn_id": 9},
{"input_text": "Jaume.", "span_text": "després va ser el Jaume", "span_start": 873, "span_end": 896, "turn_id": 10},
{"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969, "turn_id": 11},
{"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131, "turn_id": 12},
{"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341, "turn_id": 13},
{"input_text": "A una sessió literària musical.", "span_text": "just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1343, "span_end": 1453, "turn_id": 14},
{"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715, "turn_id": 15}
],
"1": [
{"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28, "turn_id": 1},
{"input_text": "Sí.", "span_text": "fotent bots i suant", "span_start": 45, "span_end": 64, "turn_id": 2},
{"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95, "turn_id": 3},
{"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162, "turn_id": 4},
{"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles', els desaparellats desesperats per trobar algú que ens escolti una mica", "span_start": 164, "span_end": 275, "turn_id": 5},
{"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399, "turn_id": 6},
{"input_text": "No.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461, "turn_id": 7},
{"input_text": "A fer sortides amb el seu germà.", "span_text": "serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà", "span_start": 467, "span_end": 586, "turn_id": 8},
{"input_text": "A l'institut.", "span_text": "una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 782, "span_end": 871, "turn_id": 9},
{"input_text": "Jaume.", "span_text": "després va ser el Jaume", "span_start": 873, "span_end": 896, "turn_id": 10},
{"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969, "turn_id": 11},
{"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131, "turn_id": 12},
{"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341, "turn_id": 13},
{"input_text": "A una sessió literària musical.", "span_text": "una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1369, "span_end": 1453, "turn_id": 14},
{"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715, "turn_id": 15}
]
}
}
```
### Data Fields
The data fields are the same among all splits.
- `source`: a `string` feature.
- `id`: a `string` feature.
- `filename`: a `string` feature.
- `story`: a `string` feature.
- `questions`: a `list` of dictionaries containing:
- `input_text`: a `string` feature.
- `turn_id`: a `int32` feature.
- `answers`: a `list` of dictionaries containing:
- `input_text`: a `string` feature.
- `span_text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `answer_end`: a `int32` feature.
- `turn_id`: a `int32` feature.
- `additional_answers` (only in `dev`and `test`): a dictionary feature containing:
- `0`: a `list` of dictionaries equal to `answers`.
- `1`: a `list` of dictionaries equal to `answers`.
### Data Splits
* dev.json: 8,909 question-answering examples. 600 text passages from 4 domains
* test.json: 8,986 question-answering examples. 600 text passages from 6 domains
* train.json: 71,489 question-answering examples. 4800 text passages from 4 domains
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
We obtained the initial text passages from different sources, depending on the domain:
| domain | source | train | dev | test | total |
|:-|:-|-:|-:|-:|-:|
| biographies | [Catalan wikipedia](ca.wikipedia.org) | 1200 | 150 | 100 | 1450|
| literature | [Gutenberg Project](gutenberg.org/) | 1200 | 150 | 100 | 1450|
| news | [VilaWeb](vilaweb.cat) | 1200 | 150 | 100 | 1450|
| mitology | Catalan wikipedia | 1200 | 150 | 100 | 1450|
| short histories | [Petites històries](https://petiteshistories.wordpress.com/) | 0 | 0 | 100 | 100|
| movie plots | Catalan wikipedia | 0 | 0 | 100 | 100 |
| TOTAL | | 4800 | 600 | 600 | 6000 |
#### Who are the source language producers?
The contents of [Catalan Wikipedia](ca.wikipedia.org) are developed by a team of volunteers, and are subject to review processes also carried out by volunteers.
The texts from [VilaWeb](vilaweb.cat) are prepared by journalists and communication professionals, and edited by expert proofreaders.
The paragraphes extracted from the [Gutenberg Project](gutenberg.org/) are written by diferent Catalan authors.
The texts of the [Petites històries website](https://petiteshistories.wordpress.com/) are written by the writer M. Carme Marí and published on her website. The author has provided them for the preparation of this dataset.
### Annotations
#### Annotation process
The annotation process was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process.
We asked to organize the annotators team in pairs: one of the members asking questions from the text passage and the other one answering them. For the elaboration of addicional answers for the dev and test splits, we asked the participation of a third person, not involved in the original conversation.
Annotation guidelines can be found in [Zenodo](https://zenodo.org/records/10362295).
#### Who are the annotators?
Annotation was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process.
We asked for a team of at least 4 annotators, students or graduates of universities, ideally in the field of Humanities or Social Sciences, with optimal demonstrable knowledge of the Catalan language (minimum level C1, or equivalent), and a senior coordinator with proven experience in management and coordination of text annotation.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a [CC BY-NC-ND 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en_GB).
### Citation Information
[DOI](https://zenodo.org/records/10362295)
### Contributions
The annotation was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process.
Thanks to [M. Carme Marí](https://petiteshistories.wordpress.com/quant-a/) and the [VilaWeb](vilaweb.cat) team for allowing us to use their texts. And also to all the [Catalan Wikipedia](ca.wikipedia.org) and [Gutenberg Project](gutenberg.org/) volunteers all their work.
|
projecte-aina/CoQCat
|
[
"task_categories:conversational",
"task_categories:question-answering",
"task_ids:dialogue-generation",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ca",
"license:cc-by-nc-nd-4.0",
"region:us"
] |
2023-09-12T14:42:48+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["ca"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["conversational", "question-answering"], "task_ids": ["dialogue-generation", "extractive-qa", "closed-domain-qa"], "pretty_name": "CoQCat - Conversational Question Answering in Catalan", "tags": []}
|
2023-12-12T13:27:41+00:00
|
[] |
[
"ca"
] |
TAGS
#task_categories-conversational #task_categories-question-answering #task_ids-dialogue-generation #task_ids-extractive-qa #task_ids-closed-domain-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Catalan #license-cc-by-nc-nd-4.0 #region-us
|
Dataset Card for CoQCat
=======================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact: langtech@URL
### Dataset Summary
CoQCat is a dataset for Conversational Question Answering in Catalan. It is based on CoQA dataset.
CoQCat comprises 89,364 question-answer pairs, sourced from conversations related to 6,000 text passages from six different domains.
The questions and responses are designed to maintain a conversational tone.
The answers are presented in a free-form text format, with evidence highlighted from the passage.
For the development and test sets, an additional 2 responses to each question have been collected.
This work is licensed under a CC BY-NC-ND 4.0 International License.
### Supported Tasks and Leaderboards
Conversational models, question answering.
### Languages
The dataset is in Catalan (ca-ES).
Dataset Structure
-----------------
### Data Instances
Three JSON files, one for each split.
An example of 'test' looks as follows:
### Data Fields
The data fields are the same among all splits.
* 'source': a 'string' feature.
* 'id': a 'string' feature.
* 'filename': a 'string' feature.
* 'story': a 'string' feature.
* 'questions': a 'list' of dictionaries containing:
+ 'input\_text': a 'string' feature.
+ 'turn\_id': a 'int32' feature.
* 'answers': a 'list' of dictionaries containing:
+ 'input\_text': a 'string' feature.
+ 'span\_text': a 'string' feature.
+ 'answer\_start': a 'int32' feature.
+ 'answer\_end': a 'int32' feature.
+ 'turn\_id': a 'int32' feature.
* 'additional\_answers' (only in 'dev'and 'test'): a dictionary feature containing:
+ '0': a 'list' of dictionaries equal to 'answers'.
+ '1': a 'list' of dictionaries equal to 'answers'.
### Data Splits
* URL: 8,909 question-answering examples. 600 text passages from 4 domains
* URL: 8,986 question-answering examples. 600 text passages from 6 domains
* URL: 71,489 question-answering examples. 4800 text passages from 4 domains
Dataset Creation
----------------
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
We obtained the initial text passages from different sources, depending on the domain:
#### Who are the source language producers?
The contents of Catalan Wikipedia are developed by a team of volunteers, and are subject to review processes also carried out by volunteers.
The texts from VilaWeb are prepared by journalists and communication professionals, and edited by expert proofreaders.
The paragraphes extracted from the Gutenberg Project are written by diferent Catalan authors.
The texts of the Petites històries website are written by the writer M. Carme Marí and published on her website. The author has provided them for the preparation of this dataset.
### Annotations
#### Annotation process
The annotation process was entrusted to the company M47 labs through a public tender process.
We asked to organize the annotators team in pairs: one of the members asking questions from the text passage and the other one answering them. For the elaboration of addicional answers for the dev and test splits, we asked the participation of a third person, not involved in the original conversation.
Annotation guidelines can be found in Zenodo.
#### Who are the annotators?
Annotation was entrusted to the company M47 labs through a public tender process.
We asked for a team of at least 4 annotators, students or graduates of universities, ideally in the field of Humanities or Social Sciences, with optimal demonstrable knowledge of the Catalan language (minimum level C1, or equivalent), and a senior coordinator with proven experience in management and coordination of text annotation.
### Personal and Sensitive Information
No personal or sensitive information included.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
We hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
Additional Information
----------------------
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)
This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.
### Licensing Information
This work is licensed under a CC BY-NC-ND 4.0 International License.
DOI
### Contributions
The annotation was entrusted to the company M47 labs through a public tender process.
Thanks to M. Carme Marí and the VilaWeb team for allowing us to use their texts. And also to all the Catalan Wikipedia and Gutenberg Project volunteers all their work.
|
[
"### Dataset Summary\n\n\nCoQCat is a dataset for Conversational Question Answering in Catalan. It is based on CoQA dataset.\n\n\nCoQCat comprises 89,364 question-answer pairs, sourced from conversations related to 6,000 text passages from six different domains.\nThe questions and responses are designed to maintain a conversational tone.\nThe answers are presented in a free-form text format, with evidence highlighted from the passage.\n\n\nFor the development and test sets, an additional 2 responses to each question have been collected.\n\n\nThis work is licensed under a CC BY-NC-ND 4.0 International License.",
"### Supported Tasks and Leaderboards\n\n\nConversational models, question answering.",
"### Languages\n\n\nThe dataset is in Catalan (ca-ES).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThree JSON files, one for each split.\n\n\nAn example of 'test' looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'source': a 'string' feature.\n* 'id': a 'string' feature.\n* 'filename': a 'string' feature.\n* 'story': a 'string' feature.\n* 'questions': a 'list' of dictionaries containing:\n\t+ 'input\\_text': a 'string' feature.\n\t+ 'turn\\_id': a 'int32' feature.\n* 'answers': a 'list' of dictionaries containing:\n\t+ 'input\\_text': a 'string' feature.\n\t+ 'span\\_text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'answer\\_end': a 'int32' feature.\n\t+ 'turn\\_id': a 'int32' feature.\n* 'additional\\_answers' (only in 'dev'and 'test'): a dictionary feature containing:\n\t+ '0': a 'list' of dictionaries equal to 'answers'.\n\t+ '1': a 'list' of dictionaries equal to 'answers'.",
"### Data Splits\n\n\n* URL: 8,909 question-answering examples. 600 text passages from 4 domains\n* URL: 8,986 question-answering examples. 600 text passages from 6 domains\n* URL: 71,489 question-answering examples. 4800 text passages from 4 domains\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nWe created this dataset to contribute to the development of language models in Catalan, a low-resource language.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nWe obtained the initial text passages from different sources, depending on the domain:",
"#### Who are the source language producers?\n\n\nThe contents of Catalan Wikipedia are developed by a team of volunteers, and are subject to review processes also carried out by volunteers.\n\n\nThe texts from VilaWeb are prepared by journalists and communication professionals, and edited by expert proofreaders.\n\n\nThe paragraphes extracted from the Gutenberg Project are written by diferent Catalan authors.\n\n\nThe texts of the Petites històries website are written by the writer M. Carme Marí and published on her website. The author has provided them for the preparation of this dataset.",
"### Annotations",
"#### Annotation process\n\n\nThe annotation process was entrusted to the company M47 labs through a public tender process.\n\n\nWe asked to organize the annotators team in pairs: one of the members asking questions from the text passage and the other one answering them. For the elaboration of addicional answers for the dev and test splits, we asked the participation of a third person, not involved in the original conversation.\n\n\nAnnotation guidelines can be found in Zenodo.",
"#### Who are the annotators?\n\n\nAnnotation was entrusted to the company M47 labs through a public tender process.\n\n\nWe asked for a team of at least 4 annotators, students or graduates of universities, ideally in the field of Humanities or Social Sciences, with optimal demonstrable knowledge of the Catalan language (minimum level C1, or equivalent), and a senior coordinator with proven experience in management and coordination of text annotation.",
"### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account.",
"### Discussion of Biases\n\n\n[N/A]",
"### Other Known Limitations\n\n\n[N/A]\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nLanguage Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)\n\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.",
"### Licensing Information\n\n\nThis work is licensed under a CC BY-NC-ND 4.0 International License.\n\n\nDOI",
"### Contributions\n\n\nThe annotation was entrusted to the company M47 labs through a public tender process.\n\n\nThanks to M. Carme Marí and the VilaWeb team for allowing us to use their texts. And also to all the Catalan Wikipedia and Gutenberg Project volunteers all their work."
] |
[
"TAGS\n#task_categories-conversational #task_categories-question-answering #task_ids-dialogue-generation #task_ids-extractive-qa #task_ids-closed-domain-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Catalan #license-cc-by-nc-nd-4.0 #region-us \n",
"### Dataset Summary\n\n\nCoQCat is a dataset for Conversational Question Answering in Catalan. It is based on CoQA dataset.\n\n\nCoQCat comprises 89,364 question-answer pairs, sourced from conversations related to 6,000 text passages from six different domains.\nThe questions and responses are designed to maintain a conversational tone.\nThe answers are presented in a free-form text format, with evidence highlighted from the passage.\n\n\nFor the development and test sets, an additional 2 responses to each question have been collected.\n\n\nThis work is licensed under a CC BY-NC-ND 4.0 International License.",
"### Supported Tasks and Leaderboards\n\n\nConversational models, question answering.",
"### Languages\n\n\nThe dataset is in Catalan (ca-ES).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThree JSON files, one for each split.\n\n\nAn example of 'test' looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'source': a 'string' feature.\n* 'id': a 'string' feature.\n* 'filename': a 'string' feature.\n* 'story': a 'string' feature.\n* 'questions': a 'list' of dictionaries containing:\n\t+ 'input\\_text': a 'string' feature.\n\t+ 'turn\\_id': a 'int32' feature.\n* 'answers': a 'list' of dictionaries containing:\n\t+ 'input\\_text': a 'string' feature.\n\t+ 'span\\_text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'answer\\_end': a 'int32' feature.\n\t+ 'turn\\_id': a 'int32' feature.\n* 'additional\\_answers' (only in 'dev'and 'test'): a dictionary feature containing:\n\t+ '0': a 'list' of dictionaries equal to 'answers'.\n\t+ '1': a 'list' of dictionaries equal to 'answers'.",
"### Data Splits\n\n\n* URL: 8,909 question-answering examples. 600 text passages from 4 domains\n* URL: 8,986 question-answering examples. 600 text passages from 6 domains\n* URL: 71,489 question-answering examples. 4800 text passages from 4 domains\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nWe created this dataset to contribute to the development of language models in Catalan, a low-resource language.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nWe obtained the initial text passages from different sources, depending on the domain:",
"#### Who are the source language producers?\n\n\nThe contents of Catalan Wikipedia are developed by a team of volunteers, and are subject to review processes also carried out by volunteers.\n\n\nThe texts from VilaWeb are prepared by journalists and communication professionals, and edited by expert proofreaders.\n\n\nThe paragraphes extracted from the Gutenberg Project are written by diferent Catalan authors.\n\n\nThe texts of the Petites històries website are written by the writer M. Carme Marí and published on her website. The author has provided them for the preparation of this dataset.",
"### Annotations",
"#### Annotation process\n\n\nThe annotation process was entrusted to the company M47 labs through a public tender process.\n\n\nWe asked to organize the annotators team in pairs: one of the members asking questions from the text passage and the other one answering them. For the elaboration of addicional answers for the dev and test splits, we asked the participation of a third person, not involved in the original conversation.\n\n\nAnnotation guidelines can be found in Zenodo.",
"#### Who are the annotators?\n\n\nAnnotation was entrusted to the company M47 labs through a public tender process.\n\n\nWe asked for a team of at least 4 annotators, students or graduates of universities, ideally in the field of Humanities or Social Sciences, with optimal demonstrable knowledge of the Catalan language (minimum level C1, or equivalent), and a senior coordinator with proven experience in management and coordination of text annotation.",
"### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account.",
"### Discussion of Biases\n\n\n[N/A]",
"### Other Known Limitations\n\n\n[N/A]\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nLanguage Technologies Unit at the Barcelona Supercomputing Center (langtech@URL)\n\n\nThis work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.",
"### Licensing Information\n\n\nThis work is licensed under a CC BY-NC-ND 4.0 International License.\n\n\nDOI",
"### Contributions\n\n\nThe annotation was entrusted to the company M47 labs through a public tender process.\n\n\nThanks to M. Carme Marí and the VilaWeb team for allowing us to use their texts. And also to all the Catalan Wikipedia and Gutenberg Project volunteers all their work."
] |
[
123,
140,
19,
22,
27,
278,
75,
30,
4,
28,
121,
5,
101,
98,
25,
34,
13,
19,
61,
25,
63
] |
[
"passage: TAGS\n#task_categories-conversational #task_categories-question-answering #task_ids-dialogue-generation #task_ids-extractive-qa #task_ids-closed-domain-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Catalan #license-cc-by-nc-nd-4.0 #region-us \n### Dataset Summary\n\n\nCoQCat is a dataset for Conversational Question Answering in Catalan. It is based on CoQA dataset.\n\n\nCoQCat comprises 89,364 question-answer pairs, sourced from conversations related to 6,000 text passages from six different domains.\nThe questions and responses are designed to maintain a conversational tone.\nThe answers are presented in a free-form text format, with evidence highlighted from the passage.\n\n\nFor the development and test sets, an additional 2 responses to each question have been collected.\n\n\nThis work is licensed under a CC BY-NC-ND 4.0 International License.### Supported Tasks and Leaderboards\n\n\nConversational models, question answering.### Languages\n\n\nThe dataset is in Catalan (ca-ES).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nThree JSON files, one for each split.\n\n\nAn example of 'test' looks as follows:",
"passage: ### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'source': a 'string' feature.\n* 'id': a 'string' feature.\n* 'filename': a 'string' feature.\n* 'story': a 'string' feature.\n* 'questions': a 'list' of dictionaries containing:\n\t+ 'input\\_text': a 'string' feature.\n\t+ 'turn\\_id': a 'int32' feature.\n* 'answers': a 'list' of dictionaries containing:\n\t+ 'input\\_text': a 'string' feature.\n\t+ 'span\\_text': a 'string' feature.\n\t+ 'answer\\_start': a 'int32' feature.\n\t+ 'answer\\_end': a 'int32' feature.\n\t+ 'turn\\_id': a 'int32' feature.\n* 'additional\\_answers' (only in 'dev'and 'test'): a dictionary feature containing:\n\t+ '0': a 'list' of dictionaries equal to 'answers'.\n\t+ '1': a 'list' of dictionaries equal to 'answers'.### Data Splits\n\n\n* URL: 8,909 question-answering examples. 600 text passages from 4 domains\n* URL: 8,986 question-answering examples. 600 text passages from 6 domains\n* URL: 71,489 question-answering examples. 4800 text passages from 4 domains\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nWe created this dataset to contribute to the development of language models in Catalan, a low-resource language.### Source Data#### Initial Data Collection and Normalization\n\n\nWe obtained the initial text passages from different sources, depending on the domain:#### Who are the source language producers?\n\n\nThe contents of Catalan Wikipedia are developed by a team of volunteers, and are subject to review processes also carried out by volunteers.\n\n\nThe texts from VilaWeb are prepared by journalists and communication professionals, and edited by expert proofreaders.\n\n\nThe paragraphes extracted from the Gutenberg Project are written by diferent Catalan authors.\n\n\nThe texts of the Petites històries website are written by the writer M. Carme Marí and published on her website. The author has provided them for the preparation of this dataset.### Annotations#### Annotation process\n\n\nThe annotation process was entrusted to the company M47 labs through a public tender process.\n\n\nWe asked to organize the annotators team in pairs: one of the members asking questions from the text passage and the other one answering them. For the elaboration of addicional answers for the dev and test splits, we asked the participation of a third person, not involved in the original conversation.\n\n\nAnnotation guidelines can be found in Zenodo.#### Who are the annotators?\n\n\nAnnotation was entrusted to the company M47 labs through a public tender process.\n\n\nWe asked for a team of at least 4 annotators, students or graduates of universities, ideally in the field of Humanities or Social Sciences, with optimal demonstrable knowledge of the Catalan language (minimum level C1, or equivalent), and a senior coordinator with proven experience in management and coordination of text annotation.### Personal and Sensitive Information\n\n\nNo personal or sensitive information included.\n\n\nConsiderations for Using the Data\n---------------------------------"
] |
4fb480909ab4f6ff144793bf52fc8a243e94ced3
|
# Dataset of Azusagawa Kaede
This is the dataset of Azusagawa Kaede, containing 177 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 177 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 411 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 177 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 177 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 177 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 177 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 177 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 411 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 411 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 411 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/azusagawa_kaede_seishunbutayarou
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T14:43:16+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:34:18+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Azusagawa Kaede
==========================
This is the dataset of Azusagawa Kaede, containing 177 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
51a290b6bd32d76e8472bc1affeefabc8dc8bb8a
|
# Lora Negev (LLaMA2) 09122023 test 1
* Dataset of Negev dialogue from Girls' Frontline
* Manually edited to turn into multi-turn dialogue
|
922-CA/lne2_09122023_test1
|
[
"license:openrail",
"region:us"
] |
2023-09-12T14:49:04+00:00
|
{"license": "openrail"}
|
2023-09-22T07:09:04+00:00
|
[] |
[] |
TAGS
#license-openrail #region-us
|
# Lora Negev (LLaMA2) 09122023 test 1
* Dataset of Negev dialogue from Girls' Frontline
* Manually edited to turn into multi-turn dialogue
|
[
"# Lora Negev (LLaMA2) 09122023 test 1\n* Dataset of Negev dialogue from Girls' Frontline\n* Manually edited to turn into multi-turn dialogue"
] |
[
"TAGS\n#license-openrail #region-us \n",
"# Lora Negev (LLaMA2) 09122023 test 1\n* Dataset of Negev dialogue from Girls' Frontline\n* Manually edited to turn into multi-turn dialogue"
] |
[
12,
43
] |
[
"passage: TAGS\n#license-openrail #region-us \n# Lora Negev (LLaMA2) 09122023 test 1\n* Dataset of Negev dialogue from Girls' Frontline\n* Manually edited to turn into multi-turn dialogue"
] |
e4283a5fd1de0f61e9996a51c9080cc96e849435
|
# Dataset Card for "babylm-10M-switchboard"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-10M-switchboard
|
[
"region:us"
] |
2023-09-12T14:51:23+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 823158, "num_examples": 20000}, {"name": "valid", "num_bytes": 724013, "num_examples": 18000}, {"name": "test", "num_bytes": 823158, "num_examples": 20000}], "download_size": 978960, "dataset_size": 2370329}}
|
2023-09-12T15:38:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-10M-switchboard"
More Information needed
|
[
"# Dataset Card for \"babylm-10M-switchboard\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-10M-switchboard\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-10M-switchboard\"\n\nMore Information needed"
] |
61853b2f81e1749b14ba4ea3af1544a832c503c1
|
# Dataset of Makinohara Shoko
This is the dataset of Makinohara Shoko, containing 120 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 120 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 283 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 120 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 120 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 120 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 120 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 120 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 283 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 283 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 283 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/makinohara_shoko_seishunbutayarou
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T14:53:51+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:34:20+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Makinohara Shoko
===========================
This is the dataset of Makinohara Shoko, containing 120 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
ba9a1afa42645289e90bfbc28a0aad3918304b2b
|
# Dataset Card for "guanaco-llama2-200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GianniCatBug/guanaco-llama2-200
|
[
"region:us"
] |
2023-09-12T15:00:00+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 338808, "num_examples": 200}], "download_size": 201257, "dataset_size": 338808}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-12T15:00:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-200"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
6fecb2ea0c3f41c2f1ba96e8bc0c7cead65e9690
|
# Dataset Card for Evaluation run of Fredithefish/Guanaco-13B-Uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Fredithefish/Guanaco-13B-Uncensored
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Fredithefish/Guanaco-13B-Uncensored](https://huggingface.co/Fredithefish/Guanaco-13B-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Fredithefish__Guanaco-13B-Uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T17:43:44.258144](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__Guanaco-13B-Uncensored/blob/main/results_2023-10-23T17-43-44.258144.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368403,
"f1": 0.06033871644295293,
"f1_stderr": 0.0013667724524043167,
"acc": 0.4270991094279287,
"acc_stderr": 0.009934457604610529
},
"harness|drop|3": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368403,
"f1": 0.06033871644295293,
"f1_stderr": 0.0013667724524043167
},
"harness|gsm8k|5": {
"acc": 0.09097801364670205,
"acc_stderr": 0.00792132284401367
},
"harness|winogrande|5": {
"acc": 0.7632202052091555,
"acc_stderr": 0.01194759236520739
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Fredithefish__Guanaco-13B-Uncensored
|
[
"region:us"
] |
2023-09-12T15:10:33+00:00
|
{"pretty_name": "Evaluation run of Fredithefish/Guanaco-13B-Uncensored", "dataset_summary": "Dataset automatically created during the evaluation run of model [Fredithefish/Guanaco-13B-Uncensored](https://huggingface.co/Fredithefish/Guanaco-13B-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Fredithefish__Guanaco-13B-Uncensored\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T17:43:44.258144](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__Guanaco-13B-Uncensored/blob/main/results_2023-10-23T17-43-44.258144.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368403,\n \"f1\": 0.06033871644295293,\n \"f1_stderr\": 0.0013667724524043167,\n \"acc\": 0.4270991094279287,\n \"acc_stderr\": 0.009934457604610529\n },\n \"harness|drop|3\": {\n \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368403,\n \"f1\": 0.06033871644295293,\n \"f1_stderr\": 0.0013667724524043167\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09097801364670205,\n \"acc_stderr\": 0.00792132284401367\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7632202052091555,\n \"acc_stderr\": 0.01194759236520739\n }\n}\n```", "repo_url": "https://huggingface.co/Fredithefish/Guanaco-13B-Uncensored", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T16_10_16.997512", "path": ["**/details_harness|arc:challenge|25_2023-09-12T16-10-16.997512.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T16-10-16.997512.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T17_43_44.258144", "path": ["**/details_harness|drop|3_2023-10-23T17-43-44.258144.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T17-43-44.258144.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T17_43_44.258144", "path": ["**/details_harness|gsm8k|5_2023-10-23T17-43-44.258144.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T17-43-44.258144.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T16_10_16.997512", "path": ["**/details_harness|hellaswag|10_2023-09-12T16-10-16.997512.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T16-10-16.997512.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T16_10_16.997512", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T16-10-16.997512.parquet", 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"**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-management|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-marketing|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T16-10-16.997512.parquet", 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"**/details_harness|hendrycksTest-security_studies|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-sociology|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-virology|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-world_religions|5_2023-09-12T16-10-16.997512.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T16-10-16.997512.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T16-10-16.997512.parquet", 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["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T16-10-16.997512.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T16-10-16.997512.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_12T16_10_16.997512", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T16-10-16.997512.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T16-10-16.997512.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_12T16_10_16.997512", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T16-10-16.997512.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T16-10-16.997512.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_12T16_10_16.997512", "path": 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["**/details_harness|truthfulqa:mc|0_2023-09-12T16-10-16.997512.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T16-10-16.997512.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_23T17_43_44.258144", "path": ["**/details_harness|winogrande|5_2023-10-23T17-43-44.258144.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-23T17-43-44.258144.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_12T16_10_16.997512", "path": ["results_2023-09-12T16-10-16.997512.parquet"]}, {"split": "2023_10_23T17_43_44.258144", "path": ["results_2023-10-23T17-43-44.258144.parquet"]}, {"split": "latest", "path": ["results_2023-10-23T17-43-44.258144.parquet"]}]}]}
|
2023-10-23T16:43:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Fredithefish/Guanaco-13B-Uncensored
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Fredithefish/Guanaco-13B-Uncensored on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T17:43:44.258144(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Fredithefish/Guanaco-13B-Uncensored",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Fredithefish/Guanaco-13B-Uncensored on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T17:43:44.258144(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Fredithefish/Guanaco-13B-Uncensored",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Fredithefish/Guanaco-13B-Uncensored on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T17:43:44.258144(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Fredithefish/Guanaco-13B-Uncensored## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Fredithefish/Guanaco-13B-Uncensored on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T17:43:44.258144(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
f66a494cd7dfeb9b19abc9aafdb47e5578453f4f
|
# Dataset of jougasaki_rika/城ヶ崎莉嘉 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of jougasaki_rika/城ヶ崎莉嘉 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `blonde_hair, long_hair, green_eyes, two_side_up, bangs, hair_ornament, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 644.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jougasaki_rika_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 382.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jougasaki_rika_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1251 | 845.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jougasaki_rika_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 570.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jougasaki_rika_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1251 | 1.16 GiB | [Download](https://huggingface.co/datasets/CyberHarem/jougasaki_rika_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/jougasaki_rika_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, navel, nipples, simple_background, small_breasts, solo, white_background, nude, blush, looking_at_viewer, smile, open_mouth |
| 1 | 13 |  |  |  |  |  | 1girl, solo, looking_at_viewer, navel, blush, open_mouth, side-tie_bikini_bottom, collarbone, simple_background, small_breasts, white_background, white_bikini, fang, micro_bikini, :d, choker, cowboy_shot, string_bikini |
| 2 | 12 |  |  |  |  |  | 1girl, blush, simple_background, solo, sweater_vest, upper_body, white_shirt, looking_at_viewer, red_ribbon, school_uniform, neck_ribbon, short_sleeves, white_background, collared_shirt, open_mouth, :d, hair_scrunchie, collarbone, fang |
| 3 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, school_uniform, simple_background, solo, sweater_vest, white_background, blush, bracelet, plaid_skirt, open_mouth, :d, short_sleeves, orange_skirt, pleated_skirt, white_shirt, fang, red_ribbon, neck_ribbon |
| 4 | 9 |  |  |  |  |  | 1girl, open_mouth, school_uniform, smile, solo, sweater_vest, ;d, one_eye_closed, looking_at_viewer, blush, fang, plaid_skirt, flower_bracelet, ribbon |
| 5 | 15 |  |  |  |  |  | 1girl, blush, earrings, looking_at_viewer, open_mouth, solo, puffy_short_sleeves, white_shirt, wrist_cuffs, plaid_skirt, white_headwear, red_skirt, yellow_necktie, frills, pleated_skirt, white_thighhighs, :d, beret, collared_shirt, fang, striped_necktie, simple_background, very_long_hair, white_jacket, bow, white_background, hand_up, heart, zettai_ryouiki |
| 6 | 5 |  |  |  |  |  | 1girl, blush, earrings, hair_bow, looking_at_viewer, solo, bare_shoulders, upper_body, heart, simple_background, bracelet, braid, grin, hairclip, nail_polish, necklace, one_eye_closed, white_background |
| 7 | 12 |  |  |  |  |  | 1girl, lion_ears, bare_shoulders, jingle_bell, paw_gloves, lion_tail, looking_at_viewer, blush, open_mouth, solo, collarbone, fangs, navel, small_breasts, lion_girl, midriff, short_shorts, striped_thighhighs, :d, cleavage, tail_bell, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | navel | nipples | simple_background | small_breasts | solo | white_background | nude | blush | looking_at_viewer | smile | open_mouth | side-tie_bikini_bottom | collarbone | white_bikini | fang | micro_bikini | :d | choker | cowboy_shot | string_bikini | sweater_vest | upper_body | white_shirt | red_ribbon | school_uniform | neck_ribbon | short_sleeves | collared_shirt | hair_scrunchie | bracelet | plaid_skirt | orange_skirt | pleated_skirt | ;d | one_eye_closed | flower_bracelet | ribbon | earrings | puffy_short_sleeves | wrist_cuffs | white_headwear | red_skirt | yellow_necktie | frills | white_thighhighs | beret | striped_necktie | very_long_hair | white_jacket | bow | hand_up | heart | zettai_ryouiki | hair_bow | bare_shoulders | braid | grin | hairclip | nail_polish | necklace | lion_ears | jingle_bell | paw_gloves | lion_tail | fangs | lion_girl | midriff | short_shorts | striped_thighhighs | cleavage | tail_bell |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------|:--------------------|:----------------|:-------|:-------------------|:-------|:--------|:--------------------|:--------|:-------------|:-------------------------|:-------------|:---------------|:-------|:---------------|:-----|:---------|:--------------|:----------------|:---------------|:-------------|:--------------|:-------------|:-----------------|:--------------|:----------------|:-----------------|:-----------------|:-----------|:--------------|:---------------|:----------------|:-----|:-----------------|:------------------|:---------|:-----------|:----------------------|:--------------|:-----------------|:------------|:-----------------|:---------|:-------------------|:--------|:------------------|:-----------------|:---------------|:------|:----------|:--------|:-----------------|:-----------|:-----------------|:--------|:-------|:-----------|:--------------|:-----------|:------------|:--------------|:-------------|:------------|:--------|:------------|:----------|:---------------|:---------------------|:-----------|:------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | | X | X | X | X | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | | | X | | X | X | | X | X | | X | | X | | X | | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | | | X | | X | X | | X | X | | X | | | | X | | X | | | | X | | X | X | X | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | | | X | | | X | X | X | X | | | | X | | | | | | X | | | | X | | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | | | X | | X | X | | X | X | | X | | | | X | | X | | | | | | X | | | | | X | | | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | X | | X | X | | X | X | | | | | | | | | | | | | X | | | | | | | | X | | | | | X | | | X | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | |
| 7 | 12 |  |  |  |  |  | X | X | | | X | X | X | | X | X | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/jougasaki_rika_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T15:10:34+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T12:30:22+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of jougasaki\_rika/城ヶ崎莉嘉 (THE iDOLM@STER: Cinderella Girls)
===================================================================
This is the dataset of jougasaki\_rika/城ヶ崎莉嘉 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, green\_eyes, two\_side\_up, bangs, hair\_ornament, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
542b1f577b62f7e0f6a8a0e74076e65c584f9bd7
|
# A New General Language Generation Evaluation Benchmark
This original dataset and instruction can be found [here](https://github.com/microsoft/glge)
To create a dataset and with specific version, you can simply run:
```commandline
git clone https://huggingface.co/datasets/shijli/glge
cd glge/data
bash preprocess.sh dataset version model
```
by replacing dataset version and model with your own parameters.
For example
```commandline
bash preprocess.sh cnndm easy prophetnet_en
```
will create the easy version of cnndm data using the prophetnet_en vocabulary file. Please rename and move the
vocabulary file into the ./vocab directory.
|
shijli/glge
|
[
"region:us"
] |
2023-09-12T15:10:36+00:00
|
{}
|
2023-09-15T05:22:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# A New General Language Generation Evaluation Benchmark
This original dataset and instruction can be found here
To create a dataset and with specific version, you can simply run:
by replacing dataset version and model with your own parameters.
For example
will create the easy version of cnndm data using the prophetnet_en vocabulary file. Please rename and move the
vocabulary file into the ./vocab directory.
|
[
"# A New General Language Generation Evaluation Benchmark\n\nThis original dataset and instruction can be found here\n\nTo create a dataset and with specific version, you can simply run:\n\n\n\nby replacing dataset version and model with your own parameters.\nFor example\n\n\n\nwill create the easy version of cnndm data using the prophetnet_en vocabulary file. Please rename and move the\nvocabulary file into the ./vocab directory."
] |
[
"TAGS\n#region-us \n",
"# A New General Language Generation Evaluation Benchmark\n\nThis original dataset and instruction can be found here\n\nTo create a dataset and with specific version, you can simply run:\n\n\n\nby replacing dataset version and model with your own parameters.\nFor example\n\n\n\nwill create the easy version of cnndm data using the prophetnet_en vocabulary file. Please rename and move the\nvocabulary file into the ./vocab directory."
] |
[
6,
95
] |
[
"passage: TAGS\n#region-us \n# A New General Language Generation Evaluation Benchmark\n\nThis original dataset and instruction can be found here\n\nTo create a dataset and with specific version, you can simply run:\n\n\n\nby replacing dataset version and model with your own parameters.\nFor example\n\n\n\nwill create the easy version of cnndm data using the prophetnet_en vocabulary file. Please rename and move the\nvocabulary file into the ./vocab directory."
] |
104b3683af067c454152a7fbba4c7eb68383ac1d
|
# Dataset Card for "test_result_large_synthesis_data_tunelm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/test_result_large_synthesis_data_tunelm
|
[
"region:us"
] |
2023-09-12T15:13:50+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "pred_str", "dtype": "string"}, {"name": "test_norm", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 208389, "num_examples": 1299}], "download_size": 109281, "dataset_size": 208389}}
|
2023-09-12T15:13:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test_result_large_synthesis_data_tunelm"
More Information needed
|
[
"# Dataset Card for \"test_result_large_synthesis_data_tunelm\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test_result_large_synthesis_data_tunelm\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"test_result_large_synthesis_data_tunelm\"\n\nMore Information needed"
] |
fa1754a3c4ae16722e40484f869f265baa572790
|
# Dataset Card for Evaluation run of zarakiquemparte/zararp-l2-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/zarakiquemparte/zararp-l2-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [zarakiquemparte/zararp-l2-7b](https://huggingface.co/zarakiquemparte/zararp-l2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_zarakiquemparte__zararp-l2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T19:07:03.309798](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zararp-l2-7b/blob/main/results_2023-10-24T19-07-03.309798.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.3148070469798658,
"em_stderr": 0.004756289906099083,
"f1": 0.3905547399328875,
"f1_stderr": 0.004648535168802013,
"acc": 0.38125227011207097,
"acc_stderr": 0.007927146918687346
},
"harness|drop|3": {
"em": 0.3148070469798658,
"em_stderr": 0.004756289906099083,
"f1": 0.3905547399328875,
"f1_stderr": 0.004648535168802013
},
"harness|gsm8k|5": {
"acc": 0.017437452615617893,
"acc_stderr": 0.003605486867998271
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_zarakiquemparte__zararp-l2-7b
|
[
"region:us"
] |
2023-09-12T15:17:54+00:00
|
{"pretty_name": "Evaluation run of zarakiquemparte/zararp-l2-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [zarakiquemparte/zararp-l2-7b](https://huggingface.co/zarakiquemparte/zararp-l2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_zarakiquemparte__zararp-l2-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T19:07:03.309798](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zararp-l2-7b/blob/main/results_2023-10-24T19-07-03.309798.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3148070469798658,\n \"em_stderr\": 0.004756289906099083,\n \"f1\": 0.3905547399328875,\n \"f1_stderr\": 0.004648535168802013,\n \"acc\": 0.38125227011207097,\n \"acc_stderr\": 0.007927146918687346\n },\n \"harness|drop|3\": {\n \"em\": 0.3148070469798658,\n \"em_stderr\": 0.004756289906099083,\n \"f1\": 0.3905547399328875,\n \"f1_stderr\": 0.004648535168802013\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.017437452615617893,\n \"acc_stderr\": 0.003605486867998271\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n }\n}\n```", "repo_url": "https://huggingface.co/zarakiquemparte/zararp-l2-7b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T16_17_38.915453", "path": ["**/details_harness|arc:challenge|25_2023-09-12T16-17-38.915453.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T16-17-38.915453.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T19_07_03.309798", "path": ["**/details_harness|drop|3_2023-10-24T19-07-03.309798.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T19-07-03.309798.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T19_07_03.309798", "path": ["**/details_harness|gsm8k|5_2023-10-24T19-07-03.309798.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T19-07-03.309798.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T16_17_38.915453", "path": ["**/details_harness|hellaswag|10_2023-09-12T16-17-38.915453.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T16-17-38.915453.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T16_17_38.915453", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T16-17-38.915453.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T16-17-38.915453.parquet", 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|
2023-10-24T18:07:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of zarakiquemparte/zararp-l2-7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model zarakiquemparte/zararp-l2-7b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T19:07:03.309798(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of zarakiquemparte/zararp-l2-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model zarakiquemparte/zararp-l2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T19:07:03.309798(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of zarakiquemparte/zararp-l2-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model zarakiquemparte/zararp-l2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T19:07:03.309798(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
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"### Data Splits",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of zarakiquemparte/zararp-l2-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model zarakiquemparte/zararp-l2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T19:07:03.309798(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
bdccf24b38d2a949fb44bb65d1d778448f84f03c
|
# Dataset Card for "squad_v2_1000_0.50_id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/squad_v2_1000_0.50_id
|
[
"region:us"
] |
2023-09-12T15:33:28+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 97308726.73032843, "num_examples": 55568}, {"name": "validation", "num_bytes": 1917601, "num_examples": 1000}], "download_size": 4274826, "dataset_size": 99226327.73032843}}
|
2023-09-12T15:42:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "squad_v2_1000_0.50_id"
More Information needed
|
[
"# Dataset Card for \"squad_v2_1000_0.50_id\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"squad_v2_1000_0.50_id\"\n\nMore Information needed"
] |
[
6,
22
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"squad_v2_1000_0.50_id\"\n\nMore Information needed"
] |
32af984d968394bef74d1885765c6000dc3a3f07
|
# Dataset Card for Evaluation run of lu-vae/llama2-13b-sharegpt4-test
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [lu-vae/llama2-13b-sharegpt4-test](https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T11:44:44.255542](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test/blob/main/results_2023-10-25T11-44-44.255542.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002202181208053691,
"em_stderr": 0.00048005108166192744,
"f1": 0.06610843120805385,
"f1_stderr": 0.0014639889801036593,
"acc": 0.44600618846762125,
"acc_stderr": 0.01064352054021588
},
"harness|drop|3": {
"em": 0.002202181208053691,
"em_stderr": 0.00048005108166192744,
"f1": 0.06610843120805385,
"f1_stderr": 0.0014639889801036593
},
"harness|gsm8k|5": {
"acc": 0.13115996967399546,
"acc_stderr": 0.00929849923558786
},
"harness|winogrande|5": {
"acc": 0.760852407261247,
"acc_stderr": 0.011988541844843902
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test
|
[
"region:us"
] |
2023-09-12T15:41:42+00:00
|
{"pretty_name": "Evaluation run of lu-vae/llama2-13b-sharegpt4-test", "dataset_summary": "Dataset automatically created during the evaluation run of model [lu-vae/llama2-13b-sharegpt4-test](https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T11:44:44.255542](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13b-sharegpt4-test/blob/main/results_2023-10-25T11-44-44.255542.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002202181208053691,\n \"em_stderr\": 0.00048005108166192744,\n \"f1\": 0.06610843120805385,\n \"f1_stderr\": 0.0014639889801036593,\n \"acc\": 0.44600618846762125,\n \"acc_stderr\": 0.01064352054021588\n },\n \"harness|drop|3\": {\n \"em\": 0.002202181208053691,\n \"em_stderr\": 0.00048005108166192744,\n \"f1\": 0.06610843120805385,\n \"f1_stderr\": 0.0014639889801036593\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13115996967399546,\n \"acc_stderr\": 0.00929849923558786\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.760852407261247,\n \"acc_stderr\": 0.011988541844843902\n }\n}\n```", "repo_url": "https://huggingface.co/lu-vae/llama2-13b-sharegpt4-test", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T16_41_26.998548", "path": ["**/details_harness|arc:challenge|25_2023-09-12T16-41-26.998548.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T16-41-26.998548.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T11_44_44.255542", "path": ["**/details_harness|drop|3_2023-10-25T11-44-44.255542.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T11-44-44.255542.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T11_44_44.255542", "path": ["**/details_harness|gsm8k|5_2023-10-25T11-44-44.255542.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T11-44-44.255542.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T16_41_26.998548", "path": ["**/details_harness|hellaswag|10_2023-09-12T16-41-26.998548.parquet"]}, {"split": "latest", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T16-41-26.998548.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T16-41-26.998548.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_12T16_41_26.998548", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T16-41-26.998548.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T16-41-26.998548.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_12T16_41_26.998548", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T16-41-26.998548.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T16-41-26.998548.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_12T16_41_26.998548", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T16-41-26.998548.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T16-41-26.998548.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_25T11_44_44.255542", "path": ["**/details_harness|winogrande|5_2023-10-25T11-44-44.255542.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-25T11-44-44.255542.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_12T16_41_26.998548", "path": ["results_2023-09-12T16-41-26.998548.parquet"]}, {"split": "2023_10_25T11_44_44.255542", "path": ["results_2023-10-25T11-44-44.255542.parquet"]}, {"split": "latest", "path": ["results_2023-10-25T11-44-44.255542.parquet"]}]}]}
|
2023-10-25T10:44:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of lu-vae/llama2-13b-sharegpt4-test
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model lu-vae/llama2-13b-sharegpt4-test on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T11:44:44.255542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of lu-vae/llama2-13b-sharegpt4-test",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13b-sharegpt4-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T11:44:44.255542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of lu-vae/llama2-13b-sharegpt4-test",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13b-sharegpt4-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T11:44:44.255542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of lu-vae/llama2-13b-sharegpt4-test## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13b-sharegpt4-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T11:44:44.255542(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
09575870559b00dd66e7ab8040bd13cfd151c006
|
# Dataset Card for "squad_v2_1000_0.00_id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/squad_v2_1000_0.00_id
|
[
"region:us"
] |
2023-09-12T15:43:17+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 65604584.71133232, "num_examples": 37056}, {"name": "validation", "num_bytes": 1920159, "num_examples": 1000}], "download_size": 0, "dataset_size": 67524743.71133232}}
|
2023-09-12T16:08:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "squad_v2_1000_0.00_id"
More Information needed
|
[
"# Dataset Card for \"squad_v2_1000_0.00_id\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"squad_v2_1000_0.00_id\"\n\nMore Information needed"
] |
[
6,
21
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"squad_v2_1000_0.00_id\"\n\nMore Information needed"
] |
dd424a368912702683813755972f20a84adabdf7
|
# Dataset of ibuki/イブキ (Pokémon)
This is the dataset of ibuki/イブキ (Pokémon), containing 500 images and their tags.
The core tags of this character are `blue_hair, long_hair, blue_eyes, ponytail, breasts, earrings, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 428.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 274.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1060 | 531.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 393.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1060 | 695.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ibuki_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ibuki_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, jewelry, bodysuit, choker, blush, solo, cape, gloves, smile |
| 1 | 8 |  |  |  |  |  | 1girl, bodysuit, cape, gloves, jewelry, boots, high_heels, solo, full_body, blue_footwear, pokemon_(creature) |
| 2 | 9 |  |  |  |  |  | 1girl, jewelry, blue_bodysuit, hair_between_eyes, solo, white_background, blue_gloves, closed_mouth, hand_on_hip, simple_background, black_cape, looking_at_viewer, smile, bangs, covered_navel, eyelashes |
| 3 | 5 |  |  |  |  |  | 1girl, bangs, black_cape, blue_bodysuit, blue_footwear, eyelashes, hair_between_eyes, jewelry, pokemon_(creature), blue_gloves, boots, closed_mouth, looking_at_viewer, smile, black_choker, full_body, covered_navel, floating_cape, hair_tie, hand_on_hip, skin_tight, standing |
| 4 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, open_mouth, penis, pussy, sex, solo_focus, vaginal, jewelry, bodysuit, cape, completely_nude, cum, gloves, navel, one_eye_closed, testicles, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jewelry | bodysuit | choker | blush | solo | cape | gloves | smile | boots | high_heels | full_body | blue_footwear | pokemon_(creature) | blue_bodysuit | hair_between_eyes | white_background | blue_gloves | closed_mouth | hand_on_hip | simple_background | black_cape | looking_at_viewer | bangs | covered_navel | eyelashes | black_choker | floating_cape | hair_tie | skin_tight | standing | 1boy | hetero | nipples | open_mouth | penis | pussy | sex | solo_focus | vaginal | completely_nude | cum | navel | one_eye_closed | testicles | uncensored |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:-----------|:---------|:--------|:-------|:-------|:---------|:--------|:--------|:-------------|:------------|:----------------|:---------------------|:----------------|:--------------------|:-------------------|:--------------|:---------------|:--------------|:--------------------|:-------------|:--------------------|:--------|:----------------|:------------|:---------------|:----------------|:-----------|:-------------|:-----------|:-------|:---------|:----------|:-------------|:--------|:--------|:------|:-------------|:----------|:------------------|:------|:--------|:-----------------|:------------|:-------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | | | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | | | | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | | | | | | | X | X | | X | X | X | X | X | | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | X | X | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/ibuki_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T15:43:40+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T22:27:02+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of ibuki/イブキ (Pokémon)
==============================
This is the dataset of ibuki/イブキ (Pokémon), containing 500 images and their tags.
The core tags of this character are 'blue\_hair, long\_hair, blue\_eyes, ponytail, breasts, earrings, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
85daef7fff979635c9f750680b73711981da9154
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
Feanix/sms_convos
|
[
"region:us"
] |
2023-09-12T16:08:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "2893149136", "path": "data/2893149136.parquet"}, {"split": "4162702577", "path": "data/4162702577.parquet"}, {"split": "4162774414", "path": "data/4162774414.parquet"}, {"split": "4164173989", "path": "data/4164173989.parquet"}, {"split": "4164736343", "path": "data/4164736343.parquet"}, {"split": "4165272818", "path": "data/4165272818.parquet"}, {"split": "4165284840", "path": "data/4165284840.parquet"}, {"split": "4165796634", "path": "data/4165796634.parquet"}, {"split": "4167054500", "path": "data/4167054500.parquet"}, {"split": "4168035459", "path": "data/4168035459.parquet"}, {"split": "4168207224", "path": "data/4168207224.parquet"}, {"split": "4168982667", "path": "data/4168982667.parquet"}, {"split": "4376844772", "path": "data/4376844772.parquet"}, {"split": "5148067528", "path": "data/5148067528.parquet"}, {"split": "5192007528", "path": "data/5192007528.parquet"}, {"split": "6473036801", "path": "data/6473036801.parquet"}, {"split": "6473852319", "path": "data/6473852319.parquet"}, {"split": "6474051995", "path": "data/6474051995.parquet"}, {"split": "6474084977", "path": "data/6474084977.parquet"}, {"split": "6474462582", "path": "data/6474462582.parquet"}, {"split": "6474827838", "path": "data/6474827838.parquet"}, {"split": "6475240601", "path": "data/6475240601.parquet"}, {"split": "6475299135", "path": "data/6475299135.parquet"}, {"split": "6475677019", "path": "data/6475677019.parquet"}, {"split": "6475692539", "path": "data/6475692539.parquet"}, {"split": "6476222943", "path": "data/6476222943.parquet"}, {"split": "6476946326", "path": "data/6476946326.parquet"}, {"split": "6477176145", "path": "data/6477176145.parquet"}, {"split": "6478245826", "path": "data/6478245826.parquet"}, {"split": "6478385496", "path": "data/6478385496.parquet"}, {"split": "6478614240", "path": "data/6478614240.parquet"}, {"split": "6478618498", "path": "data/6478618498.parquet"}, {"split": "6478845216", "path": "data/6478845216.parquet"}, {"split": "6479065591", "path": "data/6479065591.parquet"}, {"split": "6479168193", "path": "data/6479168193.parquet"}, {"split": "6479289430", "path": "data/6479289430.parquet"}, {"split": "6479690125", "path": "data/6479690125.parquet"}, {"split": "6479933258", "path": "data/6479933258.parquet"}]}]}
|
2023-09-12T17:03:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
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"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"#### Annotation process",
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"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
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"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
8,
24,
32,
10,
4,
6,
6,
5,
5,
5,
7,
4,
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9,
8,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
d35f4e78fcc0395a7fde8a6f2f244a9447b75287
|
# Dataset Card for "scireviewgen-csv"
This is a dataset built from the CSV export of the SciReviewGen dataset [found here](https://github.com/tetsu9923/SciReviewGen) (see "summarization_csv" for the original download link).
|
kdercksen/scireviewgen-csv
|
[
"region:us"
] |
2023-09-12T16:11:26+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "reference", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1017206768, "num_examples": 84705}, {"name": "validation", "num_bytes": 52660512, "num_examples": 4410}, {"name": "test", "num_bytes": 54202617, "num_examples": 4457}], "download_size": 507459864, "dataset_size": 1124069897}}
|
2023-09-12T16:18:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "scireviewgen-csv"
This is a dataset built from the CSV export of the SciReviewGen dataset found here (see "summarization_csv" for the original download link).
|
[
"# Dataset Card for \"scireviewgen-csv\"\n\nThis is a dataset built from the CSV export of the SciReviewGen dataset found here (see \"summarization_csv\" for the original download link)."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"scireviewgen-csv\"\n\nThis is a dataset built from the CSV export of the SciReviewGen dataset found here (see \"summarization_csv\" for the original download link)."
] |
[
6,
50
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"scireviewgen-csv\"\n\nThis is a dataset built from the CSV export of the SciReviewGen dataset found here (see \"summarization_csv\" for the original download link)."
] |
d74521fe45d2ec1ba0f69bd164adce62afde67ae
|
# Dataset Card for Evaluation run of tiiuae/falcon-7b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-7b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 6 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_tiiuae__falcon-7b-instruct",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:01:45.204079](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-7b-instruct/blob/main/results_2023-12-03T18-01-45.204079.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.04700530705079606,
"acc_stderr": 0.005829898355937184
},
"harness|gsm8k|5": {
"acc": 0.04700530705079606,
"acc_stderr": 0.005829898355937184
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_tiiuae__falcon-7b-instruct
|
[
"region:us"
] |
2023-09-12T16:11:45+00:00
|
{"pretty_name": "Evaluation run of tiiuae/falcon-7b-instruct", "dataset_summary": "Dataset automatically created during the evaluation run of model [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 6 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_tiiuae__falcon-7b-instruct\",\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-03T18:01:45.204079](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-7b-instruct/blob/main/results_2023-12-03T18-01-45.204079.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.04700530705079606,\n \"acc_stderr\": 0.005829898355937184\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04700530705079606,\n \"acc_stderr\": 0.005829898355937184\n }\n}\n```", "repo_url": "https://huggingface.co/tiiuae/falcon-7b-instruct", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T17_11_33.167282", "path": ["**/details_harness|arc:challenge|25_2023-09-12T17-11-33.167282.parquet"]}, {"split": "2023_10_03T22_10_35.400219", "path": ["**/details_harness|arc:challenge|25_2023-10-03T22-10-35.400219.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-10-03T22-10-35.400219.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T19_58_40.365010", 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["results_2023-12-03T18-01-19.868958.parquet"]}, {"split": "2023_12_03T18_01_45.204079", "path": ["results_2023-12-03T18-01-45.204079.parquet"]}, {"split": "latest", "path": ["results_2023-12-03T18-01-45.204079.parquet"]}]}]}
|
2023-12-03T18:01:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of tiiuae/falcon-7b-instruct
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model tiiuae/falcon-7b-instruct on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 6 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-03T18:01:45.204079(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of tiiuae/falcon-7b-instruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model tiiuae/falcon-7b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 6 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T18:01:45.204079(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of tiiuae/falcon-7b-instruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model tiiuae/falcon-7b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 6 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T18:01:45.204079(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of tiiuae/falcon-7b-instruct## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model tiiuae/falcon-7b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 6 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-03T18:01:45.204079(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
0efba9b66de4dc016d37ad51d00c168b9a71bd5e
|
# Dataset of wu_zetian/武則天/武则天 (Fate/Grand Order)
This is the dataset of wu_zetian/武則天/武则天 (Fate/Grand Order), containing 487 images and their tags.
The core tags of this character are `long_hair, purple_hair, purple_eyes, very_long_hair, bangs, hair_ornament, breasts, parted_bangs, twintails, scrunchie, small_breasts, sidelocks, hair_scrunchie, bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 487 | 584.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wu_zetian_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 487 | 364.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wu_zetian_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1201 | 768.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wu_zetian_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 487 | 530.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wu_zetian_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1201 | 1.00 GiB | [Download](https://huggingface.co/datasets/CyberHarem/wu_zetian_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/wu_zetian_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, hanfu, looking_at_viewer, shawl, solo, blush, long_sleeves, purple_dress, sash, wide_sleeves, collarbone, forehead, simple_background, white_background, yellow_scrunchie, open_mouth, pelvic_curtain, :d, bare_shoulders |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, blush, crown, earrings, fang, hanfu, long_sleeves, looking_at_viewer, navel, open_mouth, solo, string_bra, wide_sleeves, collarbone, forehead_mark, hat, ribbon, shawl, bow_bra, :d, black_gloves, pelvic_curtain, tassel |
| 2 | 6 |  |  |  |  |  | 1girl, bow_bra, crown, earrings, forehead_mark, hanfu, looking_at_viewer, navel, pelvic_curtain, shawl, smile, solo, string_bra, wide_sleeves, blush, hat, ribbon, loincloth, long_sleeves, purple_gloves, open_mouth, revealing_clothes, simple_background, sitting |
| 3 | 11 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, collarbone, forehead, hanfu, penis, looking_at_viewer, loli, long_sleeves, nipples, tongue_out, wide_sleeves, shawl, censored, open_mouth, pov, smile, sweat, yellow_scrunchie, erection, saliva, heart, oral, sash |
| 4 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, bare_shoulders, forehead, ponytail, black_dress, earrings, blush, flower, hair_bow, white_pantyhose, wrist_scrunchie |
| 5 | 11 |  |  |  |  |  | 1girl, blush, nipples, open_mouth, hetero, navel, penis, solo_focus, vaginal, 1boy, cum_in_pussy, loli, spread_legs, forehead, nude, collarbone, mosaic_censoring, saliva, tears, flat_chest, shiny_hair, sweat, hanfu, open_clothes, sex_from_behind, shiny_skin, straddling, tongue_out, yellow_scrunchie |
| 6 | 26 |  |  |  |  |  | 1girl, black_bikini, smile, looking_at_viewer, solo, bare_shoulders, cleavage, large_breasts, thighs, ahoge, black_gloves, forehead_mark, hair_flower, collarbone, fur-trimmed_cape, navel, black_cape, half_gloves, jewelry, ponytail, blush |
| 7 | 25 |  |  |  |  |  | 1girl, looking_at_viewer, smile, side_ponytail, cleavage, large_breasts, solo, wrist_scrunchie, bare_shoulders, black_one-piece_swimsuit, hair_between_eyes, highleg_swimsuit, thighs, earrings, open_mouth, blush, navel, thighlet |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hanfu | looking_at_viewer | shawl | solo | blush | long_sleeves | purple_dress | sash | wide_sleeves | collarbone | forehead | simple_background | white_background | yellow_scrunchie | open_mouth | pelvic_curtain | :d | bare_shoulders | crown | earrings | fang | navel | string_bra | forehead_mark | hat | ribbon | bow_bra | black_gloves | tassel | smile | loincloth | purple_gloves | revealing_clothes | sitting | 1boy | hetero | solo_focus | penis | loli | nipples | tongue_out | censored | pov | sweat | erection | saliva | heart | oral | ponytail | black_dress | flower | hair_bow | white_pantyhose | wrist_scrunchie | vaginal | cum_in_pussy | spread_legs | nude | mosaic_censoring | tears | flat_chest | shiny_hair | open_clothes | sex_from_behind | shiny_skin | straddling | black_bikini | cleavage | large_breasts | thighs | ahoge | hair_flower | fur-trimmed_cape | black_cape | half_gloves | jewelry | side_ponytail | black_one-piece_swimsuit | hair_between_eyes | highleg_swimsuit | thighlet |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:--------|:-------|:--------|:---------------|:---------------|:-------|:---------------|:-------------|:-----------|:--------------------|:-------------------|:-------------------|:-------------|:-----------------|:-----|:-----------------|:--------|:-----------|:-------|:--------|:-------------|:----------------|:------|:---------|:----------|:---------------|:---------|:--------|:------------|:----------------|:--------------------|:----------|:-------|:---------|:-------------|:--------|:-------|:----------|:-------------|:-----------|:------|:--------|:-----------|:---------|:--------|:-------|:-----------|:--------------|:---------|:-----------|:------------------|:------------------|:----------|:---------------|:--------------|:-------|:-------------------|:--------|:-------------|:-------------|:---------------|:------------------|:-------------|:-------------|:---------------|:-----------|:----------------|:---------|:--------|:--------------|:-------------------|:-------------|:--------------|:----------|:----------------|:---------------------------|:--------------------|:-------------------|:-----------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | | | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | | | X | | | X | | | X | X | | | X | X | | X | X | X | X | X | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | X | X | | X | X | | X | X | X | X | | | X | X | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | X | | X | X | | | | | | X | | | | | | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | X | | | | X | | | | | X | X | | | X | X | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | | | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 6 | 26 |  |  |  |  |  | X | | X | | X | X | | | | | X | | | | | | | | X | | | | X | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | |
| 7 | 25 |  |  |  |  |  | X | | X | | X | X | | | | | | | | | | X | | | X | | X | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | | | | | | | X | X | X | X | X |
|
CyberHarem/wu_zetian_fgo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T16:14:40+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-12T15:20:27+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of wu\_zetian/武則天/武则天 (Fate/Grand Order)
================================================
This is the dataset of wu\_zetian/武則天/武则天 (Fate/Grand Order), containing 487 images and their tags.
The core tags of this character are 'long\_hair, purple\_hair, purple\_eyes, very\_long\_hair, bangs, hair\_ornament, breasts, parted\_bangs, twintails, scrunchie, small\_breasts, sidelocks, hair\_scrunchie, bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
926fd614345b59757886f0deeda87e1f07638dd2
|
<a href="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases.png"><img src="https://huggingface.co/datasets/harvard-lil/cold-cases/resolve/main/coldcases-banner.webp"/></a>
# Collaborative Open Legal Data (COLD) - Cases
COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as compressed parquet files. If you'd like to view a sample of the dataset formatted as JSON Lines, you can view one [here](https://raw.githubusercontent.com/harvard-lil/cold-cases-export/main/sample.jsonl)
This dataset exists to support the open legal movement exemplified by projects like
[Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law) and
[LegalBench](https://hazyresearch.stanford.edu/legalbench/).
A key input to legal understanding projects is caselaw -- the published, precedential decisions of judges deciding legal disputes and explaining their reasoning.
United States caselaw is collected and published as open data by [CourtListener](https://www.courtlistener.com/), which maintains scrapers to aggregate data from
a wide range of public sources.
COLD Cases reformats CourtListener's [bulk data](https://www.courtlistener.com/help/api/bulk-data) so that all of the semantic information about each legal decision
(the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single record per decision,
with extraneous data removed. Serving in the traditional role of libraries as a standardization steward, the Harvard Library Innovation Lab is maintaining
this [open source](https://github.com/harvard-lil/cold-cases-export) pipeline to consolidate the data engineering for preprocessing caselaw so downstream machine
learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks.
Prepared by the [Harvard Library Innovation Lab](https://lil.law.harvard.edu) in collaboration with the [Free Law Project](https://free.law/).
---
## Links
- [Data nutrition label](https://datanutrition.org/labels/v3/?id=c29976b2-858c-4f4e-b7d0-c8ef12ce7dbe) (DRAFT). ([Archive](https://perma.cc/YV5P-B8JL)).
- [Pipeline source code](https://github.com/harvard-lil/cold-cases-export)
---
## Summary
- [Formats](#formats)
- [File structure](#file-structure)
- [Data dictionary](#data-dictionary)
- [Notes on appropriate use](#appropriate-use)
---
## Format
[Apache Parquet](https://parquet.apache.org/) is binary format that makes filtering and retrieving the data quicker because it lays out the data in columns, which means columns that are unnecessary to satisfy a given query or workflow don't need to be read. Hugging Face's [Datasets](https://huggingface.co/docs/datasets/index) library is an easy way to get started working with the entire dataset, and has features for loading and streaming the data, so you don't need to store it all locally or pay attention to how it's formatted on disk.
[☝️ Go back to Summary](#summary)
---
## Data dictionary
Partial glossary of the fields in the data.
| Field name | Description |
| --- | --- |
| `judges` | Names of judges presiding over the case, extracted from the text. |
| `date_filed` | Date the case was filed. Formatted in ISO Date format. |
| `date_filed_is_approximate` | Boolean representing whether the `date_filed` value is precise to the day. |
| `slug` | Short, human-readable unique string nickname for the case. |
| `case_name_short` | Short name for the case. |
| `case_name` | Fuller name for the case. |
| `case_name_full` | Full, formal name for the case. |
| `attorneys` | Names of attorneys arguing the case, extracted from the text. |
| `nature_of_suit` | Free text representinng type of suit, such as Civil, Tort, etc. |
| `syllabus` | Summary of the questions addressed in the decision, if provided by the reporter of decisions. |
| `headnotes` | Textual headnotes of the case |
| `summary` | Textual summary of the case |
| `disposition` | How the court disposed of the case in their final ruling. |
| `history` | Textual information about what happened to this case in later decisions. |
| `other_dates` | Other dates related to the case in free text. |
| `cross_reference` | Citations to related cases. |
| `citation_count` | Number of cases that cite this one. |
| `precedential_status` | Constrainted to the values "Published", "Unknown", "Errata", "Unpublished", "Relating-to", "Separate", "In-chambers" |
| `citations` | Cases that cite this case. |
| `court_short_name` | Short name of court presiding over case. |
| `court_full_name` | Full name of court presiding over case. |
| `court_jurisdiction` | Code for type of court that presided over the case. See: [court_jurisdiction field values](#court_jurisdiction-field-values) |
| `opinions` | An array of subrecords. |
| `opinions.author_str` | Name of the author of an individual opinion. |
| `opinions.per_curiam` | Boolean representing whether the opinion was delivered by an entire court or a single judge. |
| `opinions.type` | One of `"010combined"`, `"015unamimous"`, `"020lead"`, `"025plurality"`, `"030concurrence"`, `"035concurrenceinpart"`, `"040dissent"`, `"050addendum"`, `"060remittitur"`, `"070rehearing"`, `"080onthemerits"`, `"090onmotiontostrike"`. |
| `opinions.opinion_text` | Actual full text of the opinion. |
| `opinions.ocr` | Whether the opinion was captured via optical character recognition or born-digital text. |
### court_type field values
| Value | Description |
| --- | --- |
| F | Federal Appellate |
| FD | Federal District |
| FB | Federal Bankruptcy |
| FBP | Federal Bankruptcy Panel |
| FS | Federal Special |
| S | State Supreme |
| SA | State Appellate |
| ST | State Trial |
| SS | State Special |
| TRS | Tribal Supreme |
| TRA | Tribal Appellate |
| TRT | Tribal Trial |
| TRX | Tribal Special |
| TS | Territory Supreme |
| TA | Territory Appellate |
| TT | Territory Trial |
| TSP | Territory Special |
| SAG | State Attorney General |
| MA | Military Appellate |
| MT | Military Trial |
| C | Committee |
| I | International |
| T | Testing |
[☝️ Go back to Summary](#summary)
## Notes on appropriate use
When using this data, please keep in mind:
* All documents in this dataset are public information, published by courts within the United States to inform the public about the law. **You have a right to access them.**
* Nevertheless, **public court decisions frequently contain statements about individuals that are not true**. Court decisions often contain claims that are disputed,
or false claims taken as true based on a legal technicality, or claims taken as true but later found to be false. Legal decisions are designed to inform you about the law -- they are not
designed to inform you about individuals, and should not be used in place of credit databases, criminal records databases, news articles, or other sources intended
to provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals.
* **Court decisions are not up-to-date statements of law**. Each decision provides a given judge's best understanding of the law as applied to the stated facts
at the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context --
the skill typically provided by lawyers -- rather than simple data retrieval.
To mitigate privacy risks, we have filtered out cases [blocked or deindexed by CourtListener](https://www.courtlistener.com/terms/#removal). Researchers who
require access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data.
[☝️ Go back to Summary](#summary)
|
harvard-lil/cold-cases
|
[
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"united states",
"law",
"legal",
"court",
"opinions",
"region:us"
] |
2023-09-12T16:29:50+00:00
|
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["1M<n<10M"], "tags": ["united states", "law", "legal", "court", "opinions"], "viewer": true}
|
2024-01-24T21:44:29+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-1M<n<10M #language-English #license-cc0-1.0 #united states #law #legal #court #opinions #region-us
|
<a href="URL src="URL
Collaborative Open Legal Data (COLD) - Cases
============================================
COLD Cases is a dataset of 8.3 million United States legal decisions with text and metadata, formatted as compressed parquet files. If you'd like to view a sample of the dataset formatted as JSON Lines, you can view one here
This dataset exists to support the open legal movement exemplified by projects like
Pile of Law and
LegalBench.
A key input to legal understanding projects is caselaw -- the published, precedential decisions of judges deciding legal disputes and explaining their reasoning.
United States caselaw is collected and published as open data by CourtListener, which maintains scrapers to aggregate data from
a wide range of public sources.
COLD Cases reformats CourtListener's bulk data so that all of the semantic information about each legal decision
(the authors and text of majority and dissenting opinions; head matter; and substantive metadata) is encoded in a single record per decision,
with extraneous data removed. Serving in the traditional role of libraries as a standardization steward, the Harvard Library Innovation Lab is maintaining
this open source pipeline to consolidate the data engineering for preprocessing caselaw so downstream machine
learning and natural language processing projects can use consistent, high quality representations of cases for legal understanding tasks.
Prepared by the Harvard Library Innovation Lab in collaboration with the Free Law Project.
---
Links
-----
* Data nutrition label (DRAFT). (Archive).
* Pipeline source code
---
Summary
-------
* Formats
* File structure
* Data dictionary
* Notes on appropriate use
---
Format
------
Apache Parquet is binary format that makes filtering and retrieving the data quicker because it lays out the data in columns, which means columns that are unnecessary to satisfy a given query or workflow don't need to be read. Hugging Face's Datasets library is an easy way to get started working with the entire dataset, and has features for loading and streaming the data, so you don't need to store it all locally or pay attention to how it's formatted on disk.
️ Go back to Summary
---
Data dictionary
---------------
Partial glossary of the fields in the data.
### court\_type field values
️ Go back to Summary
Notes on appropriate use
------------------------
When using this data, please keep in mind:
* All documents in this dataset are public information, published by courts within the United States to inform the public about the law. You have a right to access them.
* Nevertheless, public court decisions frequently contain statements about individuals that are not true. Court decisions often contain claims that are disputed,
or false claims taken as true based on a legal technicality, or claims taken as true but later found to be false. Legal decisions are designed to inform you about the law -- they are not
designed to inform you about individuals, and should not be used in place of credit databases, criminal records databases, news articles, or other sources intended
to provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals.
* Court decisions are not up-to-date statements of law. Each decision provides a given judge's best understanding of the law as applied to the stated facts
at the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context --
the skill typically provided by lawyers -- rather than simple data retrieval.
To mitigate privacy risks, we have filtered out cases blocked or deindexed by CourtListener. Researchers who
require access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data.
️ Go back to Summary
|
[
"### court\\_type field values\n\n\n\n️ Go back to Summary\n\n\nNotes on appropriate use\n------------------------\n\n\nWhen using this data, please keep in mind:\n\n\n* All documents in this dataset are public information, published by courts within the United States to inform the public about the law. You have a right to access them.\n* Nevertheless, public court decisions frequently contain statements about individuals that are not true. Court decisions often contain claims that are disputed,\nor false claims taken as true based on a legal technicality, or claims taken as true but later found to be false. Legal decisions are designed to inform you about the law -- they are not\ndesigned to inform you about individuals, and should not be used in place of credit databases, criminal records databases, news articles, or other sources intended\nto provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals.\n* Court decisions are not up-to-date statements of law. Each decision provides a given judge's best understanding of the law as applied to the stated facts\nat the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context --\nthe skill typically provided by lawyers -- rather than simple data retrieval.\n\n\nTo mitigate privacy risks, we have filtered out cases blocked or deindexed by CourtListener. Researchers who\nrequire access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data.\n\n\n️ Go back to Summary"
] |
[
"TAGS\n#size_categories-1M<n<10M #language-English #license-cc0-1.0 #united states #law #legal #court #opinions #region-us \n",
"### court\\_type field values\n\n\n\n️ Go back to Summary\n\n\nNotes on appropriate use\n------------------------\n\n\nWhen using this data, please keep in mind:\n\n\n* All documents in this dataset are public information, published by courts within the United States to inform the public about the law. You have a right to access them.\n* Nevertheless, public court decisions frequently contain statements about individuals that are not true. Court decisions often contain claims that are disputed,\nor false claims taken as true based on a legal technicality, or claims taken as true but later found to be false. Legal decisions are designed to inform you about the law -- they are not\ndesigned to inform you about individuals, and should not be used in place of credit databases, criminal records databases, news articles, or other sources intended\nto provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals.\n* Court decisions are not up-to-date statements of law. Each decision provides a given judge's best understanding of the law as applied to the stated facts\nat the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context --\nthe skill typically provided by lawyers -- rather than simple data retrieval.\n\n\nTo mitigate privacy risks, we have filtered out cases blocked or deindexed by CourtListener. Researchers who\nrequire access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data.\n\n\n️ Go back to Summary"
] |
[
43,
336
] |
[
"passage: TAGS\n#size_categories-1M<n<10M #language-English #license-cc0-1.0 #united states #law #legal #court #opinions #region-us \n### court\\_type field values\n\n\n\n️ Go back to Summary\n\n\nNotes on appropriate use\n------------------------\n\n\nWhen using this data, please keep in mind:\n\n\n* All documents in this dataset are public information, published by courts within the United States to inform the public about the law. You have a right to access them.\n* Nevertheless, public court decisions frequently contain statements about individuals that are not true. Court decisions often contain claims that are disputed,\nor false claims taken as true based on a legal technicality, or claims taken as true but later found to be false. Legal decisions are designed to inform you about the law -- they are not\ndesigned to inform you about individuals, and should not be used in place of credit databases, criminal records databases, news articles, or other sources intended\nto provide factual personal information. Applications should carefully consider whether use of this data will inform about the law, or mislead about individuals.\n* Court decisions are not up-to-date statements of law. Each decision provides a given judge's best understanding of the law as applied to the stated facts\nat the time of the decision. Use of this data to generate statements about the law requires integration of a large amount of context --\nthe skill typically provided by lawyers -- rather than simple data retrieval.\n\n\nTo mitigate privacy risks, we have filtered out cases blocked or deindexed by CourtListener. Researchers who\nrequire access to the full dataset without that filter may rerun our pipeline on CourtListener's raw data.\n\n\n️ Go back to Summary"
] |
663e94b6b4f0981edc156a4ffaa2c28cdfaeda50
|
# Dataset of moon (Pokémon)
This is the dataset of moon (Pokémon), containing 127 images and their tags.
The core tags of this character are `short_hair, black_hair, hat, breasts, red_headwear, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 127 | 87.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moon_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 127 | 64.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moon_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 256 | 119.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moon_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 127 | 84.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moon_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 256 | 149.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moon_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/moon_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, navel, solo, beanie, blush, looking_at_viewer, nipples, open_mouth, shirt_lift, short_shorts, green_shorts, large_breasts, simple_background, white_background |
| 1 | 14 |  |  |  |  |  | 1girl, beanie, green_shorts, floral_print, short_sleeves, solo, yellow_shirt, blush, short_shorts, simple_background, tied_shirt, white_background, shoes, blue_eyes, smile, open_mouth |
| 2 | 5 |  |  |  |  |  | 1girl, hetero, solo_focus, 1boy, ass, beanie, blue_eyes, nipples, nude, open_mouth, pussy, uncensored, vaginal, doggystyle, medium_breasts, sex_from_behind, tongue_out, ahegao, all_fours, clitoris, disembodied_penis, heart, large_breasts, navel, shoes, sweat, teeth, z-ring |
| 3 | 13 |  |  |  |  |  | 1girl, hetero, sex, blush, beanie, nipples, open_mouth, penis, vaginal, 1boy, solo_focus, spread_legs, censored, cum_in_pussy, medium_breasts, navel, pokemon_(creature), barefoot, dark_skin, multiple_boys, pokephilia, tears, teeth, armpits, completely_nude, on_back, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | navel | solo | beanie | blush | looking_at_viewer | nipples | open_mouth | shirt_lift | short_shorts | green_shorts | large_breasts | simple_background | white_background | floral_print | short_sleeves | yellow_shirt | tied_shirt | shoes | blue_eyes | smile | hetero | solo_focus | 1boy | ass | nude | pussy | uncensored | vaginal | doggystyle | medium_breasts | sex_from_behind | tongue_out | ahegao | all_fours | clitoris | disembodied_penis | heart | sweat | teeth | z-ring | sex | penis | spread_legs | censored | cum_in_pussy | pokemon_(creature) | barefoot | dark_skin | multiple_boys | pokephilia | tears | armpits | completely_nude | on_back |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:---------|:--------|:--------------------|:----------|:-------------|:-------------|:---------------|:---------------|:----------------|:--------------------|:-------------------|:---------------|:----------------|:---------------|:-------------|:--------|:------------|:--------|:---------|:-------------|:-------|:------|:-------|:--------|:-------------|:----------|:-------------|:-----------------|:------------------|:-------------|:---------|:------------|:-----------|:--------------------|:--------|:--------|:--------|:---------|:------|:--------|:--------------|:-----------|:---------------|:---------------------|:-----------|:------------|:----------------|:-------------|:--------|:----------|:------------------|:----------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | | X | X | X | | | X | | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | X | | | X | X | | | | X | | | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 3 | 13 |  |  |  |  |  | X | X | | X | X | | X | X | | | | | | | | | | | | | | X | X | X | | | | | X | | X | | | | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/moon_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T16:34:50+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T21:20:37+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of moon (Pokémon)
=========================
This is the dataset of moon (Pokémon), containing 127 images and their tags.
The core tags of this character are 'short\_hair, black\_hair, hat, breasts, red\_headwear, bangs', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
dac1b5d608a24cfb56c521ccaac6a3ddf9ce39ee
|
# Dataset Card for Evaluation run of CalderaAI/13B-Thorns-l2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CalderaAI/13B-Thorns-l2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CalderaAI/13B-Thorns-l2](https://huggingface.co/CalderaAI/13B-Thorns-l2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T07:53:37.765793](https://huggingface.co/datasets/open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2/blob/main/results_2023-10-24T07-53-37.765793.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.38873741610738255,
"em_stderr": 0.004992082219869444,
"f1": 0.4612814597315456,
"f1_stderr": 0.004772539023607796,
"acc": 0.3770824444865971,
"acc_stderr": 0.007432066740076047
},
"harness|drop|3": {
"em": 0.38873741610738255,
"em_stderr": 0.004992082219869444,
"f1": 0.4612814597315456,
"f1_stderr": 0.004772539023607796
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.0026153265107756716
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2
|
[
"region:us"
] |
2023-09-12T16:38:11+00:00
|
{"pretty_name": "Evaluation run of CalderaAI/13B-Thorns-l2", "dataset_summary": "Dataset automatically created during the evaluation run of model [CalderaAI/13B-Thorns-l2](https://huggingface.co/CalderaAI/13B-Thorns-l2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T07:53:37.765793](https://huggingface.co/datasets/open-llm-leaderboard/details_CalderaAI__13B-Thorns-l2/blob/main/results_2023-10-24T07-53-37.765793.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.38873741610738255,\n \"em_stderr\": 0.004992082219869444,\n \"f1\": 0.4612814597315456,\n \"f1_stderr\": 0.004772539023607796,\n \"acc\": 0.3770824444865971,\n \"acc_stderr\": 0.007432066740076047\n },\n \"harness|drop|3\": {\n \"em\": 0.38873741610738255,\n \"em_stderr\": 0.004992082219869444,\n \"f1\": 0.4612814597315456,\n \"f1_stderr\": 0.004772539023607796\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \"acc_stderr\": 0.0026153265107756716\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n }\n}\n```", "repo_url": "https://huggingface.co/CalderaAI/13B-Thorns-l2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T17_37_55.153820", "path": ["**/details_harness|arc:challenge|25_2023-09-12T17-37-55.153820.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T17-37-55.153820.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T07_53_37.765793", "path": ["**/details_harness|drop|3_2023-10-24T07-53-37.765793.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T07-53-37.765793.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T07_53_37.765793", "path": ["**/details_harness|gsm8k|5_2023-10-24T07-53-37.765793.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T07-53-37.765793.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T17_37_55.153820", "path": ["**/details_harness|hellaswag|10_2023-09-12T17-37-55.153820.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T17-37-55.153820.parquet"]}]}, 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|
2023-10-24T06:53:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of CalderaAI/13B-Thorns-l2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model CalderaAI/13B-Thorns-l2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T07:53:37.765793(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of CalderaAI/13B-Thorns-l2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model CalderaAI/13B-Thorns-l2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T07:53:37.765793(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of CalderaAI/13B-Thorns-l2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model CalderaAI/13B-Thorns-l2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T07:53:37.765793(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of CalderaAI/13B-Thorns-l2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model CalderaAI/13B-Thorns-l2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T07:53:37.765793(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
0540d5d5b61e2aefe05aa4bfac48d17a73eab1c3
|
# Dataset Card for "llm-lotr-test1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bipulparua/llm-lotr-test1
|
[
"region:us"
] |
2023-09-12T16:39:12+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 2196528.0, "num_examples": 268}, {"name": "test", "num_bytes": 245880.0, "num_examples": 30}], "download_size": 1128455, "dataset_size": 2442408.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-09-13T03:54:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llm-lotr-test1"
More Information needed
|
[
"# Dataset Card for \"llm-lotr-test1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llm-lotr-test1\"\n\nMore Information needed"
] |
[
6,
18
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"llm-lotr-test1\"\n\nMore Information needed"
] |
879f57716e507da8d0231f58f99404d1efd258c9
|
# Dataset Card for Evaluation run of marcchew/Platypus-2-7B-LaMini-14K
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/marcchew/Platypus-2-7B-LaMini-14K
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [marcchew/Platypus-2-7B-LaMini-14K](https://huggingface.co/marcchew/Platypus-2-7B-LaMini-14K) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_marcchew__Platypus-2-7B-LaMini-14K",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:16:09.335987](https://huggingface.co/datasets/open-llm-leaderboard/details_marcchew__Platypus-2-7B-LaMini-14K/blob/main/results_2023-12-03T19-16-09.335987.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_marcchew__Platypus-2-7B-LaMini-14K
|
[
"region:us"
] |
2023-09-12T16:52:31+00:00
|
{"pretty_name": "Evaluation run of marcchew/Platypus-2-7B-LaMini-14K", "dataset_summary": "Dataset automatically created during the evaluation run of model [marcchew/Platypus-2-7B-LaMini-14K](https://huggingface.co/marcchew/Platypus-2-7B-LaMini-14K) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_marcchew__Platypus-2-7B-LaMini-14K\",\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-03T19:16:09.335987](https://huggingface.co/datasets/open-llm-leaderboard/details_marcchew__Platypus-2-7B-LaMini-14K/blob/main/results_2023-12-03T19-16-09.335987.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/marcchew/Platypus-2-7B-LaMini-14K", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T17_52_15.447894", "path": ["**/details_harness|arc:challenge|25_2023-09-12T17-52-15.447894.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T17-52-15.447894.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_05T06_09_29.689794", "path": ["**/details_harness|drop|3_2023-11-05T06-09-29.689794.parquet"]}, {"split": "2023_11_08T08_38_24.948337", "path": ["**/details_harness|drop|3_2023-11-08T08-38-24.948337.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-08T08-38-24.948337.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_05T06_09_29.689794", "path": ["**/details_harness|gsm8k|5_2023-11-05T06-09-29.689794.parquet"]}, {"split": "2023_11_08T08_38_24.948337", "path": ["**/details_harness|gsm8k|5_2023-11-08T08-38-24.948337.parquet"]}, {"split": "2023_12_03T19_16_09.335987", "path": ["**/details_harness|gsm8k|5_2023-12-03T19-16-09.335987.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-03T19-16-09.335987.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T17_52_15.447894", "path": ["**/details_harness|hellaswag|10_2023-09-12T17-52-15.447894.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T17-52-15.447894.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T17_52_15.447894", "path": 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2023-12-03T19:16:16+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of marcchew/Platypus-2-7B-LaMini-14K
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model marcchew/Platypus-2-7B-LaMini-14K on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-03T19:16:09.335987(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of marcchew/Platypus-2-7B-LaMini-14K",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model marcchew/Platypus-2-7B-LaMini-14K on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T19:16:09.335987(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of marcchew/Platypus-2-7B-LaMini-14K",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model marcchew/Platypus-2-7B-LaMini-14K on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-03T19:16:09.335987(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
24,
31,
173,
67,
10,
4,
6,
6,
5,
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5,
7,
4,
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9,
8,
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7,
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6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of marcchew/Platypus-2-7B-LaMini-14K## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model marcchew/Platypus-2-7B-LaMini-14K on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-03T19:16:09.335987(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
471a4b3ba131a1ff3e92acd3ec2a20cf899f3805
|
# Dataset Card for "xlsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Lancelot53/xlsum
|
[
"region:us"
] |
2023-09-12T16:52:48+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "image_paths", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 982097374, "num_examples": 306522}, {"name": "test", "num_bytes": 35146245.0, "num_examples": 11535}, {"name": "validation", "num_bytes": 35382527.0, "num_examples": 11535}], "download_size": 648046091, "dataset_size": 1052626146.0}}
|
2023-09-12T17:01:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xlsum"
More Information needed
|
[
"# Dataset Card for \"xlsum\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xlsum\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xlsum\"\n\nMore Information needed"
] |
6d74be5ed1108a8a9fc47017b3faead6eda813e1
|
# Dataset Card for "newCompleteSyntheticDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
crewdon/completeSynthetic
|
[
"region:us"
] |
2023-09-12T16:56:55+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 332515, "num_examples": 1570}], "download_size": 101432, "dataset_size": 332515}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-12T16:56:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "newCompleteSyntheticDataset"
More Information needed
|
[
"# Dataset Card for \"newCompleteSyntheticDataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"newCompleteSyntheticDataset\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"newCompleteSyntheticDataset\"\n\nMore Information needed"
] |
b4ea83c3b407128d4de364a413eea391df7fc850
|
# Dataset Card for Evaluation run of Sao10K/Stheno-Mix-L2-20B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/Stheno-Mix-L2-20B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/Stheno-Mix-L2-20B](https://huggingface.co/Sao10K/Stheno-Mix-L2-20B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__Stheno-Mix-L2-20B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T21:20:55.048363](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-Mix-L2-20B/blob/main/results_2023-10-27T21-20-55.048363.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.007864932885906041,
"em_stderr": 0.0009046332824008167,
"f1": 0.09529572147651061,
"f1_stderr": 0.001910164389772424,
"acc": 0.3452883094688581,
"acc_stderr": 0.006879302129291214
},
"harness|drop|3": {
"em": 0.007864932885906041,
"em_stderr": 0.0009046332824008167,
"f1": 0.09529572147651061,
"f1_stderr": 0.001910164389772424
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225214
},
"harness|winogrande|5": {
"acc": 0.6898184688239937,
"acc_stderr": 0.013000454144859907
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Sao10K__Stheno-Mix-L2-20B
|
[
"region:us"
] |
2023-09-12T17:05:32+00:00
|
{"pretty_name": "Evaluation run of Sao10K/Stheno-Mix-L2-20B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Sao10K/Stheno-Mix-L2-20B](https://huggingface.co/Sao10K/Stheno-Mix-L2-20B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Stheno-Mix-L2-20B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T21:20:55.048363](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-Mix-L2-20B/blob/main/results_2023-10-27T21-20-55.048363.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.007864932885906041,\n \"em_stderr\": 0.0009046332824008167,\n \"f1\": 0.09529572147651061,\n \"f1_stderr\": 0.001910164389772424,\n \"acc\": 0.3452883094688581,\n \"acc_stderr\": 0.006879302129291214\n },\n \"harness|drop|3\": {\n \"em\": 0.007864932885906041,\n \"em_stderr\": 0.0009046332824008167,\n \"f1\": 0.09529572147651061,\n \"f1_stderr\": 0.001910164389772424\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \"acc_stderr\": 0.0007581501137225214\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6898184688239937,\n \"acc_stderr\": 0.013000454144859907\n }\n}\n```", "repo_url": "https://huggingface.co/Sao10K/Stheno-Mix-L2-20B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|arc:challenge|25_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_27T21_20_55.048363", "path": ["**/details_harness|drop|3_2023-10-27T21-20-55.048363.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-27T21-20-55.048363.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_27T21_20_55.048363", "path": ["**/details_harness|gsm8k|5_2023-10-27T21-20-55.048363.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-27T21-20-55.048363.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hellaswag|10_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T18-05-15.025202.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T18-05-15.025202.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_27T21_20_55.048363", "path": ["**/details_harness|winogrande|5_2023-10-27T21-20-55.048363.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-27T21-20-55.048363.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_12T18_05_15.025202", "path": ["results_2023-09-12T18-05-15.025202.parquet"]}, {"split": "2023_10_27T21_20_55.048363", "path": ["results_2023-10-27T21-20-55.048363.parquet"]}, {"split": "latest", "path": ["results_2023-10-27T21-20-55.048363.parquet"]}]}]}
|
2023-10-27T20:21:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Sao10K/Stheno-Mix-L2-20B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Sao10K/Stheno-Mix-L2-20B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-27T21:20:55.048363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Sao10K/Stheno-Mix-L2-20B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Stheno-Mix-L2-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T21:20:55.048363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Sao10K/Stheno-Mix-L2-20B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Stheno-Mix-L2-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T21:20:55.048363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
24,
31,
172,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Sao10K/Stheno-Mix-L2-20B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Stheno-Mix-L2-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-27T21:20:55.048363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
47a13ab3442e7c0d15b982caa7bb5d0b134c34df
|
Kranajan/test-01-00
|
[
"task_categories:conversational",
"size_categories:n<1K",
"language:es",
"region:us"
] |
2023-09-12T17:13:11+00:00
|
{"language": ["es"], "size_categories": ["n<1K"], "task_categories": ["conversational"], "pretty_name": "test amco", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 284}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-27T15:53:27+00:00
|
[] |
[
"es"
] |
TAGS
#task_categories-conversational #size_categories-n<1K #language-Spanish #region-us
|
[] |
[
"TAGS\n#task_categories-conversational #size_categories-n<1K #language-Spanish #region-us \n"
] |
[
31
] |
[
"passage: TAGS\n#task_categories-conversational #size_categories-n<1K #language-Spanish #region-us \n"
] |
||
7e642770b52b397241268061ce1d690e1c7fa1b8
|
# Dataset Card for "maltaomics_dataset_clustered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roa7n/maltaomics_dataset_clustered
|
[
"region:us"
] |
2023-09-12T17:14:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "seq", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 429909, "num_examples": 1600}, {"name": "test", "num_bytes": 106032, "num_examples": 400}], "download_size": 0, "dataset_size": 535941}}
|
2023-09-13T18:48:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "maltaomics_dataset_clustered"
More Information needed
|
[
"# Dataset Card for \"maltaomics_dataset_clustered\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"maltaomics_dataset_clustered\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"maltaomics_dataset_clustered\"\n\nMore Information needed"
] |
6fea5ba7b3a930b6a8173cbb18889dec9586dfdb
|
# Dataset of ootsuki_yui/大槻唯 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of ootsuki_yui/大槻唯 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `blonde_hair, long_hair, blue_eyes, breasts, bangs, large_breasts, wavy_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 776.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ootsuki_yui_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 411.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ootsuki_yui_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1307 | 943.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ootsuki_yui_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 675.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ootsuki_yui_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1307 | 1.38 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ootsuki_yui_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ootsuki_yui_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | blue_skirt, blush, long_sleeves, looking_at_viewer, open_mouth, pleated_skirt, school_uniform, 1girl, :d, solo, upper_teeth_only, white_shirt, blue_bowtie, collared_shirt, pink_cardigan, striped_bowtie, collarbone, dress_shirt, white_background, miniskirt, necklace, simple_background, sleeves_past_wrists, holding_lollipop, medium_breasts |
| 1 | 5 |  |  |  |  |  | 1girl, blue_skirt, blush, looking_at_viewer, pleated_skirt, solo, collarbone, long_sleeves, necklace, open_mouth, school_uniform, sitting, teeth, white_background, white_socks, blue_bow, cleavage, full_body, lollipop, loose_socks, pink_cardigan, white_shirt, :d, brown_footwear, holding, loafers, loose_bowtie, simple_background, striped |
| 2 | 34 |  |  |  |  |  | looking_at_viewer, striped_bikini, 1girl, cleavage, ponytail, solo, bare_shoulders, blush, necklace, collarbone, hair_ornament, navel, open_mouth, halterneck, multicolored_bikini, bracelet, front-tie_bikini_top, denim_shorts, short_shorts, side-tie_bikini_bottom, string_bikini, outdoors, day, blue_sky, :d, o-ring, cowboy_shot, ocean, cloud, sidelocks, belt, open_fly |
| 3 | 15 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, obi, wide_sleeves, hair_ornament, long_sleeves, open_mouth, :d, braid, floral_print, holding, pink_kimono, white_background, hair_bow, upper_body, simple_background, upper_teeth_only |
| 4 | 11 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, solo, blush, necklace, cleavage, hair_flower, medium_breasts, red_dress, black_gloves, black_thighhighs, elbow_gloves, open_mouth, red_flower, rose, simple_background, strapless_dress, teeth, white_background, :d, frilled_dress, high_heels, red_footwear |
| 5 | 8 |  |  |  |  |  | 1girl, blush, looking_at_viewer, red_headwear, solo, puffy_short_sleeves, open_mouth, :d, blue_dress, red_bow, wrist_cuffs, belt, beret, blurry_background, holding, suitcase, upper_teeth_only, white_background, earrings, simple_background |
| 6 | 6 |  |  |  |  |  | 1girl, blush, completely_nude, looking_at_viewer, navel, nipples, smile, solo, open_mouth, necklace, ponytail |
| 7 | 10 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, nipples, cowgirl_position, girl_on_top, looking_at_viewer, penis, censored, collarbone, sex, smile, navel, sweat, pov, pussy, vaginal, completely_nude, heart, open_mouth |
| 8 | 5 |  |  |  |  |  | 1girl, card_(medium), character_name, solo, star_(symbol), sun_symbol, baseball_cap, lollipop, one_eye_closed, open_mouth, wristband, :d, denim, necklace, pants, skirt |
| 9 | 7 |  |  |  |  |  | 1boy, 1girl, blush, hetero, mosaic_censoring, penis, solo_focus, white_shirt, fellatio, cum, looking_at_viewer, open_mouth, ponytail, smile, long_sleeves, nipples, tongue, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blue_skirt | blush | long_sleeves | looking_at_viewer | open_mouth | pleated_skirt | school_uniform | 1girl | :d | solo | upper_teeth_only | white_shirt | blue_bowtie | collared_shirt | pink_cardigan | striped_bowtie | collarbone | dress_shirt | white_background | miniskirt | necklace | simple_background | sleeves_past_wrists | holding_lollipop | medium_breasts | sitting | teeth | white_socks | blue_bow | cleavage | full_body | lollipop | loose_socks | brown_footwear | holding | loafers | loose_bowtie | striped | striped_bikini | ponytail | bare_shoulders | hair_ornament | navel | halterneck | multicolored_bikini | bracelet | front-tie_bikini_top | denim_shorts | short_shorts | side-tie_bikini_bottom | string_bikini | outdoors | day | blue_sky | o-ring | cowboy_shot | ocean | cloud | sidelocks | belt | open_fly | obi | wide_sleeves | braid | floral_print | pink_kimono | hair_bow | upper_body | hair_flower | red_dress | black_gloves | black_thighhighs | elbow_gloves | red_flower | rose | strapless_dress | frilled_dress | high_heels | red_footwear | red_headwear | puffy_short_sleeves | blue_dress | red_bow | wrist_cuffs | beret | blurry_background | suitcase | earrings | completely_nude | nipples | smile | 1boy | hetero | solo_focus | cowgirl_position | girl_on_top | penis | censored | sex | sweat | pov | pussy | vaginal | heart | card_(medium) | character_name | star_(symbol) | sun_symbol | baseball_cap | one_eye_closed | wristband | denim | pants | skirt | mosaic_censoring | fellatio | cum | tongue |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------|:--------|:---------------|:--------------------|:-------------|:----------------|:-----------------|:--------|:-----|:-------|:-------------------|:--------------|:--------------|:-----------------|:----------------|:-----------------|:-------------|:--------------|:-------------------|:------------|:-----------|:--------------------|:----------------------|:-------------------|:-----------------|:----------|:--------|:--------------|:-----------|:-----------|:------------|:-----------|:--------------|:-----------------|:----------|:----------|:---------------|:----------|:-----------------|:-----------|:-----------------|:----------------|:--------|:-------------|:----------------------|:-----------|:-----------------------|:---------------|:---------------|:-------------------------|:----------------|:-----------|:------|:-----------|:---------|:--------------|:--------|:--------|:------------|:-------|:-----------|:------|:---------------|:--------|:---------------|:--------------|:-----------|:-------------|:--------------|:------------|:---------------|:-------------------|:---------------|:-------------|:-------|:------------------|:----------------|:-------------|:---------------|:---------------|:----------------------|:-------------|:----------|:--------------|:--------|:--------------------|:-----------|:-----------|:------------------|:----------|:--------|:-------|:---------|:-------------|:-------------------|:--------------|:--------|:-----------|:------|:--------|:------|:--------|:----------|:--------|:----------------|:-----------------|:----------------|:-------------|:---------------|:-----------------|:------------|:--------|:--------|:--------|:-------------------|:-----------|:------|:---------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | X | | | X | | X | | X | | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 34 |  |  |  |  |  | | X | | X | X | | | X | X | X | | | | | | | X | | | | X | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 15 |  |  |  |  |  | | X | X | X | X | | | X | X | X | X | | | | | | | | X | | | X | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | | X | | X | X | | | X | X | X | | | | | | | | | X | | X | X | | | X | | X | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | | X | | X | X | | | X | X | X | X | | | | | | | | X | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | | X | | X | X | | | X | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | | X | | X | X | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | | | | | X | | | X | X | X | | | | | | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | |
| 9 | 7 |  |  |  |  |  | | X | X | X | X | | | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | X | | | | | | | | | | | | | | | | | | X | X | X | X |
|
CyberHarem/ootsuki_yui_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T17:15:47+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T16:07:23+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of ootsuki\_yui/大槻唯 (THE iDOLM@STER: Cinderella Girls)
==============================================================
This is the dataset of ootsuki\_yui/大槻唯 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, blue\_eyes, breasts, bangs, large\_breasts, wavy\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
fcfb147a3293cf88b3cfb8289607107c1c8836f5
|
# Dataset Card for "paper_test_assym_bert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/paper_test_assym
|
[
"region:us"
] |
2023-09-12T17:16:40+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "retrieved_evidence_title", "sequence": "string"}, {"name": "retrieved_evidence_text", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 73088087, "num_examples": 11073}], "download_size": 34395774, "dataset_size": 73088087}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-12T17:17:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "paper_test_assym_bert"
More Information needed
|
[
"# Dataset Card for \"paper_test_assym_bert\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"paper_test_assym_bert\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"paper_test_assym_bert\"\n\nMore Information needed"
] |
7058b202caa90b9ac9a3eb9ba1bbec12ade6de79
|
# Dataset Card for "Goud-Sum-Instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ali-C137/Goud-Sum-Instruct-test-v0
|
[
"region:us"
] |
2023-09-12T17:19:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22447355, "num_examples": 9497}], "download_size": 10247768, "dataset_size": 22447355}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-12T17:19:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Goud-Sum-Instruct"
More Information needed
|
[
"# Dataset Card for \"Goud-Sum-Instruct\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Goud-Sum-Instruct\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Goud-Sum-Instruct\"\n\nMore Information needed"
] |
40424477d1d58159a65b38d0aaa2a9da38dda613
|
# Dataset Card for Evaluation run of Sao10K/Stheno-1.3-L2-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/Stheno-1.3-L2-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/Stheno-1.3-L2-13B](https://huggingface.co/Sao10K/Stheno-1.3-L2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__Stheno-1.3-L2-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T21:32:22.649999](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-1.3-L2-13B/blob/main/results_2023-10-23T21-32-22.649999.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2075293624161074,
"em_stderr": 0.0041530868871623655,
"f1": 0.3234186241610759,
"f1_stderr": 0.004163889670714078,
"acc": 0.35670155034816864,
"acc_stderr": 0.00702519954294294
},
"harness|drop|3": {
"em": 0.2075293624161074,
"em_stderr": 0.0041530868871623655,
"f1": 0.3234186241610759,
"f1_stderr": 0.004163889670714078
},
"harness|gsm8k|5": {
"acc": 0.002274450341167551,
"acc_stderr": 0.0013121578148674326
},
"harness|winogrande|5": {
"acc": 0.7111286503551697,
"acc_stderr": 0.012738241271018446
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Sao10K__Stheno-1.3-L2-13B
|
[
"region:us"
] |
2023-09-12T17:39:11+00:00
|
{"pretty_name": "Evaluation run of Sao10K/Stheno-1.3-L2-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Sao10K/Stheno-1.3-L2-13B](https://huggingface.co/Sao10K/Stheno-1.3-L2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Stheno-1.3-L2-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T21:32:22.649999](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-1.3-L2-13B/blob/main/results_2023-10-23T21-32-22.649999.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2075293624161074,\n \"em_stderr\": 0.0041530868871623655,\n \"f1\": 0.3234186241610759,\n \"f1_stderr\": 0.004163889670714078,\n \"acc\": 0.35670155034816864,\n \"acc_stderr\": 0.00702519954294294\n },\n \"harness|drop|3\": {\n \"em\": 0.2075293624161074,\n \"em_stderr\": 0.0041530868871623655,\n \"f1\": 0.3234186241610759,\n \"f1_stderr\": 0.004163889670714078\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.002274450341167551,\n \"acc_stderr\": 0.0013121578148674326\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7111286503551697,\n \"acc_stderr\": 0.012738241271018446\n }\n}\n```", "repo_url": "https://huggingface.co/Sao10K/Stheno-1.3-L2-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T18_38_55.194574", "path": ["**/details_harness|arc:challenge|25_2023-09-12T18-38-55.194574.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T18-38-55.194574.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T21_32_22.649999", "path": ["**/details_harness|drop|3_2023-10-23T21-32-22.649999.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T21-32-22.649999.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T21_32_22.649999", "path": ["**/details_harness|gsm8k|5_2023-10-23T21-32-22.649999.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T21-32-22.649999.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T18_38_55.194574", "path": ["**/details_harness|hellaswag|10_2023-09-12T18-38-55.194574.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T18-38-55.194574.parquet"]}]}, 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|
2023-10-23T20:32:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Sao10K/Stheno-1.3-L2-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Sao10K/Stheno-1.3-L2-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T21:32:22.649999(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Sao10K/Stheno-1.3-L2-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Stheno-1.3-L2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T21:32:22.649999(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Sao10K/Stheno-1.3-L2-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Stheno-1.3-L2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T21:32:22.649999(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Sao10K/Stheno-1.3-L2-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Sao10K/Stheno-1.3-L2-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T21:32:22.649999(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
455b3142989290fbfcb617df4af7caf9cea1c501
|
# Dataset of matsurika (Pokémon)
This is the dataset of matsurika (Pokémon), containing 270 images and their tags.
The core tags of this character are `blonde_hair, long_hair, bright_pupils, grey_eyes, blue_eyes, eyelashes, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 270 | 227.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsurika_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 270 | 140.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsurika_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 535 | 259.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsurika_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 270 | 204.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsurika_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 535 | 354.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsurika_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/matsurika_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, facepaint, half-closed_eyes, solo, white_shirt, oversized_shirt, upper_body, collarbone, short_sleeves, simple_background, white_background |
| 1 | 9 |  |  |  |  |  | 1girl, facepaint, full_body, half-closed_eyes, oversized_shirt, short_sleeves, white_shirt, bag, closed_mouth, simple_background, solo, torn_pants, white_background, holding, collarbone, looking_at_viewer, paintbrush, sneakers, standing |
| 2 | 15 |  |  |  |  |  | 1girl, facepaint, half-closed_eyes, simple_background, 1boy, hetero, penis, white_background, solo_focus, upper_body, white_shirt, fellatio, mosaic_censoring, collarbone, looking_at_viewer, open_mouth, tongue, low_ponytail, white_pupils |
| 3 | 31 |  |  |  |  |  | 1girl, facepaint, solo, nipples, bodypaint, navel, half-closed_eyes, pussy, collarbone, simple_background, closed_mouth, looking_at_viewer, completely_nude, barefoot, holding_paintbrush, uncensored, full_body, small_breasts, white_background, shiny |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | facepaint | half-closed_eyes | solo | white_shirt | oversized_shirt | upper_body | collarbone | short_sleeves | simple_background | white_background | full_body | bag | closed_mouth | torn_pants | holding | looking_at_viewer | paintbrush | sneakers | standing | 1boy | hetero | penis | solo_focus | fellatio | mosaic_censoring | open_mouth | tongue | low_ponytail | white_pupils | nipples | bodypaint | navel | pussy | completely_nude | barefoot | holding_paintbrush | uncensored | small_breasts | shiny |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:-------------------|:-------|:--------------|:------------------|:-------------|:-------------|:----------------|:--------------------|:-------------------|:------------|:------|:---------------|:-------------|:----------|:--------------------|:-------------|:-----------|:-----------|:-------|:---------|:--------|:-------------|:-----------|:-------------------|:-------------|:---------|:---------------|:---------------|:----------|:------------|:--------|:--------|:------------------|:-----------|:---------------------|:-------------|:----------------|:--------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 2 | 15 |  |  |  |  |  | X | X | X | | X | | X | X | | X | X | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 3 | 31 |  |  |  |  |  | X | X | X | X | | | | X | | X | X | X | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/matsurika_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T17:39:45+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T21:36:56+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of matsurika (Pokémon)
==============================
This is the dataset of matsurika (Pokémon), containing 270 images and their tags.
The core tags of this character are 'blonde\_hair, long\_hair, bright\_pupils, grey\_eyes, blue\_eyes, eyelashes, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
8e5627262b0430dcb5a8f60b673506f6477d1d6e
|
# Dataset Card for Evaluation run of jondurbin/spicyboros-7b-2.2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jondurbin/spicyboros-7b-2.2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [jondurbin/spicyboros-7b-2.2](https://huggingface.co/jondurbin/spicyboros-7b-2.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_jondurbin__spicyboros-7b-2.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T02:23:36.307180](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__spicyboros-7b-2.2/blob/main/results_2023-10-26T02-23-36.307180.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.32393036912751677,
"em_stderr": 0.004792489810373419,
"f1": 0.3773773070469811,
"f1_stderr": 0.004716033997487649,
"acc": 0.39679434744338254,
"acc_stderr": 0.009083637794148745
},
"harness|drop|3": {
"em": 0.32393036912751677,
"em_stderr": 0.004792489810373419,
"f1": 0.3773773070469811,
"f1_stderr": 0.004716033997487649
},
"harness|gsm8k|5": {
"acc": 0.04852160727824109,
"acc_stderr": 0.005918468618921068
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_jondurbin__spicyboros-7b-2.2
|
[
"region:us"
] |
2023-09-12T17:48:56+00:00
|
{"pretty_name": "Evaluation run of jondurbin/spicyboros-7b-2.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [jondurbin/spicyboros-7b-2.2](https://huggingface.co/jondurbin/spicyboros-7b-2.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jondurbin__spicyboros-7b-2.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-26T02:23:36.307180](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__spicyboros-7b-2.2/blob/main/results_2023-10-26T02-23-36.307180.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.32393036912751677,\n \"em_stderr\": 0.004792489810373419,\n \"f1\": 0.3773773070469811,\n \"f1_stderr\": 0.004716033997487649,\n \"acc\": 0.39679434744338254,\n \"acc_stderr\": 0.009083637794148745\n },\n \"harness|drop|3\": {\n \"em\": 0.32393036912751677,\n \"em_stderr\": 0.004792489810373419,\n \"f1\": 0.3773773070469811,\n \"f1_stderr\": 0.004716033997487649\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04852160727824109,\n \"acc_stderr\": 0.005918468618921068\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n }\n}\n```", "repo_url": "https://huggingface.co/jondurbin/spicyboros-7b-2.2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T18_48_40.427009", "path": ["**/details_harness|arc:challenge|25_2023-09-12T18-48-40.427009.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T18-48-40.427009.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_26T02_23_36.307180", "path": ["**/details_harness|drop|3_2023-10-26T02-23-36.307180.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-26T02-23-36.307180.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_26T02_23_36.307180", "path": ["**/details_harness|gsm8k|5_2023-10-26T02-23-36.307180.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-26T02-23-36.307180.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T18_48_40.427009", "path": ["**/details_harness|hellaswag|10_2023-09-12T18-48-40.427009.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T18-48-40.427009.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T18_48_40.427009", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T18-48-40.427009.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T18-48-40.427009.parquet", 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|
2023-10-26T01:23:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of jondurbin/spicyboros-7b-2.2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model jondurbin/spicyboros-7b-2.2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-26T02:23:36.307180(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of jondurbin/spicyboros-7b-2.2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model jondurbin/spicyboros-7b-2.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T02:23:36.307180(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of jondurbin/spicyboros-7b-2.2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model jondurbin/spicyboros-7b-2.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-26T02:23:36.307180(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of jondurbin/spicyboros-7b-2.2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model jondurbin/spicyboros-7b-2.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-26T02:23:36.307180(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
b7d0aae974815842f5ae6ec140a16b17ab346a5b
|
# Dataset of sima_yi_reines/司馬懿〔ライネス〕/司马懿〔莱妮丝〕 (Fate/Grand Order)
This is the dataset of sima_yi_reines/司馬懿〔ライネス〕/司马懿〔莱妮丝〕 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `blonde_hair, bangs, long_hair, hat, blue_eyes, black_headwear, breasts, tilted_headwear, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 623.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sima_yi_reines_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 354.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sima_yi_reines_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1197 | 768.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sima_yi_reines_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 549.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sima_yi_reines_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1197 | 1.06 GiB | [Download](https://huggingface.co/datasets/CyberHarem/sima_yi_reines_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sima_yi_reines_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, closed_mouth, fur_collar, hair_flower, long_sleeves, looking_at_viewer, smile, solo, beret, brown_gloves, simple_background, blush, rose, white_flower, blue_jacket |
| 1 | 5 |  |  |  |  |  | 1girl, beret, brown_gloves, fur_collar, green_eyes, long_sleeves, looking_at_viewer, solo, blue_jacket, blush, blue_dress, grin, hair_flower |
| 2 | 6 |  |  |  |  |  | 1girl, black_pantyhose, brown_gloves, long_sleeves, looking_at_viewer, solo, blue_jacket, closed_mouth, simple_background, smile, white_background, blue_scarf, flower |
| 3 | 5 |  |  |  |  |  | 1girl, blue_jacket, blue_scarf, brown_gloves, flower, long_sleeves, looking_at_viewer, smile, open_mouth, simple_background, solo, white_background, beret, blush, fur_collar |
| 4 | 5 |  |  |  |  |  | 1girl, blue_jacket, brown_gloves, long_sleeves, looking_at_viewer, open_mouth, solo, beret, black_pantyhose, blue_dress, blush, fur_collar, blue_scarf, brown_pantyhose, smile, cropped_jacket, hair_flower |
| 5 | 6 |  |  |  |  |  | 1girl, black_pantyhose, blush, brown_gloves, flower, fur_collar, long_sleeves, sitting, solo, blue_dress, crossed_legs, looking_at_viewer, smile, beret, blue_jacket, sidelocks, chair, closed_mouth |
| 6 | 9 |  |  |  |  |  | 1girl, blue_jacket, long_sleeves, looking_at_viewer, solo, black_skirt, blush, white_gloves, white_headwear, flower, armband, brown_pantyhose, simple_background, white_background, black_pantyhose, closed_mouth, sword, grin, peaked_cap |
| 7 | 18 |  |  |  |  |  | 1girl, nipples, blush, small_breasts, pussy, navel, 1boy, hetero, looking_at_viewer, smile, solo_focus, spread_legs, completely_nude, penis, sex, vaginal, collarbone, open_mouth, mosaic_censoring |
| 8 | 6 |  |  |  |  |  | blue_sky, blush, collarbone, day, looking_at_viewer, smile, 1girl, cloud, hair_flower, navel, outdoors, solo, bare_shoulders, black_bikini, medium_breasts, beach, blue_bikini, cleavage, ocean, small_breasts, thighs, water |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | fur_collar | hair_flower | long_sleeves | looking_at_viewer | smile | solo | beret | brown_gloves | simple_background | blush | rose | white_flower | blue_jacket | green_eyes | blue_dress | grin | black_pantyhose | white_background | blue_scarf | flower | open_mouth | brown_pantyhose | cropped_jacket | sitting | crossed_legs | sidelocks | chair | black_skirt | white_gloves | white_headwear | armband | sword | peaked_cap | nipples | small_breasts | pussy | navel | 1boy | hetero | solo_focus | spread_legs | completely_nude | penis | sex | vaginal | collarbone | mosaic_censoring | blue_sky | day | cloud | outdoors | bare_shoulders | black_bikini | medium_breasts | beach | blue_bikini | cleavage | ocean | thighs | water |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------------|:--------------|:---------------|:--------------------|:--------|:-------|:--------|:---------------|:--------------------|:--------|:-------|:---------------|:--------------|:-------------|:-------------|:-------|:------------------|:-------------------|:-------------|:---------|:-------------|:------------------|:-----------------|:----------|:---------------|:------------|:--------|:--------------|:---------------|:-----------------|:----------|:--------|:-------------|:----------|:----------------|:--------|:--------|:-------|:---------|:-------------|:--------------|:------------------|:--------|:------|:----------|:-------------|:-------------------|:-----------|:------|:--------|:-----------|:-----------------|:---------------|:-----------------|:--------|:--------------|:-----------|:--------|:---------|:--------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | | X | X | X | X | | X | X | X | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | | | X | X | X | X | | X | X | | | | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | | X | X | X | X | X | X | X | X | | | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | X | X | X | X | X | X | X | | X | | | X | | X | | X | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | X | | X | X | X | X | X | X | | X | | | X | | X | | X | | | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | X | | | X | X | | X | | | X | X | | | X | | | X | X | X | | X | | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 18 |  |  |  |  |  | X | | | | | X | X | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | | | X | | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/sima_yi_reines_fgo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T17:56:24+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-12T14:51:21+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of sima\_yi\_reines/司馬懿〔ライネス〕/司马懿〔莱妮丝〕 (Fate/Grand Order)
=================================================================
This is the dataset of sima\_yi\_reines/司馬懿〔ライネス〕/司马懿〔莱妮丝〕 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are 'blonde\_hair, bangs, long\_hair, hat, blue\_eyes, black\_headwear, breasts, tilted\_headwear, hair\_ornament', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
d57f78eaaca7fead16ff8d47c5a7b48cd2b117d3
|
# Dataset Card for Goud-Sum-Instruct
Goud-Sum-Instruct is a meticulously curated dataset originating from [Goud-sum](https://huggingface.co/datasets/Goud/Goud-sum) dataset, This dataset is primed for fine-tuning chat and instruct models, without any compromise to the existing training mode. This strategic approach enables the specific training of models to respond effectively to the main instruction which is "To Summarise". In conclusion, this dataset is meant to finetune a chat model in order to serve later as a summarizer.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Goud.ma: a News Article Dataset for Summarization in Moroccan Darija](https://openreview.net/forum?id=BMVq5MELb9)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Goud-Sum-Instruct contains 158k articles and their headlines extracted from [Goud.ma](https://www.goud.ma/) news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).
### Supported Tasks and Leaderboards
Text Summarization
### Languages
- Moroccan Arabic (Darija)
- Modern Standard Arabic
## Dataset Structure
### Data Instances
The dataset consists of article-headline pairs in string format.
### Data Fields
- article: a string containing the body of the news article
- headline: a string containing the article's headline
- categories: a list of string of article categories
### Data Splits
Goud-Sum-Instruct dataset has 3 splits: _train_, _validation_, and _test_. Below are the number of instances in each split.
| Dataset Split | Number of Instances in Split |
| ------------- | ---------------------------- |
| Train | 139,288 |
| Validation | 9,497 |
| Test | 9,497 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The text was written by journalists at [Goud](https://www.goud.ma/).
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{issam2022goudma,
title={Goud.ma: a News Article Dataset for Summarization in Moroccan Darija},
author={Abderrahmane Issam and Khalil Mrini},
booktitle={3rd Workshop on African Natural Language Processing},
year={2022},
url={https://openreview.net/forum?id=BMVq5MELb9}
}
```
### Contributions
Thanks to [@issam9](https://github.com/issam9) and [@KhalilMrini](https://github.com/KhalilMrini) for adding the original [dataset](https://huggingface.co/datasets/Goud/Goud-sum)
|
Ali-C137/Goud-Sum-Instruct
|
[
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:ar",
"license:apache-2.0",
"region:us"
] |
2023-09-12T17:58:32+00:00
|
{"language": ["ar"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["summarization"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 329002522, "num_examples": 139288}, {"name": "validation", "num_bytes": 22449821, "num_examples": 9497}, {"name": "test", "num_bytes": 22447355, "num_examples": 9497}], "download_size": 170777466, "dataset_size": 373899698}}
|
2023-09-12T18:22:47+00:00
|
[] |
[
"ar"
] |
TAGS
#task_categories-summarization #size_categories-100K<n<1M #language-Arabic #license-apache-2.0 #region-us
|
Dataset Card for Goud-Sum-Instruct
==================================
Goud-Sum-Instruct is a meticulously curated dataset originating from Goud-sum dataset, This dataset is primed for fine-tuning chat and instruct models, without any compromise to the existing training mode. This strategic approach enables the specific training of models to respond effectively to the main instruction which is "To Summarise". In conclusion, this dataset is meant to finetune a chat model in order to serve later as a summarizer.
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ Personal and Sensitive Information
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Additional Information
+ Dataset Curators
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper: URL: a News Article Dataset for Summarization in Moroccan Darija
* Leaderboard:
* Point of Contact:
### Dataset Summary
Goud-Sum-Instruct contains 158k articles and their headlines extracted from URL news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).
### Supported Tasks and Leaderboards
Text Summarization
### Languages
* Moroccan Arabic (Darija)
* Modern Standard Arabic
Dataset Structure
-----------------
### Data Instances
The dataset consists of article-headline pairs in string format.
### Data Fields
* article: a string containing the body of the news article
* headline: a string containing the article's headline
* categories: a list of string of article categories
### Data Splits
Goud-Sum-Instruct dataset has 3 splits: *train*, *validation*, and *test*. Below are the number of instances in each split.
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
The text was written by journalists at Goud.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @issam9 and @KhalilMrini for adding the original dataset
|
[
"### Dataset Summary\n\n\nGoud-Sum-Instruct contains 158k articles and their headlines extracted from URL news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).",
"### Supported Tasks and Leaderboards\n\n\nText Summarization",
"### Languages\n\n\n* Moroccan Arabic (Darija)\n* Modern Standard Arabic\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThe dataset consists of article-headline pairs in string format.",
"### Data Fields\n\n\n* article: a string containing the body of the news article\n* headline: a string containing the article's headline\n* categories: a list of string of article categories",
"### Data Splits\n\n\nGoud-Sum-Instruct dataset has 3 splits: *train*, *validation*, and *test*. Below are the number of instances in each split.\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?\n\n\nThe text was written by journalists at Goud.",
"### Annotations\n\n\nThe dataset does not contain any additional annotations.",
"#### Annotation process\n\n\n[N/A]",
"#### Who are the annotators?\n\n\n[N/A]",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @issam9 and @KhalilMrini for adding the original dataset"
] |
[
"TAGS\n#task_categories-summarization #size_categories-100K<n<1M #language-Arabic #license-apache-2.0 #region-us \n",
"### Dataset Summary\n\n\nGoud-Sum-Instruct contains 158k articles and their headlines extracted from URL news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).",
"### Supported Tasks and Leaderboards\n\n\nText Summarization",
"### Languages\n\n\n* Moroccan Arabic (Darija)\n* Modern Standard Arabic\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThe dataset consists of article-headline pairs in string format.",
"### Data Fields\n\n\n* article: a string containing the body of the news article\n* headline: a string containing the article's headline\n* categories: a list of string of article categories",
"### Data Splits\n\n\nGoud-Sum-Instruct dataset has 3 splits: *train*, *validation*, and *test*. Below are the number of instances in each split.\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?\n\n\nThe text was written by journalists at Goud.",
"### Annotations\n\n\nThe dataset does not contain any additional annotations.",
"#### Annotation process\n\n\n[N/A]",
"#### Who are the annotators?\n\n\n[N/A]",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @issam9 and @KhalilMrini for adding the original dataset"
] |
[
41,
82,
14,
24,
22,
44,
53,
7,
4,
10,
20,
17,
10,
14,
18,
7,
8,
14,
6,
6,
23
] |
[
"passage: TAGS\n#task_categories-summarization #size_categories-100K<n<1M #language-Arabic #license-apache-2.0 #region-us \n### Dataset Summary\n\n\nGoud-Sum-Instruct contains 158k articles and their headlines extracted from URL news website. The articles are written in the Arabic script. All headlines are in Moroccan Darija, while articles may be in Moroccan Darija, in Modern Standard Arabic, or a mix of both (code-switched Moroccan Darija).### Supported Tasks and Leaderboards\n\n\nText Summarization### Languages\n\n\n* Moroccan Arabic (Darija)\n* Modern Standard Arabic\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nThe dataset consists of article-headline pairs in string format.### Data Fields\n\n\n* article: a string containing the body of the news article\n* headline: a string containing the article's headline\n* categories: a list of string of article categories### Data Splits\n\n\nGoud-Sum-Instruct dataset has 3 splits: *train*, *validation*, and *test*. Below are the number of instances in each split.\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?\n\n\nThe text was written by journalists at Goud.### Annotations\n\n\nThe dataset does not contain any additional annotations.#### Annotation process\n\n\n[N/A]#### Who are the annotators?\n\n\n[N/A]### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information### Contributions\n\n\nThanks to @issam9 and @KhalilMrini for adding the original dataset"
] |
f2261277240b5bdf37470adf01b14de65c04dd44
|
# Dataset Card for "e9d30f3e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/e9d30f3e
|
[
"region:us"
] |
2023-09-12T18:09:55+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1339, "dataset_size": 186}}
|
2023-09-12T18:09:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "e9d30f3e"
More Information needed
|
[
"# Dataset Card for \"e9d30f3e\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"e9d30f3e\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"e9d30f3e\"\n\nMore Information needed"
] |
525d9d1751baf5368d1729c99348d79c31966124
|
# Dataset of lychee/ライチ (Pokémon)
This is the dataset of lychee/ライチ (Pokémon), containing 128 images and their tags.
The core tags of this character are `short_hair, dark-skinned_female, dark_skin, black_hair, breasts, earrings, pink_lips, large_breasts, black_eyes, medium_breasts, lips`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 128 | 96.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lychee_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 128 | 72.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lychee_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 279 | 134.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lychee_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 128 | 93.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lychee_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 279 | 161.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lychee_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/lychee_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, bare_shoulders, lipstick, neck_ring, necklace, solo, aqua_nails, blue_nails, cleavage, diamond_(shape), looking_at_viewer, midriff, nail_polish, navel, pink_shirt, short_shorts, smile, bracelet, hand_on_hip, simple_background, tank_top, fingernails, purple_shorts, sideboob, standing, white_background, sleeveless, thighlet, cowboy_shot, diamond_(gemstone), holding_poke_ball, wristband |
| 1 | 10 |  |  |  |  |  | 1girl, bare_shoulders, bracelet, necklace, short_shorts, lipstick, looking_at_viewer, neck_ring, smile, anklet, beads, full_body, pink_shirt, purple_shorts, simple_background, solo, white_background, from_behind, high_heels, looking_back, standing, ass, holding_poke_ball, toenail_polish, hand_on_hip, midriff, poke_ball_(basic), tank_top, thigh_strap, blue_nails, thighlet |
| 2 | 6 |  |  |  |  |  | 1boy, 1girl, blush, bracelet, hetero, lipstick, nipples, penis, solo_focus, vaginal, neck_ring, pussy, thighlet, anklet, girl_on_top, sweat, ass, bead_necklace, dark-skinned_male, diamond_(shape), interracial, navel, nude, open_mouth, pointless_censoring, sex_from_behind, spread_legs, straddling |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | lipstick | neck_ring | necklace | solo | aqua_nails | blue_nails | cleavage | diamond_(shape) | looking_at_viewer | midriff | nail_polish | navel | pink_shirt | short_shorts | smile | bracelet | hand_on_hip | simple_background | tank_top | fingernails | purple_shorts | sideboob | standing | white_background | sleeveless | thighlet | cowboy_shot | diamond_(gemstone) | holding_poke_ball | wristband | anklet | beads | full_body | from_behind | high_heels | looking_back | ass | toenail_polish | poke_ball_(basic) | thigh_strap | 1boy | blush | hetero | nipples | penis | solo_focus | vaginal | pussy | girl_on_top | sweat | bead_necklace | dark-skinned_male | interracial | nude | open_mouth | pointless_censoring | sex_from_behind | spread_legs | straddling |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------|:------------|:-----------|:-------|:-------------|:-------------|:-----------|:------------------|:--------------------|:----------|:--------------|:--------|:-------------|:---------------|:--------|:-----------|:--------------|:--------------------|:-----------|:--------------|:----------------|:-----------|:-----------|:-------------------|:-------------|:-----------|:--------------|:---------------------|:--------------------|:------------|:---------|:--------|:------------|:--------------|:-------------|:---------------|:------|:-----------------|:--------------------|:--------------|:-------|:--------|:---------|:----------|:--------|:-------------|:----------|:--------|:--------------|:--------|:----------------|:--------------------|:--------------|:-------|:-------------|:----------------------|:------------------|:--------------|:-------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | X | X | | X | | | X | X | | | X | X | X | X | X | X | X | | X | | X | X | | X | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | X | X | | | | | | X | | | | X | | | | X | | | | | | | | | | X | | | | | X | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/lychee_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T18:16:14+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T21:29:45+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of lychee/ライチ (Pokémon)
===============================
This is the dataset of lychee/ライチ (Pokémon), containing 128 images and their tags.
The core tags of this character are 'short\_hair, dark-skinned\_female, dark\_skin, black\_hair, breasts, earrings, pink\_lips, large\_breasts, black\_eyes, medium\_breasts, lips', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
a6b8f0e24451fba7d54b73a1a11ee97dd433da69
|
# Dataset of burnet (Pokémon)
This is the dataset of burnet (Pokémon), containing 69 images and their tags.
The core tags of this character are `white_hair, dark_skin, dark-skinned_female, breasts, yellow_eyes, long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 69 | 51.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/burnet_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 69 | 34.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/burnet_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 120 | 62.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/burnet_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 69 | 48.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/burnet_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 120 | 85.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/burnet_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/burnet_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, large_breasts, nipples, blush, hetero, navel, penis, pussy, 1boy, bar_censor, collarbone, looking_at_viewer, open_mouth, smile, solo_focus, bare_shoulders, female_pubic_hair, heart, shirt_lift, simple_background, tank_top, tongue_out, torn_clothes |
| 1 | 7 |  |  |  |  |  | 1girl, simple_background, grin, necklace, solo, closed_eyes, white_background, teeth, blush, sidelocks, upper_body |
| 2 | 10 |  |  |  |  |  | 1girl, smile, closed_mouth, looking_at_viewer, necklace, solo, cleavage, green_eyes, tank_top, collarbone, eyelashes, shirt, white_background, simple_background, bare_arms, bare_shoulders, sidelocks, sleeveless, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | large_breasts | nipples | blush | hetero | navel | penis | pussy | 1boy | bar_censor | collarbone | looking_at_viewer | open_mouth | smile | solo_focus | bare_shoulders | female_pubic_hair | heart | shirt_lift | simple_background | tank_top | tongue_out | torn_clothes | grin | necklace | solo | closed_eyes | white_background | teeth | sidelocks | upper_body | closed_mouth | cleavage | green_eyes | eyelashes | shirt | bare_arms | sleeveless |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:----------|:--------|:---------|:--------|:--------|:--------|:-------|:-------------|:-------------|:--------------------|:-------------|:--------|:-------------|:-----------------|:--------------------|:--------|:-------------|:--------------------|:-----------|:-------------|:---------------|:-------|:-----------|:-------|:--------------|:-------------------|:--------|:------------|:-------------|:---------------|:-----------|:-------------|:------------|:--------|:------------|:-------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | | | | | | | | | | X | X | | X | | X | | | | X | X | | | | X | X | | X | | X | X | X | X | X | X | X | X | X |
|
CyberHarem/burnet_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T18:29:44+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T21:19:28+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of burnet (Pokémon)
===========================
This is the dataset of burnet (Pokémon), containing 69 images and their tags.
The core tags of this character are 'white\_hair, dark\_skin, dark-skinned\_female, breasts, yellow\_eyes, long\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
d99e86f9f29460e1d774b5a71d7c903ad5eef260
|
# Dataset of anastasia_viy/アナスタシア&ヴィイ/阿纳斯塔西娅＆维 (Fate/Grand Order)
This is the dataset of anastasia_viy/アナスタシア&ヴィイ/阿纳斯塔西娅＆维 (Fate/Grand Order), containing 75 images and their tags.
The core tags of this character are `long_hair, blue_eyes, breasts, bangs, white_hair, very_long_hair, hair_over_one_eye, large_breasts, bow, hair_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 75 | 144.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anastasia_viy_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 75 | 71.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anastasia_viy_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 189 | 157.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anastasia_viy_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 75 | 121.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anastasia_viy_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 189 | 249.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anastasia_viy_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/anastasia_viy_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, bare_shoulders, blush, collarbone, flower_wreath, head_wreath, looking_at_viewer, twin_braids, white_dress, cleavage, dress_swimsuit, open_mouth, smile, doll, solo |
| 1 | 10 |  |  |  |  |  | 1girl, bare_shoulders, blue_dress, blush, earrings, necklace, pendant, smile, straw_hat, blue_sky, collarbone, day, looking_at_viewer, solo, bracelet, grey_hair, open_mouth, outdoors, thighs, beach, ocean, palm_tree, shore |
| 2 | 23 |  |  |  |  |  | 1girl, ponytail, blue_bikini, looking_at_viewer, bare_shoulders, blush, necklace, smile, blue_skirt, puffy_long_sleeves, cleavage, collarbone, miniskirt, navel, thighs, doll, innertube, solo, beach, blue_sky, ocean, watermelon |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | blush | collarbone | flower_wreath | head_wreath | looking_at_viewer | twin_braids | white_dress | cleavage | dress_swimsuit | open_mouth | smile | doll | solo | blue_dress | earrings | necklace | pendant | straw_hat | blue_sky | day | bracelet | grey_hair | outdoors | thighs | beach | ocean | palm_tree | shore | ponytail | blue_bikini | blue_skirt | puffy_long_sleeves | miniskirt | navel | innertube | watermelon |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:-------------|:----------------|:--------------|:--------------------|:--------------|:--------------|:-----------|:-----------------|:-------------|:--------|:-------|:-------|:-------------|:-----------|:-----------|:----------|:------------|:-----------|:------|:-----------|:------------|:-----------|:---------|:--------|:--------|:------------|:--------|:-----------|:--------------|:-------------|:---------------------|:------------|:--------|:------------|:-------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | | | X | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 2 | 23 |  |  |  |  |  | X | X | X | X | | | X | | | X | | | X | X | X | | | X | | | X | | | | | X | X | X | | | X | X | X | X | X | X | X | X |
|
CyberHarem/anastasia_viy_fgo
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T18:32:43+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-13T06:43:07+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of anastasia\_viy/アナスタシア&ヴィイ/阿纳斯塔西娅＆维 (Fate/Grand Order)
================================================================
This is the dataset of anastasia\_viy/アナスタシア&ヴィイ/阿纳斯塔西娅＆维 (Fate/Grand Order), containing 75 images and their tags.
The core tags of this character are 'long\_hair, blue\_eyes, breasts, bangs, white\_hair, very\_long\_hair, hair\_over\_one\_eye, large\_breasts, bow, hair\_bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
96637f316456bbd58dcc14ea47b8fbb4ae02aae1
|
# Dataset Card for "paper_test_assym_bert_results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/paper_test_assym_bert_results
|
[
"region:us"
] |
2023-09-12T18:39:35+00:00
|
{"dataset_info": {"features": [{"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "retrieved_evidence_title", "sequence": "string"}, {"name": "retrieved_evidence_text", "sequence": "string"}, {"name": "labels", "dtype": "int64"}, {"name": "Retrieval_Success", "dtype": "bool"}, {"name": "Predicted_Labels", "dtype": "int64"}, {"name": "Predicted_Labels_Each_doc", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 73601741, "num_examples": 11073}], "download_size": 34426515, "dataset_size": 73601741}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-12T18:39:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "paper_test_assym_bert_results"
More Information needed
|
[
"# Dataset Card for \"paper_test_assym_bert_results\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"paper_test_assym_bert_results\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"paper_test_assym_bert_results\"\n\nMore Information needed"
] |
2d37eef776024e4bb8121906a21c88aab3e7adbb
|
# Dataset Card for Evaluation run of JosephusCheung/Qwen-LLaMAfied-7B-Chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/JosephusCheung/Qwen-LLaMAfied-7B-Chat
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [JosephusCheung/Qwen-LLaMAfied-7B-Chat](https://huggingface.co/JosephusCheung/Qwen-LLaMAfied-7B-Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_JosephusCheung__Qwen-LLaMAfied-7B-Chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T05:54:59.935248](https://huggingface.co/datasets/open-llm-leaderboard/details_JosephusCheung__Qwen-LLaMAfied-7B-Chat/blob/main/results_2023-10-29T05-54-59.935248.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.29425335570469796,
"em_stderr": 0.004666860017033486,
"f1": 0.3722158137583904,
"f1_stderr": 0.004557451176367578,
"acc": 0.38970651153411406,
"acc_stderr": 0.009163863947895253
},
"harness|drop|3": {
"em": 0.29425335570469796,
"em_stderr": 0.004666860017033486,
"f1": 0.3722158137583904,
"f1_stderr": 0.004557451176367578
},
"harness|gsm8k|5": {
"acc": 0.047763457164518575,
"acc_stderr": 0.00587438753622931
},
"harness|winogrande|5": {
"acc": 0.7316495659037096,
"acc_stderr": 0.012453340359561195
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_JosephusCheung__Qwen-LLaMAfied-7B-Chat
|
[
"region:us"
] |
2023-09-12T18:56:37+00:00
|
{"pretty_name": "Evaluation run of JosephusCheung/Qwen-LLaMAfied-7B-Chat", "dataset_summary": "Dataset automatically created during the evaluation run of model [JosephusCheung/Qwen-LLaMAfied-7B-Chat](https://huggingface.co/JosephusCheung/Qwen-LLaMAfied-7B-Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_JosephusCheung__Qwen-LLaMAfied-7B-Chat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T05:54:59.935248](https://huggingface.co/datasets/open-llm-leaderboard/details_JosephusCheung__Qwen-LLaMAfied-7B-Chat/blob/main/results_2023-10-29T05-54-59.935248.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.29425335570469796,\n \"em_stderr\": 0.004666860017033486,\n \"f1\": 0.3722158137583904,\n \"f1_stderr\": 0.004557451176367578,\n \"acc\": 0.38970651153411406,\n \"acc_stderr\": 0.009163863947895253\n },\n \"harness|drop|3\": {\n \"em\": 0.29425335570469796,\n \"em_stderr\": 0.004666860017033486,\n \"f1\": 0.3722158137583904,\n \"f1_stderr\": 0.004557451176367578\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.047763457164518575,\n \"acc_stderr\": 0.00587438753622931\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7316495659037096,\n \"acc_stderr\": 0.012453340359561195\n }\n}\n```", "repo_url": "https://huggingface.co/JosephusCheung/Qwen-LLaMAfied-7B-Chat", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|arc:challenge|25_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T05_54_59.935248", "path": ["**/details_harness|drop|3_2023-10-29T05-54-59.935248.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-29T05-54-59.935248.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T05_54_59.935248", "path": ["**/details_harness|gsm8k|5_2023-10-29T05-54-59.935248.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-29T05-54-59.935248.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hellaswag|10_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T19-56-23.146408.parquet", 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"**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-management|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-marketing|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-nutrition|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-philosophy|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-prehistory|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-professional_law|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-public_relations|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-security_studies|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-sociology|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-virology|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-world_religions|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T19-56-23.146408.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T19-56-23.146408.parquet", 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["**/details_harness|hendrycksTest-philosophy|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T19-56-23.146408.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-12T19-56-23.146408.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_29T05_54_59.935248", "path": ["**/details_harness|winogrande|5_2023-10-29T05-54-59.935248.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-29T05-54-59.935248.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_12T19_56_23.146408", "path": ["results_2023-09-12T19-56-23.146408.parquet"]}, {"split": "2023_10_29T05_54_59.935248", "path": ["results_2023-10-29T05-54-59.935248.parquet"]}, {"split": "latest", "path": ["results_2023-10-29T05-54-59.935248.parquet"]}]}]}
|
2023-10-29T05:55:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of JosephusCheung/Qwen-LLaMAfied-7B-Chat
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model JosephusCheung/Qwen-LLaMAfied-7B-Chat on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-29T05:54:59.935248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of JosephusCheung/Qwen-LLaMAfied-7B-Chat",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model JosephusCheung/Qwen-LLaMAfied-7B-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-29T05:54:59.935248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of JosephusCheung/Qwen-LLaMAfied-7B-Chat",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model JosephusCheung/Qwen-LLaMAfied-7B-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-29T05:54:59.935248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of JosephusCheung/Qwen-LLaMAfied-7B-Chat## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model JosephusCheung/Qwen-LLaMAfied-7B-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-29T05:54:59.935248(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
dce218a7e1f2b845301cf4723a25446cf3c17843
|
# Dataset of lila/タワータイクーンリラ (Pokémon)
This is the dataset of lila/タワータイクーンリラ (Pokémon), containing 165 images and their tags.
The core tags of this character are `purple_hair, purple_eyes, long_hair, ponytail, ribbon, hair_ribbon, bangs, black_ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 165 | 122.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lila_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 165 | 79.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lila_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 347 | 160.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lila_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 165 | 111.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lila_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 347 | 211.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lila_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/lila_pokemon',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, black_gloves, black_jacket, black_necktie, white_shirt, collared_shirt, long_sleeves, closed_mouth, solo, looking_at_viewer, holding_poke_ball, black_pants, hand_up, ultra_ball, earpiece, low_ponytail, eyelashes, simple_background, white_background, earphones, formal, smile, suit |
| 1 | 13 |  |  |  |  |  | 1girl, black_gloves, black_pants, collared_shirt, long_sleeves, white_shirt, black_jacket, black_necktie, eyelashes, looking_at_viewer, smile, white_background, black_footwear, closed_mouth, earpiece, pokemon_(creature), shoes, standing, earrings, full_body, solo, simple_background |
| 2 | 5 |  |  |  |  |  | 1girl, formal, necktie, suit, gloves, smile, solo, earbuds |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | black_jacket | black_necktie | white_shirt | collared_shirt | long_sleeves | closed_mouth | solo | looking_at_viewer | holding_poke_ball | black_pants | hand_up | ultra_ball | earpiece | low_ponytail | eyelashes | simple_background | white_background | earphones | formal | smile | suit | black_footwear | pokemon_(creature) | shoes | standing | earrings | full_body | necktie | gloves | earbuds |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:---------------|:----------------|:--------------|:-----------------|:---------------|:---------------|:-------|:--------------------|:--------------------|:--------------|:----------|:-------------|:-----------|:---------------|:------------|:--------------------|:-------------------|:------------|:---------|:--------|:-------|:-----------------|:---------------------|:--------|:-----------|:-----------|:------------|:----------|:---------|:----------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | X | | | X | | X | X | X | | | X | | X | X | X | X | X | X | | | |
| 2 | 5 |  |  |  |  |  | X | | | | | | | | X | | | | | | | | | | | | X | X | X | | | | | | | X | X | X |
|
CyberHarem/lila_pokemon
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-12T19:05:46+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T21:57:01+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of lila/タワータイクーンリラ (Pokémon)
====================================
This is the dataset of lila/タワータイクーンリラ (Pokémon), containing 165 images and their tags.
The core tags of this character are 'purple\_hair, purple\_eyes, long\_hair, ponytail, ribbon, hair\_ribbon, bangs, black\_ribbon', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
1f1fdb40fd3ae062a1f80a250e7a854514c94894
|
# Bangumi Image Base of Yahari Ore No Seishun Lovecome Wa Machigatte Iru
This is the image base of bangumi Yahari Ore no Seishun LoveCome wa Machigatte Iru, we detected 73 characters, 10654 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1244 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 63 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 285 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 28 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 23 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 14 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 43 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 48 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 18 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
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| 56 | 7 | [Download](56/dataset.zip) |  |  |  |  |  |  |  | N/A |
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| 66 | 10 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
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| 70 | 19 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 31 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 295 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/yahariorenoseishunlovecomewamachigatteiru
|
[
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] |
2023-09-12T19:06:27+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]}
|
2023-09-29T05:51:25+00:00
|
[] |
[] |
TAGS
#size_categories-10K<n<100K #license-mit #art #region-us
|
Bangumi Image Base of Yahari Ore No Seishun Lovecome Wa Machigatte Iru
======================================================================
This is the image base of bangumi Yahari Ore no Seishun LoveCome wa Machigatte Iru, we detected 73 characters, 10654 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
6f4985cfd3158321188f672160be9993b0d7c06c
|
# Dataset Card for Evaluation run of chargoddard/llama-2-16b-nastychat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/llama-2-16b-nastychat
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/llama-2-16b-nastychat](https://huggingface.co/chargoddard/llama-2-16b-nastychat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chargoddard__llama-2-16b-nastychat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T13:04:12.376719](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-16b-nastychat/blob/main/results_2023-10-28T13-04-12.376719.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.15782298657718122,
"em_stderr": 0.0037335886860588308,
"f1": 0.24126782718120757,
"f1_stderr": 0.0038375528639246364,
"acc": 0.41388384087105284,
"acc_stderr": 0.009872075116078132
},
"harness|drop|3": {
"em": 0.15782298657718122,
"em_stderr": 0.0037335886860588308,
"f1": 0.24126782718120757,
"f1_stderr": 0.0038375528639246364
},
"harness|gsm8k|5": {
"acc": 0.08112206216830932,
"acc_stderr": 0.007520395797922653
},
"harness|winogrande|5": {
"acc": 0.7466456195737964,
"acc_stderr": 0.01222375443423361
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_chargoddard__llama-2-16b-nastychat
|
[
"region:us"
] |
2023-09-12T19:07:04+00:00
|
{"pretty_name": "Evaluation run of chargoddard/llama-2-16b-nastychat", "dataset_summary": "Dataset automatically created during the evaluation run of model [chargoddard/llama-2-16b-nastychat](https://huggingface.co/chargoddard/llama-2-16b-nastychat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__llama-2-16b-nastychat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T13:04:12.376719](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-16b-nastychat/blob/main/results_2023-10-28T13-04-12.376719.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.15782298657718122,\n \"em_stderr\": 0.0037335886860588308,\n \"f1\": 0.24126782718120757,\n \"f1_stderr\": 0.0038375528639246364,\n \"acc\": 0.41388384087105284,\n \"acc_stderr\": 0.009872075116078132\n },\n \"harness|drop|3\": {\n \"em\": 0.15782298657718122,\n \"em_stderr\": 0.0037335886860588308,\n \"f1\": 0.24126782718120757,\n \"f1_stderr\": 0.0038375528639246364\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08112206216830932,\n \"acc_stderr\": 0.007520395797922653\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.01222375443423361\n }\n}\n```", "repo_url": "https://huggingface.co/chargoddard/llama-2-16b-nastychat", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_12T20_06_49.075564", "path": ["**/details_harness|arc:challenge|25_2023-09-12T20-06-49.075564.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-12T20-06-49.075564.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T13_04_12.376719", "path": ["**/details_harness|drop|3_2023-10-28T13-04-12.376719.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T13-04-12.376719.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T13_04_12.376719", "path": ["**/details_harness|gsm8k|5_2023-10-28T13-04-12.376719.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T13-04-12.376719.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_12T20_06_49.075564", "path": ["**/details_harness|hellaswag|10_2023-09-12T20-06-49.075564.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-12T20-06-49.075564.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_12T20_06_49.075564", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-management|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-marketing|5_2023-09-12T20-06-49.075564.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T20-06-49.075564.parquet", 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|
2023-10-28T12:04:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of chargoddard/llama-2-16b-nastychat
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model chargoddard/llama-2-16b-nastychat on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T13:04:12.376719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of chargoddard/llama-2-16b-nastychat",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/llama-2-16b-nastychat on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T13:04:12.376719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of chargoddard/llama-2-16b-nastychat",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/llama-2-16b-nastychat on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T13:04:12.376719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of chargoddard/llama-2-16b-nastychat## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model chargoddard/llama-2-16b-nastychat on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T13:04:12.376719(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
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