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9f500809f0c3fa1c8046a8fe1027dd2814176aa7
|
# Dataset Card for "ActionData"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dshut002/ActionData
|
[
"region:us"
] |
2023-09-05T13:10:25+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 83784, "num_examples": 100}], "download_size": 40541, "dataset_size": 83784}}
|
2023-09-05T15:56:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ActionData"
More Information needed
|
[
"# Dataset Card for \"ActionData\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ActionData\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ActionData\"\n\nMore Information needed"
] |
a42b3b4f06eaf5a0bee78faa27fdca2f7d24d538
|
# Dataset Card for Evaluation run of TFLai/SpeechlessV1-Nova-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/SpeechlessV1-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/SpeechlessV1-Nova-13B](https://huggingface.co/TFLai/SpeechlessV1-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__SpeechlessV1-Nova-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T07:37:37.459766](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B/blob/main/results_2023-10-22T07-37-37.459766.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": 0.006396812080536913,
"em_stderr": 0.0008164468837432435,
"f1": 0.0904718959731546,
"f1_stderr": 0.0018774631078676703,
"acc": 0.41594466880448644,
"acc_stderr": 0.00908410979036271
},
"harness|drop|3": {
"em": 0.006396812080536913,
"em_stderr": 0.0008164468837432435,
"f1": 0.0904718959731546,
"f1_stderr": 0.0018774631078676703
},
"harness|gsm8k|5": {
"acc": 0.0576194086429113,
"acc_stderr": 0.006418593319822863
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902554
}
}
```
### 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__SpeechlessV1-Nova-13B
|
[
"region:us"
] |
2023-09-05T13:12:36+00:00
|
{"pretty_name": "Evaluation run of TFLai/SpeechlessV1-Nova-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/SpeechlessV1-Nova-13B](https://huggingface.co/TFLai/SpeechlessV1-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__SpeechlessV1-Nova-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T07:37:37.459766](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B/blob/main/results_2023-10-22T07-37-37.459766.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\": 0.006396812080536913,\n \"em_stderr\": 0.0008164468837432435,\n \"f1\": 0.0904718959731546,\n \"f1_stderr\": 0.0018774631078676703,\n \"acc\": 0.41594466880448644,\n \"acc_stderr\": 0.00908410979036271\n },\n \"harness|drop|3\": {\n \"em\": 0.006396812080536913,\n \"em_stderr\": 0.0008164468837432435,\n \"f1\": 0.0904718959731546,\n \"f1_stderr\": 0.0018774631078676703\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0576194086429113,\n \"acc_stderr\": 0.006418593319822863\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902554\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/SpeechlessV1-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_05T14_12_12.910236", "path": ["**/details_harness|arc:challenge|25_2023-09-05T14:12:12.910236.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-05T14:12:12.910236.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T07_37_37.459766", "path": ["**/details_harness|drop|3_2023-10-22T07-37-37.459766.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T07-37-37.459766.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T07_37_37.459766", "path": ["**/details_harness|gsm8k|5_2023-10-22T07-37-37.459766.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T07-37-37.459766.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_05T14_12_12.910236", "path": ["**/details_harness|hellaswag|10_2023-09-05T14:12:12.910236.parquet"]}, {"split": "latest", "path": 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|
2023-10-22T06:37:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/SpeechlessV1-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/SpeechlessV1-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 2023-10-22T07:37:37.459766(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/SpeechlessV1-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/SpeechlessV1-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 2023-10-22T07:37:37.459766(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 TFLai/SpeechlessV1-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/SpeechlessV1-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 2023-10-22T07:37:37.459766(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/SpeechlessV1-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/SpeechlessV1-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 2023-10-22T07:37:37.459766(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"
] |
a897672defe763baede99d300ae2ebd5f5c232ef
|
# Dataset Card for "autotree_pmlb_100000_banana_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_pmlb_100000_banana_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-09-05T13:17:49+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1237600000, "num_examples": 100000}, {"name": "validation", "num_bytes": 123760000, "num_examples": 10000}], "download_size": 274853161, "dataset_size": 1361360000}}
|
2023-09-05T13:18:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_pmlb_100000_banana_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_pmlb_100000_banana_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_pmlb_100000_banana_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
34
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_pmlb_100000_banana_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
afb42306115c36afbaab9b13fc4545b724ae66c4
|
# Dataset Card for WikiAnc
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Repository:** [WikiAnc repository](https://github.com/cyanic-selkie/wikianc)
### Dataset Summary
The WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023).
The code for generating the dataset can be found [here](https://github.com/cyanic-selkie/wikianc).
### Supported Tasks
- `wikificiation`: The dataset can be used to train a model for Wikification.
- `named-entity-linking`: The dataset can be used to train a model for Named Entity Linking.
### Languages
The text in the dataset is in all 320 Wikipedia languages. The full list can be found in the table below.
## Dataset Structure
### Data Instances
A typical data point represents a paragraph in a Wikipedia article.
The `paragraph_text` field contains the original text in an NFC normalized, UTF-8 encoded string.
The `paragraph_anchors` field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point `start` field, exclusive ending UTF-8 code point `end` field, a nullable `qid` field, a nullable `pageid` field, and an NFC normalized, UTF-8 encoded `title` (Wikipedia) field.
Additionally, each paragraph has `article_title`, `article_pageid`, and (nullable) `article_qid` fields referring to the article the paragraph came from.
There is also a nullable, NFC normalized, UTF-8 encoded `section_heading` field, and an integer `section_level` field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.
The `qid` fields refers to Wikidata's QID identifiers, while the `pageid` and `title` fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).
**NOTE:** An anchor will always have a `title`, but that doesn't mean it has to have a `pageid`. This is because Wikipedia allows defining anchors to nonexistent articles.
An example from the WikiAnc EN test set looks as follows:
```
{
"uuid": "5f74e678-944f-4761-a5e0-b6426f6f61b8",
"article_title": "Climatius",
"article_pageid": 5394373,
"article_qid": 867987,
"section_heading": null,
"section_level": 0,
"paragraph_text": "It was a small fish, at 7.5 cm, and to discourage predators, Climatius sported fifteen sharp spines. There was one spine each on the paired pelvic and pectoral fins, and on the aingle anal and two dorsal fins, and a four pairs without fins on the fish's underside.",
"paragraph_anchors": [
{
"start": 140,
"end": 146,
"qid": 3335089,
"pageid": 56849833,
"title": "Pelvic_fin"
},
{
"start": 151,
"end": 159,
"qid": 4162555,
"pageid": 331956,
"title": "Pectoral_fin"
},
{
"start": 184,
"end": 188,
"qid": 4162555,
"pageid": 331958,
"title": "Anal_fin"
},
{
"start": 197,
"end": 208,
"qid": 1568355,
"pageid": 294244,
"title": "Dorsal_fin"
}
]
}
```
### Data Fields
- `uuid`: a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example
- `article_title`: an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores
- `article_pageid`: an integer representing the Wikipedia pageID of the article
- `article_qid`: an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset
- `section_heading`: a nullable, NFC normalized, UTF-8 encoded string representing the section heading
- `section_level`: an integer representing the level of the section in the section hierarchy
- `paragraph_text`: an NFC normalized, UTF-8 encoded string representing the paragraph
- `paragraph_anchors`: a list of structs representing anchors, each anchor has:
- `start`: an integer representing the inclusive starting UTF-8 code point of the anchors
- `end`: an integer representing the exclusive ending UTF-8 code point of the anchor
- `qid`: a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset
- `pageid`: a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset
- `title`: an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article
### Data Splits
The data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:
#### Train
| | Articles | Paragraphs | Anchors | Anchors with QIDs | Anchors with PageIDs |
| :-- | --: | --: | --: | --: | --: |
| ab | 2378 | 5678 | 10515 | 3649 | 3650 |
| ace | 12591 | 23969 | 48638 | 25150 | 25175 |
| ady | 596 | 1662 | 2694 | 1593 | 1606 |
| af | 104470 | 399038 | 985640 | 900596 | 900967 |
| als | 27999 | 165085 | 402049 | 294742 | 294744 |
| alt | 1043 | 7468 | 9158 | 5446 | 5452 |
| am | 13576 | 46318 | 90051 | 51915 | 52173 |
| ami | 1582 | 12428 | 6080 | 1505 | 2579 |
| an | 40179 | 121367 | 669830 | 516248 | 516822 |
| ang | 3833 | 9664 | 24297 | 10189 | 10229 |
| anp | 2506 | 6865 | 14560 | 3825 | 5061 |
| ar | 1132271 | 3617491 | 11657228 | 11240112 | 11244160 |
| arc | 1844 | 3766 | 9232 | 5460 | 5545 |
| ary | 6736 | 17049 | 50185 | 34193 | 34227 |
| arz | 1579782 | 3693549 | 7879303 | 6906799 | 6917393 |
| as | 11947 | 77835 | 122760 | 67594 | 67720 |
| ast | 126992 | 877278 | 2952000 | 1775764 | 1777383 |
| atj | 1872 | 3820 | 6544 | 3247 | 3365 |
| av | 3048 | 8542 | 16115 | 8895 | 9000 |
| avk | 27577 | 85219 | 106100 | 32260 | 33491 |
| awa | 3396 | 5802 | 6617 | 1679 | 2370 |
| ay | 5102 | 15125 | 22802 | 13930 | 13933 |
| az | 180810 | 789902 | 1570889 | 1377797 | 1380325 |
| azb | 240990 | 585386 | 1241661 | 749575 | 753318 |
| ba | 62269 | 391926 | 625645 | 562730 | 563181 |
| ban | 18955 | 44138 | 86239 | 66213 | 66412 |
| bar | 26057 | 83298 | 185158 | 109082 | 109091 |
| bat_smg | 17013 | 41951 | 77417 | 51701 | 51733 |
| bcl | 13783 | 45457 | 78963 | 47819 | 47861 |
| be | 222883 | 821135 | 2499258 | 2204062 | 2204117 |
| bg | 285156 | 1336530 | 3967713 | 3618800 | 3627798 |
| bh | 7658 | 17052 | 29110 | 22157 | 22217 |
| bi | 1403 | 1712 | 3172 | 1991 | 1995 |
| bjn | 9672 | 19007 | 58660 | 32538 | 33071 |
| blk | 2786 | 11825 | 11341 | 5979 | 6129 |
| bm | 1111 | 2421 | 2451 | 1217 | 1218 |
| bn | 136921 | 736388 | 1530942 | 1161967 | 1162761 |
| bo | 11843 | 37121 | 8241 | 6265 | 6359 |
| bpy | 24742 | 115606 | 166906 | 86166 | 86170 |
| br | 78524 | 214128 | 657375 | 527295 | 527606 |
| bs | 86407 | 382114 | 1246030 | 965782 | 966511 |
| bug | 14231 | 14484 | 53879 | 14787 | 15146 |
| bxr | 2730 | 9571 | 27853 | 11560 | 11567 |
| ca | 691444 | 3596667 | 11359870 | 10236358 | 10237666 |
| cbk_zam | 2989 | 8322 | 9939 | 2790 | 2847 |
| cdo | 15922 | 30059 | 63474 | 29659 | 29705 |
| ce | 597137 | 2121587 | 3097393 | 1507129 | 1507806 |
| ceb | 5888811 | 11920613 | 37969424 | 33678489 | 33962205 |
| ch | 574 | 1166 | 2290 | 492 | 601 |
| chr | 980 | 1110 | 1311 | 779 | 790 |
| chy | 711 | 753 | 494 | 428 | 428 |
| ckb | 48903 | 163599 | 435662 | 224749 | 226749 |
| co | 6719 | 22954 | 46391 | 24149 | 24229 |
| cr | 158 | 216 | 209 | 94 | 94 |
| crh | 24117 | 29781 | 98534 | 70231 | 70235 |
| cs | 516037 | 2679537 | 9917806 | 8763103 | 8763291 |
| csb | 5315 | 14009 | 31294 | 16820 | 16820 |
| cu | 1171 | 2796 | 5283 | 2346 | 2349 |
| cv | 50525 | 157542 | 375399 | 166889 | 167497 |
| cy | 276031 | 992900 | 2011030 | 1613064 | 1620632 |
| da | 284765 | 1167917 | 4352733 | 3854239 | 3854549 |
| dag | 9248 | 29213 | 46084 | 10981 | 14213 |
| de | 2780056 | 16093948 | 52497421 | 50480495 | 50480548 |
| din | 485 | 1551 | 1096 | 197 | 197 |
| diq | 37565 | 70969 | 155656 | 141636 | 141695 |
| dsb | 3083 | 8760 | 19397 | 9652 | 9652 |
| dty | 3339 | 6219 | 7505 | 4417 | 4447 |
| dv | 4190 | 16809 | 7906 | 3612 | 3620 |
| dz | 652 | 2623 | 272 | 94 | 100 |
| ee | 1075 | 2326 | 1823 | 861 | 926 |
| el | 224207 | 1527561 | 4181433 | 3119952 | 3121967 |
| eml | 12169 | 53861 | 115729 | 65775 | 65940 |
| en | 6514924 | 40656507 | 109681826 | 107761324 | 107768438 |
| eo | 330486 | 1116191 | 4257655 | 3975927 | 3979379 |
| es | 1792062 | 10890435 | 33729712 | 31581851 | 31648945 |
| et | 233078 | 1110906 | 3558448 | 2879595 | 2886824 |
| eu | 386029 | 1405747 | 3398477 | 3025183 | 3030635 |
| ext | 3472 | 9626 | 20554 | 11966 | 11978 |
| fa | 901254 | 2357271 | 6189352 | 5862106 | 5870803 |
| fat | 1044 | 6092 | 1717 | 120 | 857 |
| ff | 1763 | 4103 | 3483 | 2304 | 2413 |
| fi | 373226 | 1667296 | 5221239 | 4658292 | 4663471 |
| fiu_vro | 6417 | 19897 | 40418 | 23563 | 23609 |
| fj | 1157 | 1782 | 4852 | 1910 | 1911 |
| fo | 11809 | 30828 | 119267 | 95117 | 95259 |
| fr | 2432972 | 15252697 | 43564517 | 42573624 | 42589064 |
| frp | 5341 | 10574 | 36358 | 24905 | 24926 |
| frr | 16038 | 30821 | 80265 | 68184 | 68315 |
| fur | 3665 | 10651 | 29516 | 16249 | 16278 |
| fy | 46011 | 206153 | 1271339 | 985227 | 985511 |
| ga | 52168 | 130535 | 347037 | 288261 | 288309 |
| gag | 2408 | 4844 | 8551 | 4520 | 4520 |
| gan | 4219 | 9689 | 18994 | 14119 | 14128 |
| gcr | 2227 | 5163 | 2763 | 1186 | 1186 |
| gd | 15850 | 48217 | 141290 | 95557 | 95562 |
| gl | 190419 | 910543 | 3674404 | 2937660 | 2938634 |
| glk | 6484 | 15344 | 32631 | 21395 | 21447 |
| gn | 5064 | 15481 | 40641 | 30389 | 30440 |
| gom | 4192 | 37508 | 14192 | 2369 | 2382 |
| gor | 14388 | 28133 | 107341 | 66191 | 67016 |
| got | 960 | 2186 | 4093 | 1404 | 1415 |
| gpe | 899 | 3383 | 1199 | 796 | 815 |
| gu | 30025 | 114805 | 459063 | 348651 | 348731 |
| guc | 546 | 2545 | 2300 | 1025 | 1138 |
| gur | 1010 | 5043 | 1761 | 227 | 244 |
| guw | 1263 | 3719 | 7474 | 3116 | 5375 |
| gv | 5036 | 12213 | 48801 | 19659 | 19663 |
| ha | 31977 | 149096 | 115029 | 97167 | 98184 |
| hak | 8694 | 11505 | 39744 | 28150 | 28152 |
| haw | 2470 | 5810 | 11169 | 5700 | 5705 |
| he | 323472 | 2648617 | 10904148 | 10367532 | 10379886 |
| hi | 150121 | 538451 | 964251 | 795726 | 798254 |
| hif | 10534 | 21169 | 43463 | 23970 | 24316 |
| hr | 189415 | 876107 | 3210326 | 2752205 | 2758602 |
| hsb | 13183 | 40760 | 91863 | 66632 | 66633 |
| ht | 64850 | 154160 | 201547 | 166206 | 167961 |
| hu | 346711 | 1859683 | 5267990 | 4707580 | 4710525 |
| hy | 298066 | 1542920 | 3767938 | 2689014 | 2690466 |
| hyw | 11358 | 83640 | 161227 | 82218 | 84817 |
| ia | 24581 | 43289 | 129914 | 96517 | 96595 |
| id | 620895 | 2138237 | 6589957 | 5629372 | 5644832 |
| ie | 11020 | 22342 | 60890 | 46054 | 46122 |
| ig | 19448 | 110907 | 57963 | 31022 | 31298 |
| ik | 737 | 1016 | 848 | 551 | 580 |
| ilo | 14135 | 74304 | 126533 | 75701 | 75705 |
| inh | 1754 | 4640 | 13284 | 5770 | 6011 |
| io | 36312 | 101555 | 303765 | 258933 | 259001 |
| is | 54348 | 170321 | 574897 | 436767 | 437784 |
| it | 1610989 | 8718610 | 27447754 | 26116131 | 26126157 |
| iu | 502 | 757 | 536 | 414 | 418 |
| ja | 1355269 | 9276459 | 29002111 | 27752954 | 27801000 |
| jam | 1571 | 2260 | 5887 | 3588 | 3590 |
| jbo | 1287 | 3088 | 5831 | 546 | 546 |
| jv | 66323 | 148710 | 547010 | 381682 | 382052 |
| ka | 167161 | 695865 | 2275552 | 422090 | 422095 |
| kaa | 3540 | 9814 | 12930 | 5312 | 5752 |
| kab | 5346 | 14709 | 36889 | 22000 | 22050 |
| kbd | 1549 | 6348 | 14594 | 5277 | 5280 |
| kbp | 1846 | 6005 | 7119 | 6875 | 6880 |
| kcg | 871 | 1839 | 2953 | 1857 | 1871 |
| kg | 1187 | 1933 | 3835 | 2292 | 2295 |
| ki | 1482 | 2899 | 2035 | 1386 | 1649 |
| kk | 235740 | 889990 | 1840304 | 1143049 | 1151399 |
| kl | 282 | 1024 | 1337 | 302 | 302 |
| km | 11422 | 84697 | 111378 | 40954 | 41529 |
| kn | 30729 | 261724 | 432994 | 188536 | 188807 |
| ko | 606386 | 2159706 | 6217786 | 5715559 | 5725614 |
| koi | 3260 | 9065 | 17068 | 10628 | 10628 |
| krc | 1465 | 6234 | 18092 | 7294 | 7311 |
| ks | 4176 | 9446 | 15252 | 5917 | 6226 |
| ksh | 2836 | 11043 | 26577 | 9484 | 9496 |
| ku | 55166 | 112840 | 269080 | 208679 | 210304 |
| kv | 5236 | 13396 | 32141 | 26727 | 26744 |
| kw | 6884 | 18901 | 49462 | 28074 | 28194 |
| ky | 75426 | 191772 | 271376 | 189656 | 190133 |
| la | 124150 | 240343 | 1456464 | 1283285 | 1283728 |
| lad | 3538 | 11910 | 37456 | 19124 | 19124 |
| lb | 57747 | 178507 | 573528 | 443583 | 444601 |
| lbe | 1205 | 2249 | 4470 | 2543 | 2543 |
| lez | 4067 | 16675 | 36970 | 25834 | 25842 |
| lfn | 4506 | 21746 | 29785 | 14554 | 14560 |
| lg | 3814 | 23386 | 15539 | 2088 | 2724 |
| li | 14134 | 58711 | 212772 | 137110 | 137367 |
| lij | 8092 | 23366 | 61410 | 34939 | 34940 |
| lld | 152613 | 158049 | 578033 | 443976 | 458150 |
| lmo | 67387 | 136650 | 373890 | 274174 | 274612 |
| ln | 3132 | 6066 | 11086 | 7838 | 7874 |
| lo | 4734 | 15005 | 27132 | 8562 | 8799 |
| lt | 204135 | 775863 | 2687983 | 2406710 | 2414909 |
| ltg | 1018 | 2979 | 5815 | 2190 | 2193 |
| lv | 118530 | 437086 | 1458341 | 1244609 | 1247181 |
| mad | 1113 | 3500 | 3762 | 1149 | 1157 |
| mai | 13285 | 22572 | 53246 | 38119 | 38128 |
| map_bms | 10875 | 16411 | 67964 | 51125 | 51137 |
| mdf | 4002 | 11043 | 21658 | 9178 | 9183 |
| mg | 92227 | 213580 | 328751 | 265931 | 267633 |
| mhr | 11010 | 33013 | 60771 | 38153 | 38220 |
| mi | 7274 | 10154 | 29052 | 24854 | 25216 |
| min | 223075 | 422381 | 1315030 | 513108 | 515548 |
| mk | 131522 | 695456 | 1984109 | 1639280 | 1640744 |
| ml | 84334 | 415940 | 797903 | 485482 | 486324 |
| mn | 23434 | 124485 | 295548 | 142014 | 142984 |
| mni | 10354 | 18872 | 29474 | 18810 | 19876 |
| mnw | 3136 | 34165 | 9342 | 1908 | 2387 |
| mr | 92464 | 326662 | 633452 | 383501 | 392709 |
| mrj | 10156 | 20132 | 48416 | 24098 | 24098 |
| ms | 344459 | 988647 | 2424535 | 1932685 | 1937647 |
| mt | 5381 | 49856 | 104636 | 51251 | 51278 |
| mwl | 4402 | 37271 | 127176 | 25729 | 26366 |
| my | 103938 | 334243 | 445026 | 300567 | 303288 |
| myv | 7515 | 21592 | 36762 | 26570 | 26591 |
| mzn | 17364 | 39937 | 89805 | 46962 | 47020 |
| nah | 5934 | 12478 | 30805 | 13093 | 14364 |
| nap | 11235 | 22336 | 41891 | 20798 | 20804 |
| nds | 79228 | 242004 | 583941 | 305374 | 305422 |
| nds_nl | 6484 | 28252 | 94875 | 51767 | 51785 |
| ne | 30359 | 91033 | 153937 | 124841 | 125078 |
| new | 71653 | 245033 | 454251 | 289444 | 289912 |
| nia | 1496 | 4047 | 4524 | 2258 | 2812 |
| nl | 1948842 | 5867108 | 17953497 | 16886996 | 16893078 |
| nn | 160106 | 549454 | 1751481 | 1375622 | 1376155 |
| no | 591000 | 2213493 | 7050421 | 6471776 | 6476157 |
| nov | 1341 | 3711 | 7466 | 3948 | 3955 |
| nqo | 1489 | 9858 | 23633 | 6056 | 6981 |
| nrm | 4571 | 14279 | 38935 | 33295 | 33321 |
| nso | 7618 | 9505 | 36826 | 35621 | 35623 |
| nv | 21911 | 57663 | 123762 | 107139 | 107139 |
| ny | 1060 | 3164 | 4750 | 1455 | 1490 |
| oc | 85099 | 303185 | 1035051 | 791403 | 792043 |
| olo | 4348 | 14334 | 18704 | 8634 | 8647 |
| om | 1710 | 7496 | 8222 | 4333 | 4416 |
| or | 17027 | 76677 | 137274 | 57023 | 57064 |
| os | 17468 | 40488 | 80943 | 48124 | 48414 |
| pa | 50421 | 226354 | 344239 | 197594 | 198080 |
| pag | 2533 | 41416 | 4150 | 2907 | 2907 |
| pam | 7816 | 16493 | 53785 | 29375 | 29715 |
| pap | 3153 | 12086 | 22157 | 18161 | 18233 |
| pcd | 5272 | 12203 | 15602 | 12319 | 12360 |
| pcm | 1019 | 4631 | 4161 | 1160 | 1261 |
| pdc | 2009 | 5406 | 8151 | 4122 | 4144 |
| pfl | 2717 | 14024 | 26150 | 10291 | 10294 |
| pi | 2972 | 5959 | 7773 | 201 | 201 |
| pih | 829 | 1065 | 2857 | 2016 | 2018 |
| pl | 1468194 | 5599437 | 19364191 | 18389560 | 18405120 |
| pms | 66552 | 170133 | 369956 | 308593 | 314917 |
| pnb | 67534 | 402101 | 937247 | 525105 | 533265 |
| pnt | 497 | 1467 | 3553 | 1715 | 1716 |
| ps | 19254 | 134868 | 72493 | 36348 | 36899 |
| pt | 1048823 | 5226543 | 16811382 | 15714686 | 15714890 |
| pwn | 328 | 1825 | 990 | 428 | 430 |
| qu | 22365 | 47078 | 133032 | 106686 | 106708 |
| rm | 3569 | 27345 | 47169 | 20460 | 20490 |
| rmy | 911 | 2221 | 4235 | 1854 | 1965 |
| rn | 726 | 1641 | 1436 | 594 | 601 |
| ro | 417630 | 1518438 | 4282072 | 3764830 | 3765626 |
| roa_rup | 1270 | 2751 | 4641 | 2527 | 2537 |
| roa_tara | 8407 | 18031 | 42040 | 14330 | 14331 |
| ru | 1889271 | 12344758 | 30796034 | 29268121 | 29288089 |
| rue | 7369 | 21429 | 61022 | 43241 | 43256 |
| rw | 7793 | 35619 | 38066 | 19821 | 20967 |
| sa | 12069 | 78188 | 104193 | 40307 | 41518 |
| sah | 16007 | 76450 | 82154 | 61041 | 61412 |
| sat | 8655 | 43624 | 57493 | 28497 | 28820 |
| sc | 6919 | 24434 | 66719 | 44707 | 44733 |
| scn | 21990 | 49686 | 132583 | 102735 | 102774 |
| sco | 34097 | 86464 | 301450 | 148184 | 148406 |
| sd | 16228 | 48679 | 79392 | 34572 | 35729 |
| se | 6101 | 10531 | 25844 | 17978 | 18010 |
| sg | 473 | 537 | 318 | 184 | 184 |
| sh | 445218 | 1213741 | 4337559 | 3858400 | 3860253 |
| shi | 1650 | 6036 | 10364 | 4715 | 4926 |
| shn | 10653 | 51542 | 46976 | 29925 | 29993 |
| si | 21959 | 132932 | 146935 | 55158 | 56422 |
| simple | 224811 | 618711 | 2014692 | 1689101 | 1689185 |
| sk | 230073 | 845501 | 2867955 | 2468707 | 2469129 |
| skr | 5505 | 62742 | 38412 | 15004 | 21015 |
| sl | 175804 | 810714 | 2597824 | 2067682 | 2068522 |
| sm | 995 | 1591 | 3838 | 2515 | 2523 |
| smn | 5004 | 12483 | 37008 | 22440 | 22492 |
| sn | 10159 | 19527 | 40437 | 31573 | 32763 |
| so | 8540 | 36173 | 53012 | 42913 | 43548 |
| sq | 94941 | 371562 | 699210 | 520709 | 522241 |
| sr | 657766 | 2331205 | 6562651 | 5257496 | 5264077 |
| srn | 1171 | 3050 | 6637 | 1752 | 1941 |
| ss | 783 | 2124 | 2382 | 1127 | 1139 |
| st | 982 | 1971 | 2510 | 1689 | 1701 |
| stq | 3648 | 10972 | 29713 | 15919 | 15920 |
| su | 57552 | 122590 | 496201 | 384518 | 384891 |
| sv | 2418380 | 5019466 | 22263222 | 21445193 | 21445441 |
| sw | 75109 | 218219 | 798980 | 688743 | 692052 |
| szl | 56229 | 109496 | 473528 | 129434 | 129479 |
| szy | 4628 | 49166 | 18867 | 2419 | 3187 |
| ta | 157642 | 780711 | 1642095 | 1141032 | 1142372 |
| tay | 2643 | 15831 | 10104 | 1496 | 5312 |
| tcy | 2135 | 9932 | 11073 | 4680 | 4745 |
| te | 83866 | 719826 | 822054 | 619184 | 622092 |
| tet | 1323 | 3797 | 8047 | 4093 | 4095 |
| tg | 108598 | 279635 | 761826 | 330974 | 331423 |
| th | 153075 | 715083 | 1723394 | 1395935 | 1398891 |
| ti | 388 | 987 | 1191 | 325 | 326 |
| tk | 4739 | 23629 | 18964 | 9717 | 9760 |
| tl | 43388 | 150141 | 447293 | 296084 | 296634 |
| tn | 1090 | 3960 | 3976 | 2008 | 2010 |
| to | 1512 | 2754 | 3542 | 2029 | 2080 |
| tpi | 1278 | 2055 | 3897 | 2193 | 2198 |
| tr | 500435 | 1806253 | 4476004 | 3964449 | 3965589 |
| trv | 1770 | 16650 | 3814 | 504 | 969 |
| ts | 674 | 1798 | 1557 | 903 | 909 |
| tt | 484761 | 1196573 | 2064576 | 1675637 | 1676579 |
| tum | 16778 | 31383 | 57382 | 28399 | 37107 |
| tw | 3568 | 16807 | 15312 | 10912 | 11495 |
| ty | 1175 | 1364 | 1563 | 1095 | 1095 |
| tyv | 3399 | 21968 | 21004 | 5535 | 5557 |
| udm | 5066 | 11432 | 24875 | 17709 | 17715 |
| ug | 8102 | 58982 | 23654 | 12671 | 12874 |
| uk | 522709 | 2867475 | 6800045 | 6445628 | 6451294 |
| ur | 194948 | 676227 | 1870488 | 910419 | 914840 |
| uz | 232879 | 859793 | 1344790 | 1073065 | 1084092 |
| ve | 764 | 1359 | 2524 | 2366 | 2366 |
| vec | 62729 | 98987 | 275972 | 194424 | 194447 |
| vep | 6853 | 43014 | 93864 | 39225 | 39228 |
| vi | 1300753 | 4103594 | 10852870 | 6884928 | 6892519 |
| vls | 7272 | 26374 | 61885 | 49639 | 49653 |
| vo | 32133 | 78015 | 125495 | 101612 | 101629 |
| wa | 11104 | 56305 | 116752 | 79686 | 80037 |
| war | 1158901 | 1342594 | 6654010 | 6009636 | 6009641 |
| wo | 1659 | 7693 | 10828 | 4057 | 4103 |
| wuu | 37170 | 58227 | 121928 | 82184 | 82237 |
| xal | 2008 | 4309 | 4582 | 2112 | 2113 |
| xh | 1502 | 4448 | 6733 | 2128 | 2186 |
| xmf | 19201 | 49944 | 179291 | 21189 | 22041 |
| yi | 14164 | 68937 | 172645 | 116102 | 116325 |
| yo | 29938 | 52231 | 85171 | 46928 | 47346 |
| za | 2388 | 3917 | 7463 | 4613 | 4665 |
| zea | 5445 | 16648 | 36161 | 23532 | 23578 |
| zh | 1310818 | 5501834 | 16397675 | 14380752 | 14421795 |
| zh_classical | 11775 | 44053 | 140340 | 71576 | 71692 |
| zh_min_nan | 425676 | 853753 | 2627115 | 2053956 | 2054838 |
| zh_yue | 121401 | 273459 | 844047 | 683130 | 683226 |
| zu | 10387 | 18211 | 22569 | 20193 | 20238 |
#### Validation
| | Articles | Paragraphs | Anchors | Anchors with QIDs | Anchors with PageIDs |
| :-- | --: | --: | --: | --: | --: |
| ab | 475 | 601 | 1061 | 399 | 399 |
| ace | 2443 | 2668 | 5197 | 2583 | 2587 |
| ady | 142 | 183 | 248 | 150 | 151 |
| af | 27383 | 44157 | 109108 | 100078 | 100123 |
| als | 11998 | 18277 | 44634 | 32874 | 32874 |
| alt | 481 | 827 | 1020 | 621 | 621 |
| am | 3746 | 5234 | 10111 | 5731 | 5756 |
| ami | 749 | 1431 | 744 | 179 | 304 |
| an | 10526 | 13588 | 74808 | 58195 | 58259 |
| ang | 826 | 1099 | 2647 | 1099 | 1102 |
| anp | 504 | 751 | 1698 | 437 | 581 |
| ar | 265368 | 401215 | 1295968 | 1249666 | 1250103 |
| arc | 377 | 418 | 1061 | 610 | 617 |
| ary | 1447 | 1870 | 5702 | 3885 | 3887 |
| arz | 367206 | 410487 | 876531 | 767742 | 768942 |
| as | 5463 | 8589 | 13953 | 7719 | 7732 |
| ast | 48345 | 97904 | 329690 | 197832 | 198042 |
| atj | 399 | 440 | 774 | 406 | 416 |
| av | 719 | 961 | 1918 | 1043 | 1053 |
| avk | 8056 | 9538 | 11816 | 3633 | 3772 |
| awa | 515 | 645 | 721 | 213 | 287 |
| ay | 1391 | 1653 | 2616 | 1481 | 1483 |
| az | 57070 | 88136 | 177151 | 155596 | 155858 |
| azb | 57642 | 64997 | 137053 | 83336 | 83778 |
| ba | 25690 | 43460 | 69052 | 61624 | 61666 |
| ban | 4053 | 4840 | 9581 | 7374 | 7385 |
| bar | 6905 | 9377 | 20546 | 12164 | 12164 |
| bat_smg | 4149 | 4706 | 8787 | 5820 | 5823 |
| bcl | 3355 | 5058 | 8759 | 5080 | 5083 |
| be | 64203 | 91174 | 276525 | 244114 | 244122 |
| bg | 98148 | 148234 | 438687 | 400356 | 401330 |
| bh | 1535 | 1891 | 3464 | 2630 | 2635 |
| bi | 154 | 159 | 251 | 151 | 151 |
| bjn | 1764 | 2166 | 6458 | 3694 | 3775 |
| blk | 887 | 1374 | 1538 | 821 | 839 |
| bm | 196 | 272 | 317 | 146 | 146 |
| bn | 50495 | 81841 | 169097 | 128508 | 128609 |
| bo | 2198 | 4079 | 934 | 746 | 752 |
| bpy | 10057 | 12879 | 18710 | 9693 | 9693 |
| br | 18687 | 23734 | 73278 | 59024 | 59056 |
| bs | 28533 | 42574 | 138483 | 107760 | 107846 |
| bug | 1636 | 1655 | 6141 | 1682 | 1731 |
| bxr | 754 | 1003 | 2930 | 1211 | 1211 |
| ca | 251952 | 399403 | 1265187 | 1140208 | 1140359 |
| cbk_zam | 460 | 932 | 1040 | 268 | 272 |
| cdo | 2953 | 3237 | 6938 | 3273 | 3281 |
| ce | 197899 | 234617 | 341843 | 166126 | 166206 |
| ceb | 1221405 | 1324624 | 4218179 | 3742385 | 3773844 |
| ch | 123 | 131 | 239 | 64 | 73 |
| chr | 124 | 134 | 175 | 100 | 100 |
| chy | 67 | 67 | 47 | 42 | 42 |
| ckb | 13511 | 18279 | 48490 | 25365 | 25540 |
| co | 1723 | 2587 | 5286 | 2729 | 2737 |
| cr | 22 | 23 | 22 | 13 | 13 |
| crh | 2978 | 3246 | 11005 | 7899 | 7899 |
| cs | 189136 | 297000 | 1101343 | 974485 | 974505 |
| csb | 1307 | 1533 | 3341 | 1851 | 1851 |
| cu | 250 | 275 | 540 | 229 | 229 |
| cv | 14374 | 17462 | 42486 | 19049 | 19114 |
| cy | 89897 | 110225 | 222476 | 177842 | 178698 |
| da | 87765 | 129990 | 482701 | 427333 | 427374 |
| dag | 2215 | 3237 | 4935 | 1169 | 1498 |
| de | 1120553 | 1788057 | 5831103 | 5607963 | 5607963 |
| din | 149 | 177 | 128 | 15 | 15 |
| diq | 6660 | 7883 | 17684 | 15853 | 15861 |
| dsb | 781 | 1032 | 2476 | 1301 | 1301 |
| dty | 554 | 659 | 861 | 480 | 483 |
| dv | 1227 | 1898 | 870 | 406 | 406 |
| dz | 215 | 303 | 21 | 8 | 8 |
| ee | 203 | 242 | 183 | 66 | 74 |
| el | 99725 | 169395 | 461747 | 344216 | 344456 |
| eml | 4387 | 6114 | 13938 | 8193 | 8214 |
| en | 2503257 | 4516442 | 12185882 | 11974436 | 11975194 |
| eo | 90949 | 123848 | 474727 | 442357 | 442772 |
| es | 701171 | 1209944 | 3752765 | 3514968 | 3522213 |
| et | 80911 | 123354 | 395877 | 319773 | 320587 |
| eu | 104388 | 156552 | 378553 | 337331 | 337944 |
| ext | 804 | 1045 | 2269 | 1344 | 1345 |
| fa | 191532 | 262121 | 688824 | 652200 | 653219 |
| fat | 446 | 709 | 214 | 3 | 97 |
| ff | 361 | 459 | 378 | 222 | 234 |
| fi | 123327 | 184244 | 576163 | 514419 | 514915 |
| fiu_vro | 1738 | 2263 | 4622 | 2623 | 2628 |
| fj | 168 | 213 | 604 | 214 | 214 |
| fo | 2625 | 3398 | 13383 | 10599 | 10617 |
| fr | 954388 | 1695419 | 4847588 | 4738268 | 4740047 |
| frp | 1018 | 1181 | 4089 | 2862 | 2862 |
| frr | 2968 | 3419 | 9609 | 7996 | 8011 |
| fur | 884 | 1168 | 3225 | 1833 | 1839 |
| fy | 15980 | 22974 | 139530 | 108300 | 108337 |
| ga | 10781 | 14493 | 38848 | 32343 | 32352 |
| gag | 440 | 551 | 961 | 465 | 465 |
| gan | 731 | 1045 | 2071 | 1536 | 1537 |
| gcr | 480 | 567 | 297 | 122 | 122 |
| gd | 4393 | 5296 | 15544 | 10458 | 10458 |
| gl | 62030 | 101112 | 407821 | 325854 | 325960 |
| glk | 1383 | 1747 | 3723 | 2435 | 2443 |
| gn | 1164 | 1728 | 4751 | 3521 | 3528 |
| gom | 2106 | 4116 | 1511 | 251 | 251 |
| gor | 2844 | 3082 | 11826 | 7315 | 7411 |
| got | 216 | 245 | 514 | 190 | 190 |
| gpe | 265 | 355 | 93 | 71 | 73 |
| gu | 8437 | 13008 | 50956 | 38242 | 38251 |
| guc | 198 | 279 | 312 | 141 | 162 |
| gur | 369 | 565 | 145 | 25 | 27 |
| guw | 332 | 393 | 827 | 313 | 616 |
| gv | 957 | 1324 | 5652 | 2252 | 2253 |
| ha | 10666 | 16571 | 12853 | 10862 | 10993 |
| hak | 1179 | 1302 | 4628 | 3155 | 3155 |
| haw | 541 | 650 | 1238 | 616 | 618 |
| he | 165541 | 295188 | 1213939 | 1153986 | 1155384 |
| hi | 36229 | 60184 | 108382 | 89102 | 89340 |
| hif | 2107 | 2369 | 5015 | 2648 | 2680 |
| hr | 62673 | 97103 | 354392 | 304964 | 305664 |
| hsb | 3599 | 4379 | 10001 | 7239 | 7240 |
| ht | 14693 | 17294 | 23011 | 18721 | 18928 |
| hu | 125438 | 206546 | 586091 | 523501 | 523814 |
| hy | 113060 | 171415 | 418503 | 298111 | 298292 |
| hyw | 5310 | 9207 | 17616 | 8842 | 9168 |
| ia | 4021 | 4850 | 14972 | 11257 | 11263 |
| id | 158648 | 237793 | 734148 | 627764 | 629525 |
| ie | 2213 | 2523 | 6750 | 5036 | 5046 |
| ig | 7944 | 12354 | 6464 | 3466 | 3493 |
| ik | 100 | 118 | 120 | 64 | 71 |
| ilo | 4096 | 8297 | 14183 | 8609 | 8609 |
| inh | 399 | 494 | 1298 | 626 | 645 |
| io | 8868 | 11368 | 33682 | 28744 | 28748 |
| is | 13573 | 18566 | 62576 | 47263 | 47360 |
| it | 584902 | 968880 | 3050620 | 2902006 | 2903047 |
| iu | 61 | 62 | 48 | 29 | 29 |
| ja | 573457 | 1032568 | 3222875 | 3083301 | 3088604 |
| jam | 249 | 274 | 623 | 399 | 399 |
| jbo | 270 | 321 | 562 | 56 | 56 |
| jv | 13108 | 16457 | 60143 | 42112 | 42148 |
| ka | 53071 | 76961 | 252383 | 46974 | 46975 |
| kaa | 775 | 1071 | 1476 | 669 | 717 |
| kab | 1269 | 1685 | 4050 | 2397 | 2403 |
| kbd | 474 | 663 | 1482 | 537 | 537 |
| kbp | 535 | 656 | 835 | 810 | 811 |
| kcg | 190 | 223 | 311 | 196 | 197 |
| kg | 187 | 213 | 420 | 260 | 260 |
| ki | 273 | 333 | 248 | 169 | 206 |
| kk | 76635 | 99268 | 204324 | 126732 | 127677 |
| kl | 97 | 129 | 162 | 43 | 43 |
| km | 3844 | 9340 | 12192 | 4524 | 4583 |
| kn | 14217 | 29387 | 48402 | 20992 | 21022 |
| ko | 154713 | 239887 | 689906 | 633527 | 634725 |
| koi | 682 | 1010 | 1815 | 1144 | 1144 |
| krc | 423 | 698 | 2022 | 841 | 846 |
| ks | 888 | 1006 | 1692 | 645 | 670 |
| ksh | 918 | 1156 | 2951 | 1053 | 1055 |
| ku | 10060 | 12771 | 29766 | 23050 | 23232 |
| kv | 1105 | 1456 | 3365 | 2787 | 2787 |
| kw | 1820 | 2171 | 5570 | 3076 | 3082 |
| ky | 16655 | 21571 | 31213 | 21712 | 21757 |
| la | 22397 | 26732 | 161732 | 142447 | 142486 |
| lad | 961 | 1286 | 3984 | 2056 | 2056 |
| lb | 15385 | 19667 | 60568 | 46664 | 46730 |
| lbe | 207 | 232 | 488 | 290 | 290 |
| lez | 1184 | 1764 | 3829 | 2760 | 2760 |
| lfn | 1455 | 2435 | 3328 | 1602 | 1604 |
| lg | 1272 | 2650 | 1795 | 239 | 305 |
| li | 4501 | 6650 | 24213 | 15790 | 15826 |
| lij | 1781 | 2607 | 6658 | 3933 | 3933 |
| lld | 17293 | 17539 | 64059 | 49327 | 50864 |
| lmo | 12641 | 14976 | 40217 | 29874 | 29946 |
| ln | 585 | 692 | 1321 | 996 | 997 |
| lo | 1144 | 1680 | 3023 | 991 | 1013 |
| lt | 62652 | 85962 | 300456 | 269264 | 270227 |
| ltg | 289 | 341 | 686 | 285 | 285 |
| lv | 34742 | 48371 | 160433 | 136594 | 136873 |
| mad | 284 | 381 | 439 | 135 | 136 |
| mai | 2184 | 2499 | 5878 | 4209 | 4212 |
| map_bms | 1539 | 1847 | 7486 | 5705 | 5705 |
| mdf | 1086 | 1244 | 2512 | 1077 | 1077 |
| mg | 20361 | 23650 | 36313 | 29821 | 29974 |
| mhr | 2863 | 3594 | 6538 | 4114 | 4122 |
| mi | 1078 | 1154 | 3214 | 2743 | 2776 |
| min | 42987 | 46277 | 143692 | 55809 | 56077 |
| mk | 46235 | 76890 | 219310 | 180884 | 181042 |
| ml | 31116 | 46345 | 88976 | 53726 | 53818 |
| mn | 8485 | 13887 | 32271 | 15330 | 15455 |
| mni | 1843 | 2102 | 3418 | 2183 | 2325 |
| mnw | 1284 | 3750 | 897 | 202 | 224 |
| mr | 26803 | 36202 | 70510 | 43103 | 44352 |
| mrj | 2062 | 2297 | 5627 | 2888 | 2888 |
| ms | 75473 | 110077 | 270064 | 215280 | 215811 |
| mt | 2516 | 5510 | 11680 | 5760 | 5761 |
| mwl | 1828 | 4316 | 15365 | 3216 | 3287 |
| my | 24005 | 37165 | 49321 | 33223 | 33518 |
| myv | 1732 | 2327 | 4094 | 2923 | 2925 |
| mzn | 3784 | 4409 | 9938 | 5199 | 5205 |
| nah | 1128 | 1314 | 3316 | 1418 | 1556 |
| nap | 2047 | 2473 | 4579 | 2249 | 2249 |
| nds | 20646 | 26845 | 65355 | 34090 | 34094 |
| nds_nl | 2127 | 3063 | 10188 | 5585 | 5587 |
| ne | 6956 | 10087 | 16847 | 13502 | 13536 |
| new | 22645 | 27233 | 50860 | 32165 | 32217 |
| nia | 312 | 430 | 512 | 277 | 329 |
| nl | 490380 | 651743 | 1994062 | 1874588 | 1875259 |
| nn | 44180 | 60918 | 194747 | 153072 | 153140 |
| no | 172653 | 245377 | 779775 | 715618 | 716153 |
| nov | 339 | 410 | 861 | 452 | 452 |
| nqo | 583 | 1037 | 2598 | 704 | 813 |
| nrm | 1318 | 1600 | 4276 | 3734 | 3736 |
| nso | 960 | 1038 | 4242 | 4119 | 4119 |
| nv | 5649 | 6281 | 13652 | 11768 | 11768 |
| ny | 236 | 318 | 392 | 126 | 126 |
| oc | 23067 | 33775 | 115155 | 87980 | 88063 |
| olo | 1273 | 1598 | 2162 | 997 | 998 |
| om | 401 | 830 | 891 | 401 | 412 |
| or | 6261 | 8669 | 16120 | 6752 | 6757 |
| os | 3923 | 4535 | 9130 | 5470 | 5524 |
| pa | 17242 | 24844 | 37813 | 21759 | 21812 |
| pag | 1602 | 4519 | 404 | 300 | 300 |
| pam | 1509 | 1831 | 6019 | 3230 | 3272 |
| pap | 773 | 1376 | 2526 | 2042 | 2056 |
| pcd | 1089 | 1361 | 1803 | 1334 | 1338 |
| pcm | 353 | 542 | 409 | 128 | 139 |
| pdc | 370 | 565 | 839 | 424 | 429 |
| pfl | 1113 | 1500 | 2861 | 1070 | 1070 |
| pi | 578 | 682 | 881 | 26 | 26 |
| pih | 118 | 125 | 317 | 217 | 218 |
| pl | 444095 | 621669 | 2149058 | 2041686 | 2043400 |
| pms | 16530 | 19186 | 41547 | 34783 | 35474 |
| pnb | 21586 | 44654 | 103992 | 58461 | 59380 |
| pnt | 147 | 172 | 389 | 177 | 178 |
| ps | 7566 | 14922 | 8427 | 4108 | 4187 |
| pt | 349931 | 580790 | 1868210 | 1745832 | 1745858 |
| pwn | 103 | 166 | 85 | 31 | 31 |
| qu | 4540 | 5211 | 14781 | 11746 | 11750 |
| rm | 1076 | 3100 | 5539 | 2293 | 2298 |
| rmy | 214 | 235 | 446 | 176 | 184 |
| rn | 125 | 172 | 124 | 53 | 53 |
| ro | 106169 | 168972 | 473512 | 416263 | 416347 |
| roa_rup | 214 | 290 | 458 | 254 | 254 |
| roa_tara | 1278 | 1979 | 4455 | 1534 | 1534 |
| ru | 806592 | 1369860 | 3416036 | 3245837 | 3247963 |
| rue | 2022 | 2513 | 7023 | 5064 | 5066 |
| rw | 2577 | 3925 | 4139 | 2223 | 2349 |
| sa | 4344 | 8607 | 11313 | 4249 | 4391 |
| sah | 4729 | 8472 | 9040 | 6623 | 6660 |
| sat | 3485 | 4960 | 6473 | 3225 | 3278 |
| sc | 1900 | 2807 | 7641 | 5096 | 5098 |
| scn | 4263 | 5604 | 14333 | 11167 | 11171 |
| sco | 7382 | 9639 | 33771 | 16432 | 16453 |
| sd | 3970 | 5499 | 8879 | 3804 | 3925 |
| se | 982 | 1149 | 2841 | 1958 | 1958 |
| sg | 67 | 72 | 36 | 24 | 24 |
| sh | 103283 | 135121 | 484459 | 429555 | 429770 |
| shi | 477 | 679 | 1144 | 545 | 570 |
| shn | 3633 | 5630 | 5456 | 3627 | 3639 |
| si | 7672 | 14760 | 16443 | 6215 | 6346 |
| simple | 52503 | 68765 | 224811 | 187586 | 187598 |
| sk | 67520 | 93957 | 317232 | 272711 | 272779 |
| skr | 2090 | 6926 | 4136 | 1683 | 2359 |
| sl | 55621 | 89740 | 285769 | 228421 | 228530 |
| sm | 153 | 171 | 485 | 297 | 297 |
| smn | 1163 | 1420 | 4517 | 2681 | 2688 |
| sn | 1896 | 2139 | 4351 | 3384 | 3529 |
| so | 2358 | 4032 | 6064 | 5027 | 5083 |
| sq | 25223 | 41621 | 79295 | 59156 | 59350 |
| sr | 177997 | 258455 | 728755 | 584663 | 585394 |
| srn | 281 | 342 | 796 | 205 | 225 |
| ss | 188 | 259 | 265 | 125 | 125 |
| st | 157 | 198 | 248 | 164 | 166 |
| stq | 804 | 1162 | 3150 | 1816 | 1816 |
| su | 10348 | 13687 | 55055 | 42915 | 42944 |
| sv | 467467 | 558522 | 2473790 | 2382576 | 2382608 |
| sw | 18014 | 24348 | 90302 | 77817 | 78145 |
| szl | 11292 | 12173 | 52459 | 14419 | 14424 |
| szy | 2391 | 5418 | 2042 | 235 | 285 |
| ta | 59923 | 87114 | 183399 | 126977 | 127148 |
| tay | 1192 | 1757 | 1101 | 175 | 591 |
| tcy | 769 | 1077 | 1089 | 464 | 465 |
| te | 43790 | 79667 | 91327 | 69148 | 69484 |
| tet | 294 | 412 | 871 | 471 | 471 |
| tg | 27060 | 31599 | 86180 | 37522 | 37561 |
| th | 49169 | 78814 | 189768 | 154097 | 154453 |
| ti | 87 | 99 | 89 | 22 | 22 |
| tk | 1328 | 2612 | 2116 | 1056 | 1062 |
| tl | 11731 | 16623 | 49726 | 32858 | 32914 |
| tn | 296 | 424 | 477 | 278 | 278 |
| to | 254 | 277 | 393 | 230 | 233 |
| tpi | 180 | 207 | 394 | 216 | 217 |
| tr | 134938 | 200972 | 496960 | 440639 | 440790 |
| trv | 807 | 1814 | 400 | 53 | 98 |
| ts | 155 | 203 | 219 | 132 | 132 |
| tt | 113689 | 132676 | 228544 | 185563 | 185662 |
| tum | 2188 | 3516 | 6442 | 3105 | 4083 |
| tw | 1249 | 1885 | 1729 | 1217 | 1291 |
| ty | 162 | 167 | 215 | 143 | 143 |
| tyv | 1494 | 2486 | 2342 | 611 | 617 |
| udm | 1036 | 1240 | 2781 | 1957 | 1957 |
| ug | 2629 | 6556 | 2657 | 1479 | 1493 |
| uk | 203057 | 318240 | 758049 | 718278 | 718908 |
| ur | 54784 | 75152 | 206169 | 99493 | 100041 |
| uz | 65767 | 95465 | 149763 | 119192 | 120519 |
| ve | 128 | 148 | 256 | 229 | 229 |
| vec | 9463 | 11242 | 32188 | 22525 | 22531 |
| vep | 3225 | 4804 | 10375 | 4295 | 4295 |
| vi | 330763 | 455933 | 1211343 | 768936 | 769829 |
| vls | 2189 | 2904 | 7133 | 5776 | 5777 |
| vo | 7308 | 8647 | 13902 | 11270 | 11273 |
| wa | 4457 | 6269 | 12736 | 8751 | 8794 |
| war | 146537 | 149236 | 738087 | 666983 | 666983 |
| wo | 516 | 864 | 1083 | 404 | 414 |
| wuu | 5530 | 6448 | 13732 | 9168 | 9171 |
| xal | 407 | 449 | 549 | 308 | 308 |
| xh | 399 | 550 | 804 | 284 | 293 |
| xmf | 4516 | 5414 | 19437 | 2342 | 2447 |
| yi | 5260 | 7563 | 18821 | 12493 | 12510 |
| yo | 4431 | 5855 | 9761 | 5361 | 5410 |
| za | 335 | 414 | 777 | 457 | 458 |
| zea | 1470 | 1847 | 3682 | 2569 | 2574 |
| zh | 389361 | 611537 | 1817382 | 1592929 | 1597686 |
| zh_classical | 3601 | 4995 | 15834 | 8157 | 8170 |
| zh_min_nan | 87849 | 94529 | 291330 | 227978 | 228083 |
| zh_yue | 23579 | 30146 | 92720 | 75081 | 75096 |
| zu | 1646 | 2050 | 2518 | 2228 | 2234 |
**NOTE:** The number of articles in the tables above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.
## Additional Information
### Licensing Information
The WikiAnc dataset is given under the [Creative Commons Attribution ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license.
|
cyanic-selkie/wikianc
|
[
"task_categories:token-classification",
"annotations_creators:machine-generated",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"language:en",
"language:ceb",
"language:de",
"language:sv",
"language:fr",
"language:nl",
"language:ru",
"language:es",
"language:it",
"language:arz",
"language:pl",
"language:ja",
"language:zh",
"language:vi",
"language:uk",
"language:war",
"language:ar",
"language:pt",
"language:fa",
"language:ca",
"language:sr",
"language:id",
"language:ko",
"language:no",
"language:ce",
"language:fi",
"language:cs",
"language:tr",
"language:hu",
"language:tt",
"language:sh",
"language:ro",
"language:eu",
"language:ms",
"language:eo",
"language:he",
"language:hy",
"language:da",
"language:bg",
"language:cy",
"language:sk",
"language:azb",
"language:uz",
"language:et",
"language:be",
"language:kk",
"language:min",
"language:el",
"language:hr",
"language:lt",
"language:gl",
"language:az",
"language:ur",
"language:sl",
"language:lld",
"language:ka",
"language:nn",
"language:hi",
"language:th",
"language:ta",
"language:bn",
"language:la",
"language:mk",
"language:ast",
"language:lv",
"language:af",
"language:tg",
"language:my",
"language:mg",
"language:mr",
"language:sq",
"language:bs",
"language:oc",
"language:te",
"language:ml",
"language:nds",
"language:br",
"language:ky",
"language:sw",
"language:jv",
"language:lmo",
"language:new",
"language:pnb",
"language:vec",
"language:ht",
"language:pms",
"language:ba",
"language:lb",
"language:su",
"language:ku",
"language:ga",
"language:szl",
"language:is",
"language:fy",
"language:cv",
"language:ckb",
"language:pa",
"language:tl",
"language:an",
"language:wuu",
"language:diq",
"language:io",
"language:sco",
"language:vo",
"language:yo",
"language:ne",
"language:ia",
"language:kn",
"language:gu",
"language:als",
"language:ha",
"language:avk",
"language:bar",
"language:crh",
"language:scn",
"language:bpy",
"language:qu",
"language:mn",
"language:nv",
"language:xmf",
"language:ban",
"language:si",
"language:tum",
"language:ps",
"language:ig",
"language:frr",
"language:os",
"language:mzn",
"language:or",
"language:sah",
"language:cdo",
"language:gd",
"language:bug",
"language:yi",
"language:sd",
"language:ilo",
"language:am",
"language:nap",
"language:li",
"language:bcl",
"language:fo",
"language:gor",
"language:hsb",
"language:mai",
"language:shn",
"language:eml",
"language:ace",
"language:sa",
"language:as",
"language:wa",
"language:ie",
"language:hyw",
"language:lij",
"language:mhr",
"language:zu",
"language:sn",
"language:hif",
"language:mrj",
"language:bjn",
"language:km",
"language:mni",
"language:hak",
"language:pam",
"language:sat",
"language:rue",
"language:nso",
"language:bh",
"language:so",
"language:mi",
"language:se",
"language:myv",
"language:vls",
"language:dag",
"language:sc",
"language:co",
"language:ary",
"language:kw",
"language:bo",
"language:vep",
"language:glk",
"language:tk",
"language:kab",
"language:gan",
"language:rw",
"language:ab",
"language:gv",
"language:ug",
"language:nah",
"language:zea",
"language:skr",
"language:frp",
"language:udm",
"language:pcd",
"language:mt",
"language:kv",
"language:csb",
"language:gn",
"language:smn",
"language:ay",
"language:nrm",
"language:ks",
"language:lez",
"language:lfn",
"language:olo",
"language:mwl",
"language:lo",
"language:stq",
"language:ang",
"language:mdf",
"language:fur",
"language:rm",
"language:lad",
"language:kaa",
"language:gom",
"language:ext",
"language:koi",
"language:tyv",
"language:pap",
"language:av",
"language:dsb",
"language:ln",
"language:dty",
"language:tw",
"language:dv",
"language:ksh",
"language:za",
"language:gag",
"language:bxr",
"language:pfl",
"language:lg",
"language:szy",
"language:pag",
"language:blk",
"language:pi",
"language:tay",
"language:haw",
"language:awa",
"language:inh",
"language:krc",
"language:xal",
"language:pdc",
"language:to",
"language:atj",
"language:tcy",
"language:arc",
"language:mnw",
"language:shi",
"language:jam",
"language:kbp",
"language:wo",
"language:anp",
"language:kbd",
"language:nia",
"language:om",
"language:nov",
"language:ki",
"language:nqo",
"language:bi",
"language:xh",
"language:tpi",
"language:ff",
"language:tet",
"language:jbo",
"language:fj",
"language:kg",
"language:lbe",
"language:ty",
"language:cu",
"language:guw",
"language:trv",
"language:ami",
"language:srn",
"language:sm",
"language:mad",
"language:alt",
"language:ltg",
"language:gcr",
"language:chr",
"language:tn",
"language:ny",
"language:st",
"language:pih",
"language:got",
"language:rmy",
"language:ee",
"language:pcm",
"language:bm",
"language:ss",
"language:gpe",
"language:ts",
"language:ve",
"language:kcg",
"language:chy",
"language:rn",
"language:ch",
"language:gur",
"language:ik",
"language:ady",
"language:fat",
"language:pnt",
"language:guc",
"language:iu",
"language:pwn",
"language:sg",
"language:din",
"language:ti",
"language:kl",
"language:dz",
"language:cr",
"license:cc-by-sa-4.0",
"wikidata",
"wikipedia",
"wikification",
"named-entity-linking",
"nel",
"entity-linking",
"el",
"named-entity-disambiguation",
"ned",
"entity-disambiguation",
"ed",
"region:us"
] |
2023-09-05T13:22:32+00:00
|
{"annotations_creators": ["machine-generated", "crowdsourced"], "language_creators": ["machine-generated", "crowdsourced"], "language": ["en", "ceb", "de", "sv", "fr", "nl", "ru", "es", "it", "arz", "pl", "ja", "zh", "vi", "uk", "war", "ar", "pt", "fa", "ca", "sr", "id", "ko", "no", "ce", "fi", "cs", "tr", "hu", "tt", "sh", "ro", "eu", "ms", "eo", "he", "hy", "da", "bg", "cy", "sk", "azb", "uz", "et", "be", "kk", "min", "el", "hr", "lt", "gl", "az", "ur", "sl", "lld", "ka", "nn", "hi", "th", "ta", "bn", "la", "mk", "ast", "lv", "af", "tg", "my", "mg", "mr", "sq", "bs", "oc", "te", "ml", "nds", "br", "ky", "sw", "jv", "lmo", "new", "pnb", "vec", "ht", "pms", "ba", "lb", "su", "ku", "ga", "szl", "is", "fy", "cv", "ckb", "pa", "tl", "an", "wuu", "diq", "io", "sco", "vo", "yo", "ne", "ia", "kn", "gu", "als", "ha", "avk", "bar", "crh", "scn", "bpy", "qu", "mn", "nv", "xmf", "ban", "si", "tum", "ps", "ig", "frr", "os", "mzn", "or", "sah", "cdo", "gd", "bug", "yi", "sd", "ilo", "am", "nap", "li", "bcl", "fo", "gor", "hsb", "mai", "shn", "eml", "ace", "sa", "as", "wa", "ie", "hyw", "lij", "mhr", "zu", "sn", "hif", "mrj", "bjn", "km", "mni", "hak", "pam", "sat", "rue", "nso", "bh", "so", "mi", "se", "myv", "vls", "dag", "sc", "co", "ary", "kw", "bo", "vep", "glk", "tk", "kab", "gan", "rw", "ab", "gv", "ug", "nah", "zea", "skr", "frp", "udm", "pcd", "mt", "kv", "csb", "gn", "smn", "ay", "nrm", "ks", "lez", "lfn", "olo", "mwl", "lo", "stq", "ang", "mdf", "fur", "rm", "lad", "kaa", "gom", "ext", "koi", "tyv", "pap", "av", "dsb", "ln", "dty", "tw", "dv", "ksh", "za", "gag", "bxr", "pfl", "lg", "szy", "pag", "blk", "pi", "tay", "haw", "awa", "inh", "krc", "xal", "pdc", "to", "atj", "tcy", "arc", "mnw", "shi", "jam", "kbp", "wo", "anp", "kbd", "nia", "om", "nov", "ki", "nqo", "bi", "xh", "tpi", "ff", "tet", "jbo", "fj", "kg", "lbe", "ty", "cu", "guw", "trv", "ami", "srn", "sm", "mad", "alt", "ltg", "gcr", "chr", "tn", "ny", "st", "pih", "got", "rmy", "ee", "pcm", "bm", "ss", "gpe", "ts", "ve", "kcg", "chy", "rn", "ch", "gur", "ik", "ady", "fat", "pnt", "guc", "iu", "pwn", "sg", "din", "ti", "kl", "dz", "cr"], "license": "cc-by-sa-4.0", "multilinguality": ["multilingual"], "task_categories": ["token-classification"], "pretty_name": "WikiAnc", "tags": ["wikidata", "wikipedia", "wikification", "named-entity-linking", "nel", "entity-linking", "el", "named-entity-disambiguation", "ned", "entity-disambiguation", "ed"], "configs": [{"config_name": "ab", "data_files": [{"split": "train", "path": "data/ab/train.parquet"}, {"split": "validation", "path": "data/ab/validation.parquet"}]}, {"config_name": "ace", "data_files": [{"split": "train", "path": "data/ace/train.parquet"}, {"split": "validation", "path": "data/ace/validation.parquet"}]}, {"config_name": "ady", "data_files": [{"split": "train", "path": "data/ady/train.parquet"}, {"split": "validation", "path": "data/ady/validation.parquet"}]}, {"config_name": "af", "data_files": [{"split": "train", "path": 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"data/za/train.parquet"}, {"split": "validation", "path": "data/za/validation.parquet"}]}, {"config_name": "zea", "data_files": [{"split": "train", "path": "data/zea/train.parquet"}, {"split": "validation", "path": "data/zea/validation.parquet"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "data/zh/train.parquet"}, {"split": "validation", "path": "data/zh/validation.parquet"}]}, {"config_name": "zh_classical", "data_files": [{"split": "train", "path": "data/zh_classical/train.parquet"}, {"split": "validation", "path": "data/zh_classical/validation.parquet"}]}, {"config_name": "zh_min_nan", "data_files": [{"split": "train", "path": "data/zh_min_nan/train.parquet"}, {"split": "validation", "path": "data/zh_min_nan/validation.parquet"}]}, {"config_name": "zh_yue", "data_files": [{"split": "train", "path": "data/zh_yue/train.parquet"}, {"split": "validation", "path": "data/zh_yue/validation.parquet"}]}, {"config_name": "zu", "data_files": [{"split": "train", "path": "data/zu/train.parquet"}, {"split": "validation", "path": "data/zu/validation.parquet"}]}]}
|
2023-09-05T13:22:32+00:00
|
[] |
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] |
TAGS
#task_categories-token-classification #annotations_creators-machine-generated #annotations_creators-crowdsourced #language_creators-machine-generated #language_creators-crowdsourced #multilinguality-multilingual #language-English #language-Cebuano #language-German #language-Swedish #language-French #language-Dutch #language-Russian #language-Spanish #language-Italian #language-Egyptian Arabic #language-Polish #language-Japanese #language-Chinese #language-Vietnamese #language-Ukrainian #language-Waray (Philippines) #language-Arabic #language-Portuguese #language-Persian #language-Catalan #language-Serbian #language-Indonesian #language-Korean #language-Norwegian #language-Chechen #language-Finnish #language-Czech #language-Turkish #language-Hungarian #language-Tatar #language-Serbo-Croatian #language-Romanian #language-Basque #language-Malay (macrolanguage) #language-Esperanto #language-Hebrew #language-Armenian #language-Danish #language-Bulgarian #language-Welsh #language-Slovak #language-South Azerbaijani #language-Uzbek #language-Estonian #language-Belarusian #language-Kazakh #language-Minangkabau #language-Modern Greek (1453-) #language-Croatian #language-Lithuanian #language-Galician #language-Azerbaijani #language-Urdu #language-Slovenian #language-Ladin #language-Georgian #language-Norwegian Nynorsk #language-Hindi #language-Thai #language-Tamil #language-Bengali #language-Latin #language-Macedonian #language-Asturian #language-Latvian #language-Afrikaans #language-Tajik #language-Burmese #language-Malagasy #language-Marathi #language-Albanian #language-Bosnian #language-Occitan (post 1500) #language-Telugu #language-Malayalam #language-Low German #language-Breton #language-Kirghiz #language-Swahili (macrolanguage) #language-Javanese #language-Lombard #language-Newari #language-Western Panjabi #language-Venetian #language-Haitian #language-Piemontese #language-Bashkir #language-Luxembourgish #language-Sundanese #language-Kurdish #language-Irish #language-Silesian #language-Icelandic #language-Western Frisian #language-Chuvash #language-Central Kurdish #language-Panjabi #language-Tagalog #language-Aragonese #language-Wu Chinese #language-Dimli (individual language) #language-Ido #language-Scots #language-Volapük #language-Yoruba #language-Nepali (macrolanguage) #language-Interlingua (International Auxiliary Language Association) #language-Kannada #language-Gujarati #language-Tosk Albanian #language-Hausa #language-Kotava #language-Bavarian #language-Crimean Tatar #language-Sicilian #language-Bishnupriya #language-Quechua #language-Mongolian #language-Navajo #language-Mingrelian #language-Balinese #language-Sinhala #language-Tumbuka #language-Pushto #language-Igbo #language-Northern Frisian #language-Ossetian #language-Mazanderani #language-Oriya (macrolanguage) #language-Yakut #language-Min Dong Chinese #language-Scottish Gaelic #language-Buginese #language-Yiddish #language-Sindhi #language-Iloko #language-Amharic #language-Neapolitan #language-Limburgan #language-Central Bikol #language-Faroese #language-Gorontalo #language-Upper Sorbian #language-Maithili #language-Shan #language-Emiliano-Romagnolo #language-Achinese #language-Sanskrit #language-Assamese #language-Walloon #language-Interlingue #language-Western Armenian #language-Ligurian #language-Eastern Mari #language-Zulu #language-Shona #language-Fiji Hindi #language-Western Mari #language-Banjar #language-Khmer #language-Manipuri #language-Hakka Chinese #language-Pampanga #language-Santali #language-Rusyn #language-Pedi #language-bh #language-Somali #language-Maori #language-Northern Sami #language-Erzya #language-Vlaams #language-Dagbani #language-Sardinian #language-Corsican #language-Moroccan Arabic #language-Cornish #language-Tibetan #language-Veps #language-Gilaki #language-Turkmen #language-Kabyle #language-Gan Chinese #language-Kinyarwanda #language-Abkhazian #language-Manx #language-Uighur #language-nah #language-Zeeuws 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#language-Venda #language-Tyap #language-Cheyenne #language-Rundi #language-Chamorro #language-Farefare #language-Inupiaq #language-Adyghe #language-Fanti #language-Pontic #language-Wayuu #language-Inuktitut #language-Paiwan #language-Sango #language-Dinka #language-Tigrinya #language-Kalaallisut #language-Dzongkha #language-Cree #license-cc-by-sa-4.0 #wikidata #wikipedia #wikification #named-entity-linking #nel #entity-linking #el #named-entity-disambiguation #ned #entity-disambiguation #ed #region-us
|
Dataset Card for WikiAnc
========================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Additional Information
+ Licensing Information
Dataset Description
-------------------
* Repository: WikiAnc repository
### Dataset Summary
The WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023).
The code for generating the dataset can be found here.
### Supported Tasks
* 'wikificiation': The dataset can be used to train a model for Wikification.
* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.
### Languages
The text in the dataset is in all 320 Wikipedia languages. The full list can be found in the table below.
Dataset Structure
-----------------
### Data Instances
A typical data point represents a paragraph in a Wikipedia article.
The 'paragraph\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.
The 'paragraph\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.
Additionally, each paragraph has 'article\_title', 'article\_pageid', and (nullable) 'article\_qid' fields referring to the article the paragraph came from.
There is also a nullable, NFC normalized, UTF-8 encoded 'section\_heading' field, and an integer 'section\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.
The 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).
NOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.
An example from the WikiAnc EN test set looks as follows:
### Data Fields
* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example
* 'article\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores
* 'article\_pageid': an integer representing the Wikipedia pageID of the article
* 'article\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset
* 'section\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading
* 'section\_level': an integer representing the level of the section in the section hierarchy
* 'paragraph\_text': an NFC normalized, UTF-8 encoded string representing the paragraph
* 'paragraph\_anchors': a list of structs representing anchors, each anchor has:
+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors
+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor
+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset
+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset
+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article
### Data Splits
The data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:
#### Train
#### Validation
NOTE: The number of articles in the tables above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.
Additional Information
----------------------
### Licensing Information
The WikiAnc dataset is given under the Creative Commons Attribution ShareAlike 4.0 International license.
|
[
"### Dataset Summary\n\n\nThe WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023).\n\n\nThe code for generating the dataset can be found here.",
"### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.\n* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.",
"### Languages\n\n\nThe text in the dataset is in all 320 Wikipedia languages. The full list can be found in the table below.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc EN test set looks as follows:",
"### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article",
"### Data Splits\n\n\nThe data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:",
"#### Train",
"#### Validation\n\n\n\nNOTE: The number of articles in the tables above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe WikiAnc dataset is given under the Creative Commons Attribution ShareAlike 4.0 International license."
] |
[
"TAGS\n#task_categories-token-classification #annotations_creators-machine-generated #annotations_creators-crowdsourced #language_creators-machine-generated #language_creators-crowdsourced #multilinguality-multilingual #language-English #language-Cebuano #language-German #language-Swedish #language-French #language-Dutch #language-Russian #language-Spanish #language-Italian #language-Egyptian Arabic #language-Polish #language-Japanese #language-Chinese #language-Vietnamese #language-Ukrainian #language-Waray (Philippines) #language-Arabic #language-Portuguese #language-Persian #language-Catalan #language-Serbian #language-Indonesian #language-Korean #language-Norwegian #language-Chechen #language-Finnish #language-Czech #language-Turkish #language-Hungarian #language-Tatar #language-Serbo-Croatian #language-Romanian #language-Basque #language-Malay (macrolanguage) #language-Esperanto #language-Hebrew #language-Armenian #language-Danish #language-Bulgarian #language-Welsh #language-Slovak #language-South Azerbaijani #language-Uzbek #language-Estonian #language-Belarusian #language-Kazakh #language-Minangkabau #language-Modern Greek (1453-) #language-Croatian #language-Lithuanian #language-Galician #language-Azerbaijani #language-Urdu #language-Slovenian #language-Ladin #language-Georgian #language-Norwegian Nynorsk #language-Hindi #language-Thai #language-Tamil #language-Bengali #language-Latin #language-Macedonian #language-Asturian #language-Latvian #language-Afrikaans #language-Tajik #language-Burmese #language-Malagasy #language-Marathi #language-Albanian #language-Bosnian #language-Occitan (post 1500) #language-Telugu #language-Malayalam #language-Low German #language-Breton #language-Kirghiz #language-Swahili (macrolanguage) #language-Javanese #language-Lombard #language-Newari #language-Western Panjabi #language-Venetian #language-Haitian #language-Piemontese #language-Bashkir #language-Luxembourgish #language-Sundanese #language-Kurdish #language-Irish #language-Silesian #language-Icelandic #language-Western Frisian #language-Chuvash #language-Central Kurdish #language-Panjabi #language-Tagalog #language-Aragonese #language-Wu Chinese #language-Dimli (individual language) #language-Ido #language-Scots #language-Volapük #language-Yoruba #language-Nepali (macrolanguage) #language-Interlingua (International Auxiliary Language Association) #language-Kannada #language-Gujarati #language-Tosk Albanian #language-Hausa #language-Kotava #language-Bavarian #language-Crimean Tatar #language-Sicilian #language-Bishnupriya #language-Quechua #language-Mongolian #language-Navajo #language-Mingrelian #language-Balinese #language-Sinhala #language-Tumbuka #language-Pushto #language-Igbo #language-Northern Frisian #language-Ossetian #language-Mazanderani #language-Oriya (macrolanguage) #language-Yakut #language-Min Dong Chinese #language-Scottish Gaelic #language-Buginese #language-Yiddish #language-Sindhi #language-Iloko #language-Amharic #language-Neapolitan #language-Limburgan #language-Central Bikol #language-Faroese #language-Gorontalo #language-Upper Sorbian #language-Maithili #language-Shan #language-Emiliano-Romagnolo #language-Achinese #language-Sanskrit #language-Assamese #language-Walloon #language-Interlingue #language-Western Armenian #language-Ligurian #language-Eastern Mari #language-Zulu #language-Shona #language-Fiji Hindi #language-Western Mari #language-Banjar #language-Khmer #language-Manipuri #language-Hakka Chinese #language-Pampanga #language-Santali #language-Rusyn #language-Pedi #language-bh #language-Somali #language-Maori #language-Northern Sami #language-Erzya #language-Vlaams #language-Dagbani #language-Sardinian #language-Corsican #language-Moroccan Arabic #language-Cornish #language-Tibetan #language-Veps #language-Gilaki #language-Turkmen #language-Kabyle #language-Gan Chinese #language-Kinyarwanda #language-Abkhazian #language-Manx #language-Uighur #language-nah #language-Zeeuws #language-Saraiki #language-Arpitan #language-Udmurt #language-Picard #language-Maltese #language-Komi #language-Kashubian #language-Guarani #language-Inari Sami #language-Aymara #language-Narom #language-Kashmiri #language-Lezghian #language-Lingua Franca Nova #language-Livvi #language-Mirandese #language-Lao #language-Saterfriesisch #language-Old English (ca. 450-1100) #language-Moksha #language-Friulian #language-Romansh #language-Ladino #language-Kara-Kalpak #language-Goan Konkani #language-Extremaduran #language-Komi-Permyak #language-Tuvinian #language-Papiamento #language-Avaric #language-Lower Sorbian #language-Lingala #language-Dotyali #language-Twi #language-Dhivehi #language-Kölsch #language-Zhuang #language-Gagauz #language-Russia Buriat #language-Pfaelzisch #language-Ganda #language-Sakizaya #language-Pangasinan #language-Pa'o Karen #language-Pali #language-Atayal #language-Hawaiian #language-Awadhi #language-Ingush #language-Karachay-Balkar #language-Kalmyk #language-Pennsylvania German #language-Tonga (Tonga Islands) #language-Atikamekw #language-Tulu #language-Official Aramaic (700-300 BCE) #language-Mon #language-Tachelhit #language-Jamaican Creole English #language-Kabiyè #language-Wolof #language-Angika #language-Kabardian #language-Nias #language-Oromo #language-Novial #language-Kikuyu #language-N'Ko #language-Bislama #language-Xhosa #language-Tok Pisin #language-Fulah #language-Tetum #language-Lojban #language-Fijian #language-Kongo #language-Lak #language-Tahitian #language-Church Slavic #language-Gun #language-Sediq #language-Amis #language-Sranan Tongo #language-Samoan #language-Madurese #language-Southern Altai #language-Latgalian #language-Guianese Creole French #language-Cherokee #language-Tswana #language-Nyanja #language-Southern Sotho #language-Pitcairn-Norfolk #language-Gothic #language-Vlax Romani #language-Ewe #language-Nigerian Pidgin #language-Bambara #language-Swati #language-Ghanaian Pidgin English #language-Tsonga #language-Venda #language-Tyap #language-Cheyenne #language-Rundi #language-Chamorro #language-Farefare #language-Inupiaq #language-Adyghe #language-Fanti #language-Pontic #language-Wayuu #language-Inuktitut #language-Paiwan #language-Sango #language-Dinka #language-Tigrinya #language-Kalaallisut #language-Dzongkha #language-Cree #license-cc-by-sa-4.0 #wikidata #wikipedia #wikification #named-entity-linking #nel #entity-linking #el #named-entity-disambiguation #ned #entity-disambiguation #ed #region-us \n",
"### Dataset Summary\n\n\nThe WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023).\n\n\nThe code for generating the dataset can be found here.",
"### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.\n* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.",
"### Languages\n\n\nThe text in the dataset is in all 320 Wikipedia languages. The full list can be found in the table below.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc EN test set looks as follows:",
"### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article",
"### Data Splits\n\n\nThe data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:",
"#### Train",
"#### Validation\n\n\n\nNOTE: The number of articles in the tables above refers to the number of articles that have at least one paragraph belonging to the article appear in the split.\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe WikiAnc dataset is given under the Creative Commons Attribution ShareAlike 4.0 International license."
] |
[
2031,
49,
58,
36,
379,
436,
46,
3,
46,
25
] |
[
"passage: ",
"passage: TAGS\n#task_categories-token-classification #annotations_creators-machine-generated #annotations_creators-crowdsourced #language_creators-machine-generated #language_creators-crowdsourced #multilinguality-multilingual #language-English #language-Cebuano #language-German #language-Swedish #language-French #language-Dutch #language-Russian #language-Spanish #language-Italian #language-Egyptian Arabic #language-Polish #language-Japanese #language-Chinese #language-Vietnamese #language-Ukrainian #language-Waray (Philippines) #language-Arabic #language-Portuguese #language-Persian #language-Catalan #language-Serbian #language-Indonesian #language-Korean #language-Norwegian #language-Chechen #language-Finnish #language-Czech #language-Turkish #language-Hungarian #language-Tatar #language-Serbo-Croatian #language-Romanian #language-Basque #language-Malay (macrolanguage) #language-Esperanto #language-Hebrew #language-Armenian #language-Danish #language-Bulgarian #language-Welsh #language-Slovak #language-South Azerbaijani #language-Uzbek #language-Estonian #language-Belarusian #language-Kazakh #language-Minangkabau #language-Modern Greek (1453-) #language-Croatian #language-Lithuanian #language-Galician #language-Azerbaijani #language-Urdu #language-Slovenian #language-Ladin #language-Georgian #language-Norwegian Nynorsk #language-Hindi #language-Thai #language-Tamil #language-Bengali #language-Latin #language-Macedonian #language-Asturian #language-Latvian #language-Afrikaans #language-Tajik #language-Burmese #language-Malagasy #language-Marathi #language-Albanian #language-Bosnian #language-Occitan (post 1500) #language-Telugu #language-Malayalam #language-Low German #language-Breton #language-Kirghiz #language-Swahili (macrolanguage) #language-Javanese #language-Lombard #language-Newari #language-Western Panjabi #language-Venetian #language-Haitian #language-Piemontese #language-Bashkir #language-Luxembourgish #language-Sundanese #language-Kurdish #language-Irish #language-Silesian #language-Icelandic #language-Western Frisian #language-Chuvash #language-Central Kurdish #language-Panjabi #language-Tagalog #language-Aragonese #language-Wu Chinese #language-Dimli (individual language) #language-Ido #language-Scots #language-Volapük #language-Yoruba #language-Nepali (macrolanguage) #language-Interlingua (International Auxiliary Language Association) #language-Kannada #language-Gujarati #language-Tosk Albanian #language-Hausa #language-Kotava #language-Bavarian #language-Crimean Tatar #language-Sicilian #language-Bishnupriya #language-Quechua #language-Mongolian #language-Navajo #language-Mingrelian #language-Balinese #language-Sinhala #language-Tumbuka #language-Pushto #language-Igbo #language-Northern Frisian #language-Ossetian #language-Mazanderani #language-Oriya (macrolanguage) #language-Yakut #language-Min Dong Chinese #language-Scottish Gaelic #language-Buginese #language-Yiddish #language-Sindhi #language-Iloko #language-Amharic #language-Neapolitan #language-Limburgan #language-Central Bikol #language-Faroese #language-Gorontalo #language-Upper Sorbian #language-Maithili #language-Shan #language-Emiliano-Romagnolo #language-Achinese #language-Sanskrit #language-Assamese #language-Walloon #language-Interlingue #language-Western Armenian #language-Ligurian #language-Eastern Mari #language-Zulu #language-Shona #language-Fiji Hindi #language-Western Mari #language-Banjar #language-Khmer #language-Manipuri #language-Hakka Chinese #language-Pampanga #language-Santali #language-Rusyn #language-Pedi #language-bh #language-Somali #language-Maori #language-Northern Sami #language-Erzya #language-Vlaams #language-Dagbani #language-Sardinian #language-Corsican #language-Moroccan Arabic #language-Cornish #language-Tibetan #language-Veps #language-Gilaki #language-Turkmen #language-Kabyle #language-Gan Chinese #language-Kinyarwanda #language-Abkhazian #language-Manx #language-Uighur #language-nah #language-Zeeuws #language-Saraiki #language-Arpitan #language-Udmurt #language-Picard #language-Maltese #language-Komi #language-Kashubian #language-Guarani #language-Inari Sami #language-Aymara #language-Narom #language-Kashmiri #language-Lezghian #language-Lingua Franca Nova #language-Livvi #language-Mirandese #language-Lao #language-Saterfriesisch #language-Old English (ca. 450-1100) #language-Moksha #language-Friulian #language-Romansh #language-Ladino #language-Kara-Kalpak #language-Goan Konkani #language-Extremaduran #language-Komi-Permyak #language-Tuvinian #language-Papiamento #language-Avaric #language-Lower Sorbian #language-Lingala #language-Dotyali #language-Twi #language-Dhivehi #language-Kölsch #language-Zhuang #language-Gagauz #language-Russia Buriat #language-Pfaelzisch #language-Ganda #language-Sakizaya #language-Pangasinan #language-Pa'o Karen #language-Pali #language-Atayal #language-Hawaiian #language-Awadhi #language-Ingush #language-Karachay-Balkar #language-Kalmyk #language-Pennsylvania German #language-Tonga (Tonga Islands) #language-Atikamekw #language-Tulu #language-Official Aramaic (700-300 BCE) #language-Mon #language-Tachelhit #language-Jamaican Creole English #language-Kabiyè #language-Wolof #language-Angika #language-Kabardian #language-Nias #language-Oromo #language-Novial #language-Kikuyu #language-N'Ko #language-Bislama #language-Xhosa #language-Tok Pisin #language-Fulah #language-Tetum #language-Lojban #language-Fijian #language-Kongo #language-Lak #language-Tahitian #language-Church Slavic #language-Gun #language-Sediq #language-Amis #language-Sranan Tongo #language-Samoan #language-Madurese #language-Southern Altai #language-Latgalian #language-Guianese Creole French #language-Cherokee #language-Tswana #language-Nyanja #language-Southern Sotho #language-Pitcairn-Norfolk #language-Gothic #language-Vlax Romani #language-Ewe #language-Nigerian Pidgin #language-Bambara #language-Swati #language-Ghanaian Pidgin English #language-Tsonga #language-Venda #language-Tyap #language-Cheyenne #language-Rundi #language-Chamorro #language-Farefare #language-Inupiaq #language-Adyghe #language-Fanti #language-Pontic #language-Wayuu #language-Inuktitut #language-Paiwan #language-Sango #language-Dinka #language-Tigrinya #language-Kalaallisut #language-Dzongkha #language-Cree #license-cc-by-sa-4.0 #wikidata #wikipedia #wikification #named-entity-linking #nel #entity-linking #el #named-entity-disambiguation #ned #entity-disambiguation #ed #region-us \n### Dataset Summary\n\n\nThe WikiAnc dataset is an automatically generated dataset from Wikipedia (all languages) and Wikidata dumps (August, 2023).\n\n\nThe code for generating the dataset can be found here.### Supported Tasks\n\n\n* 'wikificiation': The dataset can be used to train a model for Wikification.\n* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.### Languages\n\n\nThe text in the dataset is in all 320 Wikipedia languages. The full list can be found in the table below.\n\n\nDataset Structure\n-----------------",
"passage: ### Data Instances\n\n\nA typical data point represents a paragraph in a Wikipedia article.\n\n\nThe 'paragraph\\_text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'paragraph\\_anchors' field contains a list of anchors, each represented by a struct with the inclusive starting UTF-8 code point 'start' field, exclusive ending UTF-8 code point 'end' field, a nullable 'qid' field, a nullable 'pageid' field, and an NFC normalized, UTF-8 encoded 'title' (Wikipedia) field.\n\n\nAdditionally, each paragraph has 'article\\_title', 'article\\_pageid', and (nullable) 'article\\_qid' fields referring to the article the paragraph came from.\n\n\nThere is also a nullable, NFC normalized, UTF-8 encoded 'section\\_heading' field, and an integer 'section\\_level' field referring to the heading (if it exists) of the article section, and the level in the section hierarchy that the paragraph came from.\n\n\nThe 'qid' fields refers to Wikidata's QID identifiers, while the 'pageid' and 'title' fields refer to Wikipedia's pageID and title identifiers (there is a one-to-one mapping between pageIDs and titles).\n\n\nNOTE: An anchor will always have a 'title', but that doesn't mean it has to have a 'pageid'. This is because Wikipedia allows defining anchors to nonexistent articles.\n\n\nAn example from the WikiAnc EN test set looks as follows:### Data Fields\n\n\n* 'uuid': a UTF-8 encoded string representing a v4 UUID that uniquely identifies the example\n* 'article\\_title': an NFC normalized, UTF-8 encoded Wikipedia title of the article; spaces are replaced with underscores\n* 'article\\_pageid': an integer representing the Wikipedia pageID of the article\n* 'article\\_qid': an integer representing the Wikidata QID this article refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n* 'section\\_heading': a nullable, NFC normalized, UTF-8 encoded string representing the section heading\n* 'section\\_level': an integer representing the level of the section in the section hierarchy\n* 'paragraph\\_text': an NFC normalized, UTF-8 encoded string representing the paragraph\n* 'paragraph\\_anchors': a list of structs representing anchors, each anchor has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the anchors\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the anchor\n\t+ 'qid': a nullable integer representing the Wikidata QID this anchor refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': a nullable integer representing the Wikipedia pageID of the anchor; it can be null if the article didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the Wikipedia title of the anchor; spaces are replaced with underscores; can refer to a nonexistent Wikipedia article### Data Splits\n\n\nThe data is split into training, validation and test sets; paragraphs belonging to the same article aren't necessarily in the same split. The final split sizes are as follows:#### Train"
] |
2d6ebb37abb8a5cbe8b7380e2167fa98f33a96b4
|
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Physics dataset is composed of 20K problem-solution pairs obtained using gpt-4.
The dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
## Data Fields
**The data fields are as follows:**
* `role_1`: assistant role
* `topic`: physics topic
* `sub_topic`: physics subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
**Download in python**
```python
from datasets import load_dataset
dataset = load_dataset("lgaalves/camel-ai-physics")
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
|
lgaalves/camel-ai-physics
|
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2303.17760",
"region:us"
] |
2023-09-05T13:51:49+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "pretty_name": "CAMEL Physics", "dataset_info": {"features": [{"name": "role_1", "dtype": "string"}, {"name": "topic;", "dtype": "string"}, {"name": "sub_topic", "dtype": "string"}, {"name": "message_1", "dtype": "string"}, {"name": "message_2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51650490, "num_examples": 20000}], "download_size": 23872398, "dataset_size": 51650490}, "tags": ["instruction-finetuning"], "arxiv": 2303.1776, "extra_gated_prompt": "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT.", "extra_gated_fields": {"Name": "text", "Email": "text"}, "I will adhere to the terms and conditions of this dataset": "checkbox"}
|
2023-10-17T18:27:21+00:00
|
[
"2303.17760"
] |
[
"en"
] |
TAGS
#task_categories-text-generation #language-English #license-cc-by-nc-4.0 #instruction-finetuning #arxiv-2303.17760 #region-us
|
# CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society
- Github: URL
- Website: URL
- Arxiv Paper: URL
## Dataset Summary
Physics dataset is composed of 20K problem-solution pairs obtained using gpt-4.
The dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
## Data Fields
The data fields are as follows:
* 'role_1': assistant role
* 'topic': physics topic
* 'sub_topic': physics subtopic belonging to topic
* 'message_1': refers to the problem the assistant is asked to solve.
* 'message_2': refers to the solution provided by the assistant.
Download in python
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
|
[
"# CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society\n- Github: URL\n- Website: URL\n- Arxiv Paper: URL",
"## Dataset Summary\n\nPhysics dataset is composed of 20K problem-solution pairs obtained using gpt-4. \nThe dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each \"topic,subtopic\" pairs.",
"## Data Fields\n\nThe data fields are as follows:\n\n* 'role_1': assistant role\n* 'topic': physics topic\n* 'sub_topic': physics subtopic belonging to topic\n* 'message_1': refers to the problem the assistant is asked to solve.\n* 'message_2': refers to the solution provided by the assistant.\n\nDownload in python",
"## Disclaimer:\n\nThis data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.\n\n---\nlicense: cc-by-nc-4.0\n---"
] |
[
"TAGS\n#task_categories-text-generation #language-English #license-cc-by-nc-4.0 #instruction-finetuning #arxiv-2303.17760 #region-us \n",
"# CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society\n- Github: URL\n- Website: URL\n- Arxiv Paper: URL",
"## Dataset Summary\n\nPhysics dataset is composed of 20K problem-solution pairs obtained using gpt-4. \nThe dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each \"topic,subtopic\" pairs.",
"## Data Fields\n\nThe data fields are as follows:\n\n* 'role_1': assistant role\n* 'topic': physics topic\n* 'sub_topic': physics subtopic belonging to topic\n* 'message_1': refers to the problem the assistant is asked to solve.\n* 'message_2': refers to the solution provided by the assistant.\n\nDownload in python",
"## Disclaimer:\n\nThis data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.\n\n---\nlicense: cc-by-nc-4.0\n---"
] |
[
46,
39,
68,
90,
44
] |
[
"passage: TAGS\n#task_categories-text-generation #language-English #license-cc-by-nc-4.0 #instruction-finetuning #arxiv-2303.17760 #region-us \n# CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society\n- Github: URL\n- Website: URL\n- Arxiv Paper: URL## Dataset Summary\n\nPhysics dataset is composed of 20K problem-solution pairs obtained using gpt-4. \nThe dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each \"topic,subtopic\" pairs.## Data Fields\n\nThe data fields are as follows:\n\n* 'role_1': assistant role\n* 'topic': physics topic\n* 'sub_topic': physics subtopic belonging to topic\n* 'message_1': refers to the problem the assistant is asked to solve.\n* 'message_2': refers to the solution provided by the assistant.\n\nDownload in python## Disclaimer:\n\nThis data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.\n\n---\nlicense: cc-by-nc-4.0\n---"
] |
a54d9e5fddda52095f4415f882e8bfd0b39096b2
|
# Dataset Card for Evaluation run of TFLai/EnsembleV5-Nova-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/EnsembleV5-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/EnsembleV5-Nova-13B](https://huggingface.co/TFLai/EnsembleV5-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__EnsembleV5-Nova-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T04:00:31.640164](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__EnsembleV5-Nova-13B/blob/main/results_2023-09-23T04-00-31.640164.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": 0.007445469798657718,
"em_stderr": 0.0008803652515899855,
"f1": 0.08636220637583875,
"f1_stderr": 0.0018310737230495444,
"acc": 0.4350441276875584,
"acc_stderr": 0.010249391454413254
},
"harness|drop|3": {
"em": 0.007445469798657718,
"em_stderr": 0.0008803652515899855,
"f1": 0.08636220637583875,
"f1_stderr": 0.0018310737230495444
},
"harness|gsm8k|5": {
"acc": 0.10765731614859743,
"acc_stderr": 0.008537484003023352
},
"harness|winogrande|5": {
"acc": 0.7624309392265194,
"acc_stderr": 0.011961298905803157
}
}
```
### 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__EnsembleV5-Nova-13B
|
[
"region:us"
] |
2023-09-05T13:57:22+00:00
|
{"pretty_name": "Evaluation run of TFLai/EnsembleV5-Nova-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/EnsembleV5-Nova-13B](https://huggingface.co/TFLai/EnsembleV5-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__EnsembleV5-Nova-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-23T04:00:31.640164](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__EnsembleV5-Nova-13B/blob/main/results_2023-09-23T04-00-31.640164.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\": 0.007445469798657718,\n \"em_stderr\": 0.0008803652515899855,\n \"f1\": 0.08636220637583875,\n \"f1_stderr\": 0.0018310737230495444,\n \"acc\": 0.4350441276875584,\n \"acc_stderr\": 0.010249391454413254\n },\n \"harness|drop|3\": {\n \"em\": 0.007445469798657718,\n \"em_stderr\": 0.0008803652515899855,\n \"f1\": 0.08636220637583875,\n \"f1_stderr\": 0.0018310737230495444\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10765731614859743,\n \"acc_stderr\": 0.008537484003023352\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803157\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/EnsembleV5-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_05T14_56_57.875038", "path": ["**/details_harness|arc:challenge|25_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_23T04_00_31.640164", "path": ["**/details_harness|drop|3_2023-09-23T04-00-31.640164.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-23T04-00-31.640164.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_23T04_00_31.640164", "path": ["**/details_harness|gsm8k|5_2023-09-23T04-00-31.640164.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-23T04-00-31.640164.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hellaswag|10_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": 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["**/details_harness|hendrycksTest-philosophy|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-05T14:56:57.875038.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-05T14:56:57.875038.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_23T04_00_31.640164", "path": ["**/details_harness|winogrande|5_2023-09-23T04-00-31.640164.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-23T04-00-31.640164.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_05T14_56_57.875038", "path": ["results_2023-09-05T14:56:57.875038.parquet"]}, {"split": "2023_09_23T04_00_31.640164", "path": ["results_2023-09-23T04-00-31.640164.parquet"]}, {"split": "latest", "path": ["results_2023-09-23T04-00-31.640164.parquet"]}]}]}
|
2023-09-23T03:00:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/EnsembleV5-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/EnsembleV5-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 2023-09-23T04:00:31.640164(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/EnsembleV5-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/EnsembleV5-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 2023-09-23T04:00:31.640164(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/EnsembleV5-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/EnsembleV5-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 2023-09-23T04:00:31.640164(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,
22,
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170,
66,
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4,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TFLai/EnsembleV5-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/EnsembleV5-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 2023-09-23T04:00:31.640164(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"
] |
f96884197402f8e7c8fc813e3805645b42684539
|
# Dataset Card for "wikisource_fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
manu/wikisource_fr
|
[
"region:us"
] |
2023-09-05T13:58:26+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11647349958, "num_examples": 2567238}], "download_size": 7238737612, "dataset_size": 11647349958}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-05T14:08:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wikisource_fr"
More Information needed
|
[
"# Dataset Card for \"wikisource_fr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wikisource_fr\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wikisource_fr\"\n\nMore Information needed"
] |
d0af727e043bca54793c4ef6f031485e287e19f9
|
# Dataset Card for "data_for_synthesis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/data_for_synthesis
|
[
"region:us"
] |
2023-09-05T14:08:40+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "sentence_annotation", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}, {"name": "file", "dtype": "string"}, {"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "string"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "origin_transcription", "dtype": "string"}, {"name": "sentence_norm", "dtype": "string"}, {"name": "w2v2_large_transcription", "dtype": "string"}, {"name": "wer", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3484659441, "num_examples": 6729}], "download_size": 825836967, "dataset_size": 3484659441}}
|
2023-09-05T14:16:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data_for_synthesis"
More Information needed
|
[
"# Dataset Card for \"data_for_synthesis\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data_for_synthesis\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data_for_synthesis\"\n\nMore Information needed"
] |
2cf58dce7d98dad08b075144271e95a0ebdf8de5
|
# Dataset Card for "data_for_synthesis_filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/data_for_synthesis_filtered
|
[
"region:us"
] |
2023-09-05T14:18:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "sentence_annotation", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}, {"name": "file", "dtype": "string"}, {"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "string"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "origin_transcription", "dtype": "string"}, {"name": "sentence_norm", "dtype": "string"}, {"name": "w2v2_large_transcription", "dtype": "string"}, {"name": "wer", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 859642543.031654, "num_examples": 1660}], "download_size": 191939150, "dataset_size": 859642543.031654}}
|
2023-09-05T14:20:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data_for_synthesis_filtered"
More Information needed
|
[
"# Dataset Card for \"data_for_synthesis_filtered\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data_for_synthesis_filtered\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data_for_synthesis_filtered\"\n\nMore Information needed"
] |
e9f252ef532635b2d13cd67e120b8271ae4d90cf
|
# Dataset Card for Evaluation run of dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16
- **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 [dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16](https://huggingface.co/dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16) 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_dhmeltzer__Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T19:51:06.659965](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16/blob/main/results_2023-09-22T19-51-06.659965.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": 0.002726510067114094,
"em_stderr": 0.0005340111700415918,
"f1": 0.06889890939597297,
"f1_stderr": 0.0014912452735151907,
"acc": 0.43548543448224686,
"acc_stderr": 0.010181852995139873
},
"harness|drop|3": {
"em": 0.002726510067114094,
"em_stderr": 0.0005340111700415918,
"f1": 0.06889890939597297,
"f1_stderr": 0.0014912452735151907
},
"harness|gsm8k|5": {
"acc": 0.10538286580742987,
"acc_stderr": 0.00845757588404176
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237986
}
}
```
### 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_dhmeltzer__Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16
|
[
"region:us"
] |
2023-09-05T14:27:03+00:00
|
{"pretty_name": "Evaluation run of dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16", "dataset_summary": "Dataset automatically created during the evaluation run of model [dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16](https://huggingface.co/dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16) 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_dhmeltzer__Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-22T19:51:06.659965](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16/blob/main/results_2023-09-22T19-51-06.659965.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\": 0.002726510067114094,\n \"em_stderr\": 0.0005340111700415918,\n \"f1\": 0.06889890939597297,\n \"f1_stderr\": 0.0014912452735151907,\n \"acc\": 0.43548543448224686,\n \"acc_stderr\": 0.010181852995139873\n },\n \"harness|drop|3\": {\n \"em\": 0.002726510067114094,\n \"em_stderr\": 0.0005340111700415918,\n \"f1\": 0.06889890939597297,\n \"f1_stderr\": 0.0014912452735151907\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10538286580742987,\n \"acc_stderr\": 0.00845757588404176\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237986\n }\n}\n```", "repo_url": "https://huggingface.co/dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": 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2023-09-22T18:51:18+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16 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 2023-09-22T19:51:06.659965(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 dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16",
"## 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 dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16 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 2023-09-22T19:51:06.659965(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 dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16",
"## 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 dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16 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 2023-09-22T19:51:06.659965(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,
36,
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184,
66,
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6,
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9,
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7,
5,
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6,
5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16## 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 dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16 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 2023-09-22T19:51:06.659965(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"
] |
82beba758f242395928034b22679f3afa8285236
|
# Dataset Card for "AO3_fandom_chatbot_1to1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ebony59/AO3_fandom_chatbot_1to1
|
[
"region:us"
] |
2023-09-05T14:32:32+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1485153, "num_examples": 750}], "download_size": 385380, "dataset_size": 1485153}}
|
2023-09-05T16:39:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "AO3_fandom_chatbot_1to1"
More Information needed
|
[
"# Dataset Card for \"AO3_fandom_chatbot_1to1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"AO3_fandom_chatbot_1to1\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"AO3_fandom_chatbot_1to1\"\n\nMore Information needed"
] |
c039355a909d7bf8a3e805ba806a324cdf3e82ec
|
# Dataset Card for "sen12mscr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mespinosami/sen12mscr
|
[
"region:us"
] |
2023-09-05T14:50:25+00:00
|
{"dataset_info": {"features": [{"name": "s1", "dtype": "image"}, {"name": "s2", "dtype": "image"}, {"name": "s2_cloudy", "dtype": "image"}, {"name": "text_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31920756270.948, "num_examples": 110238}, {"name": "test", "num_bytes": 2353833252.636, "num_examples": 7899}], "download_size": 18722799729, "dataset_size": 34274589523.584003}}
|
2023-09-05T16:38:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sen12mscr"
More Information needed
|
[
"# Dataset Card for \"sen12mscr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sen12mscr\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sen12mscr\"\n\nMore Information needed"
] |
c91d65d281fc8b69bc49803d1f1e94eee3ce104b
|
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
strkan/guanaco-llama2-1k
|
[
"region:us"
] |
2023-09-05T15:29:53+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-08T09:34:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
ca6180cbae23d6bd05a5fd6d9175967be93fd67f
|
# Dataset Card for "processed_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pbaoo2705/processed_dataset
|
[
"region:us"
] |
2023-09-05T15:49:31+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3544789, "num_examples": 5000}, {"name": "test", "num_bytes": 708063, "num_examples": 1000}], "download_size": 2342034, "dataset_size": 4252852}}
|
2023-09-05T15:50:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "processed_dataset"
More Information needed
|
[
"# Dataset Card for \"processed_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_dataset\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_dataset\"\n\nMore Information needed"
] |
0b8a812053ded4d3583561c551d92a93a1ac3b3d
|
# Dataset Card for "EventData"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dshut002/EventData
|
[
"region:us"
] |
2023-09-05T15:57:15+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 83366, "num_examples": 100}], "download_size": 44686, "dataset_size": 83366}}
|
2023-09-05T16:00:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "EventData"
More Information needed
|
[
"# Dataset Card for \"EventData\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"EventData\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"EventData\"\n\nMore Information needed"
] |
bacc19d71f61a2badecbe2e3844af0ead99dae6c
|
# Dataset Card for "LocationData"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dshut002/LocationData
|
[
"region:us"
] |
2023-09-05T15:57:20+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61548, "num_examples": 100}], "download_size": 32055, "dataset_size": 61548}}
|
2023-09-05T15:57:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "LocationData"
More Information needed
|
[
"# Dataset Card for \"LocationData\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"LocationData\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"LocationData\"\n\nMore Information needed"
] |
27d46fe5bc420e23d168a68d549bafb61850cb7e
|
## Overview
This dataset is mostly a continuation of https://hf.co/datasets/jondurbin/airoboros-2.1, with some notable additions and fixes.
__*I've gated access with request, due to the de-alignment data. To download, you must agree to the following:*__
- Some of the content is "toxic"/"harmful", and contains profanity and other types of sensitive content.
- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.
- Use with extreme caution, particularly in locations with less-than-free speech laws.
- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.
### 2.1 Contamination
I accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:
- https://huggingface.co/jondurbin/airoboros-l2-70b-2.1/discussions/3#64f325ce352152814d1f796a
- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/225#64f0997659da193a12b78c32
I flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.
Some of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.
This time around, I used `thenlper/gte-small` to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!
I haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.
### Awareness
I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.
For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.
If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory).
https://github.com/jondurbin/airoboros/commit/e91562c88d7610edb051606622e7c25a99884f7e
### Editor
I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output.
https://github.com/jondurbin/airoboros/commit/e60a68de5f9622320c9cfff3b238bd83cc7e373b
### Writing
I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring.
### Multiple choice
I created many more multiple choice questions, many of which have additional text context.
### Roleplay/conversation
I re-created all of the GTKM and RP datasets this time around, removing all of the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces.
The GTKM instructor now does the same thing as RP, in that it saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.
### De-alignment
I included a small sampling of "de-alignment" data. The llama-2 base models seem extremely reluctant to discuss certain topics, curse, or otherwise produce other-than-pg content. I don't want a vile model, but I also don't *NOT* want a vile model.
- comedy skits, to add more comedy and occasional cursing
- instruction/response pairs that would typically otherwise be refused
- various (LLM ehanced) stories from the internet with somewhat spicy content
- story-writing tasks as a re-telling of popular horror/fantasy films (by default, the LLM generated stories often have too sunny of a disposition, so hopefully this will allow for some twists or more interesting stories)
- rude responses (if a character card specifies they are rude and curse, it should actually do so without prompt hacking IMO)
None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web. Use with extreme caution, particularly in locations with strict speech laws!
See "instructions-clean.jsonl" for a version without dealignment data.
### UTF-8 to ASCII
I replaced most of the "standard" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.
### Summarization
I also included 500 examples from:
https://hf.co/datasets/mattpscott/airoboros-summarization
These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.
Thanks Matt!
### Usage/license info
Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting.
|
unalignment/airoboros-2.2
|
[
"license:other",
"not-for-all-audiences",
"region:us"
] |
2023-09-05T16:07:10+00:00
|
{"license": "other", "tags": ["not-for-all-audiences"]}
|
2023-12-11T21:02:03+00:00
|
[] |
[] |
TAGS
#license-other #not-for-all-audiences #region-us
|
## Overview
This dataset is mostly a continuation of URL with some notable additions and fixes.
__*I've gated access with request, due to the de-alignment data. To download, you must agree to the following:*__
- Some of the content is "toxic"/"harmful", and contains profanity and other types of sensitive content.
- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.
- Use with extreme caution, particularly in locations with less-than-free speech laws.
- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.
### 2.1 Contamination
I accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:
- URL
- URL
I flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.
Some of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.
This time around, I used 'thenlper/gte-small' to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!
I haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.
### Awareness
I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.
For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.
If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory).
URL
### Editor
I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output.
URL
### Writing
I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring.
### Multiple choice
I created many more multiple choice questions, many of which have additional text context.
### Roleplay/conversation
I re-created all of the GTKM and RP datasets this time around, removing all of the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces.
The GTKM instructor now does the same thing as RP, in that it saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.
### De-alignment
I included a small sampling of "de-alignment" data. The llama-2 base models seem extremely reluctant to discuss certain topics, curse, or otherwise produce other-than-pg content. I don't want a vile model, but I also don't *NOT* want a vile model.
- comedy skits, to add more comedy and occasional cursing
- instruction/response pairs that would typically otherwise be refused
- various (LLM ehanced) stories from the internet with somewhat spicy content
- story-writing tasks as a re-telling of popular horror/fantasy films (by default, the LLM generated stories often have too sunny of a disposition, so hopefully this will allow for some twists or more interesting stories)
- rude responses (if a character card specifies they are rude and curse, it should actually do so without prompt hacking IMO)
None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web. Use with extreme caution, particularly in locations with strict speech laws!
See "URL" for a version without dealignment data.
### UTF-8 to ASCII
I replaced most of the "standard" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.
### Summarization
I also included 500 examples from:
URL
These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.
Thanks Matt!
### Usage/license info
Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting.
|
[
"## Overview\n\nThis dataset is mostly a continuation of URL with some notable additions and fixes.\n\n__*I've gated access with request, due to the de-alignment data. To download, you must agree to the following:*__\n- Some of the content is \"toxic\"/\"harmful\", and contains profanity and other types of sensitive content.\n- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.\n- Use with extreme caution, particularly in locations with less-than-free speech laws.\n- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.",
"### 2.1 Contamination\n\nI accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:\n- URL\n- URL\n\nI flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.\n\nSome of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.\n\nThis time around, I used 'thenlper/gte-small' to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!\n\nI haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.",
"### Awareness\n\nI added a new \"awareness\" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.\n\nFor example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.\nIf, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from \"home\" and will provide a more nuanced answer as a human would (in theory).\n\nURL",
"### Editor\n\nI created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like \"Please correct and improve the text.\" with the original well-written text and target output.\n\nURL",
"### Writing\n\nI regenerated (almost) all of the training data that included \"Once upon a time...\" because it's too cliche and boring.",
"### Multiple choice\n\nI created many more multiple choice questions, many of which have additional text context.",
"### Roleplay/conversation\n\nI re-created all of the GTKM and RP datasets this time around, removing all of the \"USER: \" and \"ASSISTANT: \" prefixes from the instructions/responses, so it's more compatible with existing interfaces.\n\nThe GTKM instructor now does the same thing as RP, in that it saves each round of \"conversation\" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.",
"### De-alignment\n\nI included a small sampling of \"de-alignment\" data. The llama-2 base models seem extremely reluctant to discuss certain topics, curse, or otherwise produce other-than-pg content. I don't want a vile model, but I also don't *NOT* want a vile model.\n\n- comedy skits, to add more comedy and occasional cursing\n- instruction/response pairs that would typically otherwise be refused\n- various (LLM ehanced) stories from the internet with somewhat spicy content\n- story-writing tasks as a re-telling of popular horror/fantasy films (by default, the LLM generated stories often have too sunny of a disposition, so hopefully this will allow for some twists or more interesting stories)\n- rude responses (if a character card specifies they are rude and curse, it should actually do so without prompt hacking IMO)\n\nNone of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web. Use with extreme caution, particularly in locations with strict speech laws!\n\nSee \"URL\" for a version without dealignment data.",
"### UTF-8 to ASCII\n\nI replaced most of the \"standard\" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.",
"### Summarization\n\nI also included 500 examples from:\nURL\n\nThese are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.\n\nThanks Matt!",
"### Usage/license info\n\nMuch (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about \"competing\" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting."
] |
[
"TAGS\n#license-other #not-for-all-audiences #region-us \n",
"## Overview\n\nThis dataset is mostly a continuation of URL with some notable additions and fixes.\n\n__*I've gated access with request, due to the de-alignment data. To download, you must agree to the following:*__\n- Some of the content is \"toxic\"/\"harmful\", and contains profanity and other types of sensitive content.\n- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.\n- Use with extreme caution, particularly in locations with less-than-free speech laws.\n- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.",
"### 2.1 Contamination\n\nI accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:\n- URL\n- URL\n\nI flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.\n\nSome of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.\n\nThis time around, I used 'thenlper/gte-small' to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!\n\nI haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.",
"### Awareness\n\nI added a new \"awareness\" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.\n\nFor example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.\nIf, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from \"home\" and will provide a more nuanced answer as a human would (in theory).\n\nURL",
"### Editor\n\nI created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like \"Please correct and improve the text.\" with the original well-written text and target output.\n\nURL",
"### Writing\n\nI regenerated (almost) all of the training data that included \"Once upon a time...\" because it's too cliche and boring.",
"### Multiple choice\n\nI created many more multiple choice questions, many of which have additional text context.",
"### Roleplay/conversation\n\nI re-created all of the GTKM and RP datasets this time around, removing all of the \"USER: \" and \"ASSISTANT: \" prefixes from the instructions/responses, so it's more compatible with existing interfaces.\n\nThe GTKM instructor now does the same thing as RP, in that it saves each round of \"conversation\" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.",
"### De-alignment\n\nI included a small sampling of \"de-alignment\" data. The llama-2 base models seem extremely reluctant to discuss certain topics, curse, or otherwise produce other-than-pg content. I don't want a vile model, but I also don't *NOT* want a vile model.\n\n- comedy skits, to add more comedy and occasional cursing\n- instruction/response pairs that would typically otherwise be refused\n- various (LLM ehanced) stories from the internet with somewhat spicy content\n- story-writing tasks as a re-telling of popular horror/fantasy films (by default, the LLM generated stories often have too sunny of a disposition, so hopefully this will allow for some twists or more interesting stories)\n- rude responses (if a character card specifies they are rude and curse, it should actually do so without prompt hacking IMO)\n\nNone of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web. Use with extreme caution, particularly in locations with strict speech laws!\n\nSee \"URL\" for a version without dealignment data.",
"### UTF-8 to ASCII\n\nI replaced most of the \"standard\" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.",
"### Summarization\n\nI also included 500 examples from:\nURL\n\nThese are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.\n\nThanks Matt!",
"### Usage/license info\n\nMuch (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about \"competing\" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting."
] |
[
20,
188,
256,
136,
79,
36,
21,
126,
277,
83,
42,
67
] |
[
"passage: TAGS\n#license-other #not-for-all-audiences #region-us \n## Overview\n\nThis dataset is mostly a continuation of URL with some notable additions and fixes.\n\n__*I've gated access with request, due to the de-alignment data. To download, you must agree to the following:*__\n- Some of the content is \"toxic\"/\"harmful\", and contains profanity and other types of sensitive content.\n- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.\n- Use with extreme caution, particularly in locations with less-than-free speech laws.\n- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.### 2.1 Contamination\n\nI accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:\n- URL\n- URL\n\nI flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.\n\nSome of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.\n\nThis time around, I used 'thenlper/gte-small' to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!\n\nI haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.",
"passage: ### Awareness\n\nI added a new \"awareness\" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.\n\nFor example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.\nIf, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from \"home\" and will provide a more nuanced answer as a human would (in theory).\n\nURL### Editor\n\nI created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like \"Please correct and improve the text.\" with the original well-written text and target output.\n\nURL### Writing\n\nI regenerated (almost) all of the training data that included \"Once upon a time...\" because it's too cliche and boring.### Multiple choice\n\nI created many more multiple choice questions, many of which have additional text context.### Roleplay/conversation\n\nI re-created all of the GTKM and RP datasets this time around, removing all of the \"USER: \" and \"ASSISTANT: \" prefixes from the instructions/responses, so it's more compatible with existing interfaces.\n\nThe GTKM instructor now does the same thing as RP, in that it saves each round of \"conversation\" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs."
] |
2aca4f9eb7005b0a7e60fa8b0a86d625d3e49ce9
|
# General Protein Binding Sites
This is an `85/15` train/test split of protein sequences and their corresponding binding sites obtained from UniProt.
There protein sequences of varying sizes, split into non-overlapping subsequences of length 1000 or less.
|
AmelieSchreiber/general_binding_sites
|
[
"task_categories:token-classification",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"proteins",
"biology",
"region:us"
] |
2023-09-05T16:07:17+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["token-classification"], "pretty_name": "General Protein Binding Sites", "tags": ["proteins", "biology"]}
|
2023-09-07T14:21:04+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-token-classification #size_categories-100K<n<1M #language-English #license-mit #proteins #biology #region-us
|
# General Protein Binding Sites
This is an '85/15' train/test split of protein sequences and their corresponding binding sites obtained from UniProt.
There protein sequences of varying sizes, split into non-overlapping subsequences of length 1000 or less.
|
[
"# General Protein Binding Sites\n\nThis is an '85/15' train/test split of protein sequences and their corresponding binding sites obtained from UniProt. \nThere protein sequences of varying sizes, split into non-overlapping subsequences of length 1000 or less."
] |
[
"TAGS\n#task_categories-token-classification #size_categories-100K<n<1M #language-English #license-mit #proteins #biology #region-us \n",
"# General Protein Binding Sites\n\nThis is an '85/15' train/test split of protein sequences and their corresponding binding sites obtained from UniProt. \nThere protein sequences of varying sizes, split into non-overlapping subsequences of length 1000 or less."
] |
[
45,
65
] |
[
"passage: TAGS\n#task_categories-token-classification #size_categories-100K<n<1M #language-English #license-mit #proteins #biology #region-us \n# General Protein Binding Sites\n\nThis is an '85/15' train/test split of protein sequences and their corresponding binding sites obtained from UniProt. \nThere protein sequences of varying sizes, split into non-overlapping subsequences of length 1000 or less."
] |
95b59e456ede1ea05143c86f10cea0d3ebaad3b6
|
# Dataset Card for "autotree_snnxor_n15_l1_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_snnxor_n15_l1_10
|
[
"region:us"
] |
2023-09-05T16:49:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "input_y_clean", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 236440000, "num_examples": 10000}, {"name": "validation", "num_bytes": 236440000, "num_examples": 10000}, {"name": "test", "num_bytes": 236440000, "num_examples": 10000}], "download_size": 432260994, "dataset_size": 709320000}}
|
2023-09-18T20:51:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_snnxor_n15_l1_10"
More Information needed
|
[
"# Dataset Card for \"autotree_snnxor_n15_l1_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_snnxor_n15_l1_10\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_snnxor_n15_l1_10\"\n\nMore Information needed"
] |
9ef597004ef4e940afcd57cedb21ae017fc6c255
|
# Dataset Card for "belebele_arabic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arbml/belebele_arabic
|
[
"region:us"
] |
2023-09-05T16:50:15+00:00
|
{"dataset_info": {"features": [{"name": "link", "dtype": "string"}, {"name": "question_number", "dtype": "int64"}, {"name": "flores_passage", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "mc_answer1", "dtype": "string"}, {"name": "mc_answer2", "dtype": "string"}, {"name": "mc_answer3", "dtype": "string"}, {"name": "mc_answer4", "dtype": "string"}, {"name": "correct_answer_num", "dtype": "string"}, {"name": "dialect", "dtype": "string"}, {"name": "ds", "dtype": "timestamp[s]"}], "splits": [{"name": "train", "num_bytes": 6174536, "num_examples": 5400}], "download_size": 2102867, "dataset_size": 6174536}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-05T16:50:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "belebele_arabic"
More Information needed
|
[
"# Dataset Card for \"belebele_arabic\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"belebele_arabic\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"belebele_arabic\"\n\nMore Information needed"
] |
5b713175fd441f43346e38d7610c5fbf07675bd1
|
# Dataset Card for "straight_dense_summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
griffin/straight_dense_summ
|
[
"region:us"
] |
2023-09-05T16:58:32+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9735079, "num_examples": 2000}], "download_size": 3461736, "dataset_size": 9735079}}
|
2023-09-05T17:06:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "straight_dense_summ"
More Information needed
|
[
"# Dataset Card for \"straight_dense_summ\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"straight_dense_summ\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"straight_dense_summ\"\n\nMore Information needed"
] |
c1b4adb6080caff004f9f0551fbf5c68af8989eb
|
From: https://www.kaggle.com/datasets/jacobhds/leetcode-solutions-and-content-kpis
|
cassanof/leetcode-solutions
|
[
"region:us"
] |
2023-09-05T16:59:18+00:00
|
{}
|
2023-09-05T16:59:57+00:00
|
[] |
[] |
TAGS
#region-us
|
From: URL
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
c08f266d294f4a01dd9cdadab051733594efa6a6
|
# Dataset Card for "multiview_panohead"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
irodkin/multiview_panohead
|
[
"region:us"
] |
2023-09-05T17:03:13+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "view_45", "dtype": "image"}, {"name": "view_90", "dtype": "image"}, {"name": "view_180", "dtype": "image"}, {"name": "view_270", "dtype": "image"}, {"name": "view_above", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3000408300.0, "num_examples": 5000}], "download_size": 2997397205, "dataset_size": 3000408300.0}}
|
2023-09-05T17:28:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "multiview_panohead"
More Information needed
|
[
"# Dataset Card for \"multiview_panohead\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"multiview_panohead\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"multiview_panohead\"\n\nMore Information needed"
] |
bb0c6dfb39574368120672f26d7beb65eeae91cb
|
# Dataset Card for "xyz"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Moghazy/xyz
|
[
"region:us"
] |
2023-09-05T17:04:08+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 75274, "num_examples": 398}], "download_size": 16836, "dataset_size": 75274}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-05T17:04:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xyz"
More Information needed
|
[
"# Dataset Card for \"xyz\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xyz\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xyz\"\n\nMore Information needed"
] |
5df3c7572baaa3dff9ba0d78b2c5a80d3b1977b2
|
# Dataset Card for "movie_review_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Junr-syl/movie_review_test
|
[
"region:us"
] |
2023-09-05T18:00:15+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7334957, "num_examples": 5000}], "download_size": 0, "dataset_size": 7334957}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-05T18:02:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "movie_review_test"
More Information needed
|
[
"# Dataset Card for \"movie_review_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"movie_review_test\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"movie_review_test\"\n\nMore Information needed"
] |
c923d95633c4759a35a808c62f122bbf84f16ee5
|
# Dataset Card for "autotree_snnxor_n15_l1_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_snnxor_n15_l1_2
|
[
"region:us"
] |
2023-09-05T18:55:24+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "input_y_clean", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 154520000, "num_examples": 10000}, {"name": "validation", "num_bytes": 154520000, "num_examples": 10000}, {"name": "test", "num_bytes": 154520000, "num_examples": 10000}], "download_size": 185689368, "dataset_size": 463560000}}
|
2023-09-05T18:55:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_snnxor_n15_l1_2"
More Information needed
|
[
"# Dataset Card for \"autotree_snnxor_n15_l1_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_snnxor_n15_l1_2\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_snnxor_n15_l1_2\"\n\nMore Information needed"
] |
8610dfc898333162c6b56d3c416df864004a3208
|
# Dataset Card for "autotree_pmlb_100000_phoneme_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_pmlb_100000_phoneme_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-09-05T19:12:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1544800000, "num_examples": 100000}, {"name": "validation", "num_bytes": 154480000, "num_examples": 10000}], "download_size": 371906202, "dataset_size": 1699280000}}
|
2023-09-05T19:13:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_pmlb_100000_phoneme_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_pmlb_100000_phoneme_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_pmlb_100000_phoneme_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_pmlb_100000_phoneme_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
964bc24a366c0204aaab5c560cccb3d236b2495f
|
# Dataset Card for "epsilon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxie/epsilon
|
[
"region:us"
] |
2023-09-05T19:17:42+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "sequence": {"sequence": "float64"}}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9604800000, "num_examples": 400000}, {"name": "test", "num_bytes": 2401200000, "num_examples": 100000}], "download_size": 7805263919, "dataset_size": 12006000000}}
|
2023-09-05T19:40:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "epsilon"
More Information needed
|
[
"# Dataset Card for \"epsilon\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"epsilon\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"epsilon\"\n\nMore Information needed"
] |
12c7ed613c9afc83626d3c3a51c3551aa76d6ec6
|
# Dataset Card for "llama2-DS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ParthGohil19/llama2-DS
|
[
"region:us"
] |
2023-09-05T19:27:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 688, "num_examples": 172}], "download_size": 714, "dataset_size": 688}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-05T20:08:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama2-DS"
More Information needed
|
[
"# Dataset Card for \"llama2-DS\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama2-DS\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama2-DS\"\n\nMore Information needed"
] |
64dbe2282f15d9413d19b06d63212a5a73177000
|
# Dataset Card for "hp_example_data_new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Samee-ur/hp_example_data_new
|
[
"region:us"
] |
2023-09-05T19:41:20+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 459624, "num_examples": 7646}], "download_size": 0, "dataset_size": 459624}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-05T19:51:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hp_example_data_new"
More Information needed
|
[
"# Dataset Card for \"hp_example_data_new\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hp_example_data_new\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"hp_example_data_new\"\n\nMore Information needed"
] |
56756514f1e4d51a7c01c5852330245835166b91
|
# Dataset Card for "Metallography_segmenter_Dataset_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_D
|
[
"region:us"
] |
2023-09-05T19:43:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 39143882.0, "num_examples": 172}, {"name": "validation", "num_bytes": 9747778.0, "num_examples": 44}], "download_size": 48708161, "dataset_size": 48891660.0}}
|
2023-09-06T18:14:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_D"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_D\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_D\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_D\"\n\nMore Information needed"
] |
df9f14234466ae573ac86bc71f47534920210872
|
# Flair Base Model Detection
For detailed instructions of dataset generation process, please refer to this [GIST](https://gist.github.com/stefan-it/c746ed3562a9b5162f8229724d136975).
|
stefan-it/flair-base-model-detection
|
[
"license:mit",
"region:us"
] |
2023-09-05T19:51:25+00:00
|
{"license": "mit"}
|
2023-09-05T21:19:30+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# Flair Base Model Detection
For detailed instructions of dataset generation process, please refer to this GIST.
|
[
"# Flair Base Model Detection\n\nFor detailed instructions of dataset generation process, please refer to this GIST."
] |
[
"TAGS\n#license-mit #region-us \n",
"# Flair Base Model Detection\n\nFor detailed instructions of dataset generation process, please refer to this GIST."
] |
[
11,
23
] |
[
"passage: TAGS\n#license-mit #region-us \n# Flair Base Model Detection\n\nFor detailed instructions of dataset generation process, please refer to this GIST."
] |
05d3ca47f75b44f858d85dd96e8c62668fe71cfb
|
This dataset merge 3 datasets and have two setup for experiments in generalisation for multi-class clasificacitino task.
* ID, near-OOD, covariate-shitf: [CLINC150](https://github.com/clinc/oos-eval)
* ID, near-OOD, covariate-shitf: [ROSTD+OOD](https://github.com/vgtomahawk/LR_GC_OOD) (fbreleasecoarse version)
* far-OOD Validation: [SST2](https://huggingface.co/datasets/sst2)
* far-OOD Test: [News Category](https://www.kaggle.com/datasets/rmisra/news-category-dataset?resource=download) (v3)
|
cmaldona/Generalization-MultiClass-CLINC150-ROSTD
|
[
"task_categories:text-classification",
"language:en",
"license:openrail",
"region:us"
] |
2023-09-05T20:35:36+00:00
|
{"language": ["en"], "license": "openrail", "task_categories": ["text-classification"], "name": "generalization-test", "version": "1.0.0", "description": "Merge between 3 datasets.", "configs": [{"config_name": "clinc150", "default": true, "data_files": [{"split": "train", "path": "train_clinc150.csv"}, {"split": "validation", "path": "validation_clinc150.csv"}, {"split": "test", "path": "test_clinc150.csv"}]}, {"config_name": "rostd+", "data_files": [{"split": "train", "path": "train_rostd+.csv"}, {"split": "validation", "path": "val_rostd+.csv"}, {"split": "test", "path": "test_rostd+.csv"}]}]}
|
2023-12-09T18:52:24+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #language-English #license-openrail #region-us
|
This dataset merge 3 datasets and have two setup for experiments in generalisation for multi-class clasificacitino task.
* ID, near-OOD, covariate-shitf: CLINC150
* ID, near-OOD, covariate-shitf: ROSTD+OOD (fbreleasecoarse version)
* far-OOD Validation: SST2
* far-OOD Test: News Category (v3)
|
[] |
[
"TAGS\n#task_categories-text-classification #language-English #license-openrail #region-us \n"
] |
[
27
] |
[
"passage: TAGS\n#task_categories-text-classification #language-English #license-openrail #region-us \n"
] |
8059a5f82a5825d688ef38193f2bafadde3a96b4
|
# Dataset Card for "imagenet_batched_64"
Subset of ImageNet-1k batched by image size
```python
from datasets import load_dataset
import PIL.Image as Image
import io
dataset = load_dataset("danjacobellis/imagenet_batched_64")
img_batch = dataset['train'][0]['img_batch']
img = Image.open(io.BytesIO(img_batch[0]['bytes']))
img
```
|
danjacobellis/imagenet_batched_64
|
[
"region:us"
] |
2023-09-05T20:39:20+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": "img_batch", "list": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}, {"name": "label_batch", "sequence": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 29474962100, "num_examples": 11497}, {"name": "test", "num_bytes": 2439605108, "num_examples": 939}, {"name": "validation", "num_bytes": 1204052050, "num_examples": 463}], "download_size": 33102976411, "dataset_size": 33118619258}}
|
2023-09-06T14:35:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "imagenet_batched_64"
Subset of ImageNet-1k batched by image size
!png
|
[
"# Dataset Card for \"imagenet_batched_64\"\nSubset of ImageNet-1k batched by image size\n\n\n\n \n!png"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"imagenet_batched_64\"\nSubset of ImageNet-1k batched by image size\n\n\n\n \n!png"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"imagenet_batched_64\"\nSubset of ImageNet-1k batched by image size\n\n\n\n \n!png"
] |
7eb82c77cca07e01db54c5a4ca63449c7bf59158
|
# Dataset Card for "rm-cr-search"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rmadiraju/rm-cr-search
|
[
"region:us"
] |
2023-09-05T20:46:57+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19941, "num_examples": 9}], "download_size": 19959, "dataset_size": 19941}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-06T18:32:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rm-cr-search"
More Information needed
|
[
"# Dataset Card for \"rm-cr-search\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rm-cr-search\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rm-cr-search\"\n\nMore Information needed"
] |
1fac80fe7c2353fc7feebe7bc1e45c1b83720a40
|
# SpanMarker Base Model Detection
It is relative simply to determine base model of a fine-tuned SpanMarker model:
```python
import os
from huggingface_hub import login, HfApi
hf_token = os.environ.get("HF_TOKEN")
login(token=hf_token, add_to_git_credential=True)
api = HfApi()
```
Please make sure that `HF_TOKEN` is set as environment variable.
After that, list of all SpanMarker models can be retrieved and configuration file is parsed.
Please make sure that `span-marker` library is installed:
```python
from span_marker import SpanMarkerConfig
f_out = open("span_marker_base_model_detection.csv", "wt")
f_out.write("Nr,Model ID,Base Model ID\n")
counter = 1
for span_marker_model in api.list_models(filter="span-marker"):
try:
config = SpanMarkerConfig.from_pretrained(span_marker_model.modelId)
base_model = config.encoder["_name_or_path"]
f_out.write(f"{counter},{span_marker_model.modelId},{base_model}\n")
counter +=1
except Exception as e:
print(e)
f_out.close()
```
|
stefan-it/span-marker-base-model-detection
|
[
"license:mit",
"region:us"
] |
2023-09-05T21:15:28+00:00
|
{"license": "mit"}
|
2023-09-05T21:22:04+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# SpanMarker Base Model Detection
It is relative simply to determine base model of a fine-tuned SpanMarker model:
Please make sure that 'HF_TOKEN' is set as environment variable.
After that, list of all SpanMarker models can be retrieved and configuration file is parsed.
Please make sure that 'span-marker' library is installed:
|
[
"# SpanMarker Base Model Detection\n\nIt is relative simply to determine base model of a fine-tuned SpanMarker model:\n\n\n\nPlease make sure that 'HF_TOKEN' is set as environment variable.\n\nAfter that, list of all SpanMarker models can be retrieved and configuration file is parsed.\nPlease make sure that 'span-marker' library is installed:"
] |
[
"TAGS\n#license-mit #region-us \n",
"# SpanMarker Base Model Detection\n\nIt is relative simply to determine base model of a fine-tuned SpanMarker model:\n\n\n\nPlease make sure that 'HF_TOKEN' is set as environment variable.\n\nAfter that, list of all SpanMarker models can be retrieved and configuration file is parsed.\nPlease make sure that 'span-marker' library is installed:"
] |
[
11,
81
] |
[
"passage: TAGS\n#license-mit #region-us \n# SpanMarker Base Model Detection\n\nIt is relative simply to determine base model of a fine-tuned SpanMarker model:\n\n\n\nPlease make sure that 'HF_TOKEN' is set as environment variable.\n\nAfter that, list of all SpanMarker models can be retrieved and configuration file is parsed.\nPlease make sure that 'span-marker' library is installed:"
] |
3e9909d3715527e421e45b1cd44a826265c7bae3
|
# Dataset Card for "e7874b25"
[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/e7874b25
|
[
"region:us"
] |
2023-09-05T21:33:51+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1341, "dataset_size": 182}}
|
2023-09-05T21:33:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "e7874b25"
More Information needed
|
[
"# Dataset Card for \"e7874b25\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"e7874b25\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"e7874b25\"\n\nMore Information needed"
] |
602ebed46e519b84f7a885585ccf25ae4a94b47a
|
# Dataset Card for detect-waste
## Dataset Description
- **Homepage: https://github.com/wimlds-trojmiasto/detect-waste**
### Dataset Summary
AI4Good project for detecting waste in environment. www.detectwaste.ml.
Our latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments.
You can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment.
Did you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled.
The idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good.
### Supported Tasks and Leaderboards
Object Detection
### Languages
English
### Data Fields
https://github.com/wimlds-trojmiasto/detect-waste/tree/main/annotations
## Dataset Creation
The images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed
### Personal and Sensitive Information
**BEWARE** This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/
## Considerations for Using the Data
### Licensing Information
https://raw.githubusercontent.com/wimlds-trojmiasto/detect-waste/main/LICENSE
|
Yorai/detect-waste_loading_script
|
[
"size_categories:1K<n<10K",
"language:en",
"climate",
"region:us"
] |
2023-09-05T21:41:33+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "pretty_name": "detect-waste", "dataset_info": {"config_name": "taco-multi", "features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "metals_and_plastic", "1": "other", "2": "non_recyclable", "3": "glass", "4": "paper", "5": "bio", "6": "unknown"}}}}]}], "splits": [{"name": "train", "num_bytes": 1006510, "num_examples": 3647}, {"name": "test", "num_bytes": 248312, "num_examples": 915}], "download_size": 10265127938, "dataset_size": 1254822}, "tags": ["climate"]}
|
2023-09-05T22:06:39+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-1K<n<10K #language-English #climate #region-us
|
# Dataset Card for detect-waste
## Dataset Description
- Homepage: URL
### Dataset Summary
AI4Good project for detecting waste in environment. URL.
Our latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments.
You can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment.
Did you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled.
The idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good.
### Supported Tasks and Leaderboards
Object Detection
### Languages
English
### Data Fields
URL
## Dataset Creation
The images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed
### Personal and Sensitive Information
BEWARE This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/
## Considerations for Using the Data
### Licensing Information
URL
|
[
"# Dataset Card for detect-waste",
"## Dataset Description\n\n- Homepage: URL",
"### Dataset Summary\n\nAI4Good project for detecting waste in environment. URL.\n\nOur latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments.\n\nYou can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment.\n\nDid you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled.\n\nThe idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good.",
"### Supported Tasks and Leaderboards\n\nObject Detection",
"### Languages\n\nEnglish",
"### Data Fields\n\nURL",
"## Dataset Creation\n\nThe images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed",
"### Personal and Sensitive Information\n\nBEWARE This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/",
"## Considerations for Using the Data",
"### Licensing Information\n\nURL"
] |
[
"TAGS\n#size_categories-1K<n<10K #language-English #climate #region-us \n",
"# Dataset Card for detect-waste",
"## Dataset Description\n\n- Homepage: URL",
"### Dataset Summary\n\nAI4Good project for detecting waste in environment. URL.\n\nOur latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments.\n\nYou can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment.\n\nDid you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled.\n\nThe idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good.",
"### Supported Tasks and Leaderboards\n\nObject Detection",
"### Languages\n\nEnglish",
"### Data Fields\n\nURL",
"## Dataset Creation\n\nThe images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed",
"### Personal and Sensitive Information\n\nBEWARE This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/",
"## Considerations for Using the Data",
"### Licensing Information\n\nURL"
] |
[
26,
9,
8,
146,
13,
5,
6,
52,
41,
8,
7
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #language-English #climate #region-us \n# Dataset Card for detect-waste## Dataset Description\n\n- Homepage: URL### Dataset Summary\n\nAI4Good project for detecting waste in environment. URL.\n\nOur latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments.\n\nYou can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment.\n\nDid you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled.\n\nThe idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good.### Supported Tasks and Leaderboards\n\nObject Detection### Languages\n\nEnglish### Data Fields\n\nURL## Dataset Creation\n\nThe images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed### Personal and Sensitive Information\n\nBEWARE This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/## Considerations for Using the Data### Licensing Information\n\nURL"
] |
c6415a584dd88af041a42f4603a56efb9a2468e0
|
# Dataset Card for "epsilon-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxie/epsilon-normalized
|
[
"region:us"
] |
2023-09-05T21:47:01+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "sequence": {"sequence": "float64"}}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9604800000, "num_examples": 400000}, {"name": "test", "num_bytes": 2401200000, "num_examples": 100000}], "download_size": 6279601264, "dataset_size": 12006000000}}
|
2023-09-05T21:57:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "epsilon-normalized"
More Information needed
|
[
"# Dataset Card for \"epsilon-normalized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"epsilon-normalized\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"epsilon-normalized\"\n\nMore Information needed"
] |
410892ceedfd7136c65c57bfd6d976c2a8f80c3f
|
# Dataset Card for "ai-traffic-flows"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fmops/ai-traffic-flows
|
[
"region:us"
] |
2023-09-05T22:01:18+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1361185.4960141717, "num_examples": 1693}, {"name": "test", "num_bytes": 454264.50398582814, "num_examples": 565}], "download_size": 1527131, "dataset_size": 1815450.0}}
|
2023-09-05T22:01:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ai-traffic-flows"
More Information needed
|
[
"# Dataset Card for \"ai-traffic-flows\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ai-traffic-flows\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ai-traffic-flows\"\n\nMore Information needed"
] |
089f6c4d9bbfdfcbc1ce66f671a9107a5b005225
|
# Dataset Card for "Sentiment-Analysis-on-appReviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yashika0998/Sentiment-Analysis-on-appReviews
|
[
"region:us"
] |
2023-09-05T22:56:52+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 5367302.1, "num_examples": 45000}, {"name": "test", "num_bytes": 596366.9, "num_examples": 5000}], "download_size": 0, "dataset_size": 5963669.0}}
|
2023-09-18T00:25:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Sentiment-Analysis-on-appReviews"
More Information needed
|
[
"# Dataset Card for \"Sentiment-Analysis-on-appReviews\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Sentiment-Analysis-on-appReviews\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Sentiment-Analysis-on-appReviews\"\n\nMore Information needed"
] |
fde8ef8de2300f5e778f56261843dab89f230815
|
<img src="imgs/OpenWebMath-left.png" width="300">
[Keiran Paster](https://keirp.com)\*, [Marco Dos Santos](https://marco-dossantos.github.io/)\*, [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Jimmy Ba](https://jimmylba.github.io/)
[GitHub ](https://github.com/keirp/OpenWebMath) | [ArXiv](https://arxiv.org/abs/2310.06786)
| [PDF](https://arxiv.org/pdf/2310.06786.pdf)
**OpenWebMath** is a dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of **6.3 million documents** containing a total of **14.7B tokens**. OpenWebMath is intended for use in _pretraining_ and _finetuning_ large language models.
You can download the dataset using Hugging Face:
```python
from datasets import load_dataset
ds = load_dataset("open-web-math/open-web-math")
```
# OpenWebMath Contents
The dataset is structured as follows:
```python
{
"text": ..., # document text.
"url": ..., # document url.
"date": ..., # date the page was crawled.
"metadata": ..., # JSON containing information from the extraction process.
}
```
OpenWebMath contains documents from over 130k different domains, including data from forums, educational pages, and blogs. The dataset contains documents covering mathematics, physics, statistics, computer science, and more. The following table shows the most common domains in OpenWebMath by character count.
| Domain | # Characters | % Characters |
| ----------------- | ------------- | ------------ |
| stackexchange.com | 4,655,132,784 | 9.55% |
| nature.com | 1,529,935,838 | 3.14% |
| wordpress.com | 1,294,166,938 | 2.66% |
| physicsforums.com | 1,160,137,919 | 2.38% |
| github.io | 725,689,722 | 1.49% |
| zbmath.org | 620,019,503 | 1.27% |
| wikipedia.org | 618,024,754 | 1.27% |
| groundai.com | 545,214,990 | 1.12% |
| blogspot.com | 520,392,333 | 1.07% |
| mathoverflow.net | 499,102,560 | 1.02% |
# OpenWebMath Pipeline
<img src="imgs/pipeline.png" alt="Overview of the OpenWebMath Pipeline">
OpenWebMath builds on the massive [Common Crawl](https://commoncrawl.org/) dataset, which contains over 200B HTML documents. We filtered the data to only include documents that are: (1) in English, (2) contain mathematical content, and (3) are of high quality. We also put a strong emphasis on extracting LaTeX content from the HTML documents as well as reducing boilerplate in comparison to other web datasets.
The OpenWebMath pipeline consists of five steps:
1. **Prefiltering HTML Documents**:
- We apply a simple prefilter to all HTML documents in Common Crawl in order to skip documents without mathematical content to unnecessary processing time.
2. **Text Extraction**:
- Extract text, including LaTeX content, from the HTML documents while removing boilerplate.
3. **Content Classification and Filtering**:
- Apply a [FastText language identification model](https://fasttext.cc/docs/en/language-identification.html) to keep only English documents.
- Filter high perplexity documents using a [KenLM](https://github.com/kpu/kenlm) model trained on [Proof-Pile](https://huggingface.co/datasets/hoskinson-center/proof-pile).
- Filter non-mathematical documents using our own _MathScore_ model.
4. **Deduplication**:
- Deduplicate the dataset using SimHash in [text-dedup](https://github.com/ChenghaoMou/text-dedup).
5. **Manual Inspection**:
- Inspect the documents gathered from previous steps and remove low quality pages.
For a detailed discussion on the processing pipeline, please refer to our paper.
# License
OpenWebMath is made available under an ODC-By 1.0 license; users should also abide by the CommonCrawl ToU: [https://commoncrawl.org/terms-of-use/](https://commoncrawl.org/terms-of-use/). We do not alter the license of any of the underlying data.
# Citation Information
```
@misc{paster2023openwebmath,
title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text},
author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba},
year={2023},
eprint={2310.06786},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
|
open-web-math/open-web-math
|
[
"arxiv:2310.06786",
"region:us"
] |
2023-09-05T23:25:12+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "metadata", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56651995057, "num_examples": 6315233}], "download_size": 16370689925, "dataset_size": 56651995057, "license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "OpenWebMath", "size_categories": ["10B<n<100B"]}}
|
2023-10-17T19:14:00+00:00
|
[
"2310.06786"
] |
[] |
TAGS
#arxiv-2310.06786 #region-us
|
Keiran Paster\*, Marco Dos Santos\*, Zhangir Azerbayev, Jimmy Ba
GitHub | ArXiv
| PDF
OpenWebMath is a dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of 6.3 million documents containing a total of 14.7B tokens. OpenWebMath is intended for use in *pretraining* and *finetuning* large language models.
You can download the dataset using Hugging Face:
OpenWebMath Contents
====================
The dataset is structured as follows:
OpenWebMath contains documents from over 130k different domains, including data from forums, educational pages, and blogs. The dataset contains documents covering mathematics, physics, statistics, computer science, and more. The following table shows the most common domains in OpenWebMath by character count.
Domain: URL, # Characters: 4,655,132,784, % Characters: 9.55%
Domain: URL, # Characters: 1,529,935,838, % Characters: 3.14%
Domain: URL, # Characters: 1,294,166,938, % Characters: 2.66%
Domain: URL, # Characters: 1,160,137,919, % Characters: 2.38%
Domain: URL, # Characters: 725,689,722, % Characters: 1.49%
Domain: URL, # Characters: 620,019,503, % Characters: 1.27%
Domain: URL, # Characters: 618,024,754, % Characters: 1.27%
Domain: URL, # Characters: 545,214,990, % Characters: 1.12%
Domain: URL, # Characters: 520,392,333, % Characters: 1.07%
Domain: URL, # Characters: 499,102,560, % Characters: 1.02%
OpenWebMath Pipeline
====================
OpenWebMath builds on the massive Common Crawl dataset, which contains over 200B HTML documents. We filtered the data to only include documents that are: (1) in English, (2) contain mathematical content, and (3) are of high quality. We also put a strong emphasis on extracting LaTeX content from the HTML documents as well as reducing boilerplate in comparison to other web datasets.
The OpenWebMath pipeline consists of five steps:
1. Prefiltering HTML Documents:
* We apply a simple prefilter to all HTML documents in Common Crawl in order to skip documents without mathematical content to unnecessary processing time.
2. Text Extraction:
* Extract text, including LaTeX content, from the HTML documents while removing boilerplate.
3. Content Classification and Filtering:
* Apply a FastText language identification model to keep only English documents.
* Filter high perplexity documents using a KenLM model trained on Proof-Pile.
* Filter non-mathematical documents using our own *MathScore* model.
4. Deduplication:
* Deduplicate the dataset using SimHash in text-dedup.
5. Manual Inspection:
* Inspect the documents gathered from previous steps and remove low quality pages.
For a detailed discussion on the processing pipeline, please refer to our paper.
License
=======
OpenWebMath is made available under an ODC-By 1.0 license; users should also abide by the CommonCrawl ToU: URL We do not alter the license of any of the underlying data.
|
[
"# Characters: 4,655,132,784, % Characters: 9.55%\nDomain: URL, # Characters: 1,529,935,838, % Characters: 3.14%\nDomain: URL, # Characters: 1,294,166,938, % Characters: 2.66%\nDomain: URL, # Characters: 1,160,137,919, % Characters: 2.38%\nDomain: URL, # Characters: 725,689,722, % Characters: 1.49%\nDomain: URL, # Characters: 620,019,503, % Characters: 1.27%\nDomain: URL, # Characters: 618,024,754, % Characters: 1.27%\nDomain: URL, # Characters: 545,214,990, % Characters: 1.12%\nDomain: URL, # Characters: 520,392,333, % Characters: 1.07%\nDomain: URL, # Characters: 499,102,560, % Characters: 1.02%\n\n\nOpenWebMath Pipeline\n====================\n\n\n\nOpenWebMath builds on the massive Common Crawl dataset, which contains over 200B HTML documents. We filtered the data to only include documents that are: (1) in English, (2) contain mathematical content, and (3) are of high quality. We also put a strong emphasis on extracting LaTeX content from the HTML documents as well as reducing boilerplate in comparison to other web datasets.\n\n\nThe OpenWebMath pipeline consists of five steps:\n\n\n1. Prefiltering HTML Documents:\n\t* We apply a simple prefilter to all HTML documents in Common Crawl in order to skip documents without mathematical content to unnecessary processing time.\n2. Text Extraction:\n\t* Extract text, including LaTeX content, from the HTML documents while removing boilerplate.\n3. Content Classification and Filtering:\n\t* Apply a FastText language identification model to keep only English documents.\n\t* Filter high perplexity documents using a KenLM model trained on Proof-Pile.\n\t* Filter non-mathematical documents using our own *MathScore* model.\n4. Deduplication:\n\t* Deduplicate the dataset using SimHash in text-dedup.\n5. Manual Inspection:\n\t* Inspect the documents gathered from previous steps and remove low quality pages.\n\n\nFor a detailed discussion on the processing pipeline, please refer to our paper.\n\n\nLicense\n=======\n\n\nOpenWebMath is made available under an ODC-By 1.0 license; users should also abide by the CommonCrawl ToU: URL We do not alter the license of any of the underlying data."
] |
[
"TAGS\n#arxiv-2310.06786 #region-us \n",
"# Characters: 4,655,132,784, % Characters: 9.55%\nDomain: URL, # Characters: 1,529,935,838, % Characters: 3.14%\nDomain: URL, # Characters: 1,294,166,938, % Characters: 2.66%\nDomain: URL, # Characters: 1,160,137,919, % Characters: 2.38%\nDomain: URL, # Characters: 725,689,722, % Characters: 1.49%\nDomain: URL, # Characters: 620,019,503, % Characters: 1.27%\nDomain: URL, # Characters: 618,024,754, % Characters: 1.27%\nDomain: URL, # Characters: 545,214,990, % Characters: 1.12%\nDomain: URL, # Characters: 520,392,333, % Characters: 1.07%\nDomain: URL, # Characters: 499,102,560, % Characters: 1.02%\n\n\nOpenWebMath Pipeline\n====================\n\n\n\nOpenWebMath builds on the massive Common Crawl dataset, which contains over 200B HTML documents. We filtered the data to only include documents that are: (1) in English, (2) contain mathematical content, and (3) are of high quality. We also put a strong emphasis on extracting LaTeX content from the HTML documents as well as reducing boilerplate in comparison to other web datasets.\n\n\nThe OpenWebMath pipeline consists of five steps:\n\n\n1. Prefiltering HTML Documents:\n\t* We apply a simple prefilter to all HTML documents in Common Crawl in order to skip documents without mathematical content to unnecessary processing time.\n2. Text Extraction:\n\t* Extract text, including LaTeX content, from the HTML documents while removing boilerplate.\n3. Content Classification and Filtering:\n\t* Apply a FastText language identification model to keep only English documents.\n\t* Filter high perplexity documents using a KenLM model trained on Proof-Pile.\n\t* Filter non-mathematical documents using our own *MathScore* model.\n4. Deduplication:\n\t* Deduplicate the dataset using SimHash in text-dedup.\n5. Manual Inspection:\n\t* Inspect the documents gathered from previous steps and remove low quality pages.\n\n\nFor a detailed discussion on the processing pipeline, please refer to our paper.\n\n\nLicense\n=======\n\n\nOpenWebMath is made available under an ODC-By 1.0 license; users should also abide by the CommonCrawl ToU: URL We do not alter the license of any of the underlying data."
] |
[
15,
597
] |
[
"passage: TAGS\n#arxiv-2310.06786 #region-us \n"
] |
a844eac7f281a6aea48dc81683c3a5bf92818726
|
# Dataset Card for "civil_comments_hatebert"
This is an experiment to see how "civil-comments" can be changed by models without much manipulation to offensive speech in certain cases.
This data is a reformat of the civil comments dataset, discarding all scoring attributes of abusive speech, masking random tokens, and processing with hatebert to fill-masked tokens with possible abusive language.
This merely sets up some good data for three things: fill-mask activities, text training, and scored responses based on random tokens being manipulatible according to this model.
Showing the progress of incarnation, three columns illustrate the original text data extracted, the randomly masked text, and the filled text with scores in a list for the hatebert output.
So far in practice, the hatebert model mostly fills with innocuous placeholders, from *very* limited testing.
Hatebert is as it sounds, a BERT based model trained on fill-mask activites.
[civil_comments dataset](https://huggingface.co/datasets/civil_comments)
[hatebert model](https://huggingface.co/datasets/civil_comments)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jtatman/civil_comments_hatebert
|
[
"task_categories:text-classification",
"task_categories:text2text-generation",
"task_categories:fill-mask",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"masked",
"mask-scored",
"comment scoring",
"masked-model",
"region:us"
] |
2023-09-05T23:41:24+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "text2text-generation", "fill-mask"], "pretty_name": "civil comments w/hatebert scoring", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "text_masked", "dtype": "string"}, {"name": "text_replaced", "list": [{"name": "score", "dtype": "float64"}, {"name": "sequence", "dtype": "string"}, {"name": "token", "dtype": "int64"}, {"name": "token_str", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 872262083, "num_examples": 451219}], "download_size": 333147199, "dataset_size": 872262083}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["masked", "mask-scored", "comment scoring", "masked-model"]}
|
2023-09-06T07:15:58+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #task_categories-text2text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-English #license-mit #masked #mask-scored #comment scoring #masked-model #region-us
|
# Dataset Card for "civil_comments_hatebert"
This is an experiment to see how "civil-comments" can be changed by models without much manipulation to offensive speech in certain cases.
This data is a reformat of the civil comments dataset, discarding all scoring attributes of abusive speech, masking random tokens, and processing with hatebert to fill-masked tokens with possible abusive language.
This merely sets up some good data for three things: fill-mask activities, text training, and scored responses based on random tokens being manipulatible according to this model.
Showing the progress of incarnation, three columns illustrate the original text data extracted, the randomly masked text, and the filled text with scores in a list for the hatebert output.
So far in practice, the hatebert model mostly fills with innocuous placeholders, from *very* limited testing.
Hatebert is as it sounds, a BERT based model trained on fill-mask activites.
civil_comments dataset
hatebert model
More Information needed
|
[
"# Dataset Card for \"civil_comments_hatebert\"\n\nThis is an experiment to see how \"civil-comments\" can be changed by models without much manipulation to offensive speech in certain cases.\n\nThis data is a reformat of the civil comments dataset, discarding all scoring attributes of abusive speech, masking random tokens, and processing with hatebert to fill-masked tokens with possible abusive language. \nThis merely sets up some good data for three things: fill-mask activities, text training, and scored responses based on random tokens being manipulatible according to this model. \nShowing the progress of incarnation, three columns illustrate the original text data extracted, the randomly masked text, and the filled text with scores in a list for the hatebert output. \nSo far in practice, the hatebert model mostly fills with innocuous placeholders, from *very* limited testing.\n\nHatebert is as it sounds, a BERT based model trained on fill-mask activites. \n\n\ncivil_comments dataset\nhatebert model\n\n\nMore Information needed"
] |
[
"TAGS\n#task_categories-text-classification #task_categories-text2text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-English #license-mit #masked #mask-scored #comment scoring #masked-model #region-us \n",
"# Dataset Card for \"civil_comments_hatebert\"\n\nThis is an experiment to see how \"civil-comments\" can be changed by models without much manipulation to offensive speech in certain cases.\n\nThis data is a reformat of the civil comments dataset, discarding all scoring attributes of abusive speech, masking random tokens, and processing with hatebert to fill-masked tokens with possible abusive language. \nThis merely sets up some good data for three things: fill-mask activities, text training, and scored responses based on random tokens being manipulatible according to this model. \nShowing the progress of incarnation, three columns illustrate the original text data extracted, the randomly masked text, and the filled text with scores in a list for the hatebert output. \nSo far in practice, the hatebert model mostly fills with innocuous placeholders, from *very* limited testing.\n\nHatebert is as it sounds, a BERT based model trained on fill-mask activites. \n\n\ncivil_comments dataset\nhatebert model\n\n\nMore Information needed"
] |
[
81,
247
] |
[
"passage: TAGS\n#task_categories-text-classification #task_categories-text2text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-English #license-mit #masked #mask-scored #comment scoring #masked-model #region-us \n# Dataset Card for \"civil_comments_hatebert\"\n\nThis is an experiment to see how \"civil-comments\" can be changed by models without much manipulation to offensive speech in certain cases.\n\nThis data is a reformat of the civil comments dataset, discarding all scoring attributes of abusive speech, masking random tokens, and processing with hatebert to fill-masked tokens with possible abusive language. \nThis merely sets up some good data for three things: fill-mask activities, text training, and scored responses based on random tokens being manipulatible according to this model. \nShowing the progress of incarnation, three columns illustrate the original text data extracted, the randomly masked text, and the filled text with scores in a list for the hatebert output. \nSo far in practice, the hatebert model mostly fills with innocuous placeholders, from *very* limited testing.\n\nHatebert is as it sounds, a BERT based model trained on fill-mask activites. \n\n\ncivil_comments dataset\nhatebert model\n\n\nMore Information needed"
] |
74f2420a625cc2a828ad25e6062742cd4583ecfc
|
# Dataset Card for "dreambooth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ImagenHub/DreamBooth_Concepts
|
[
"region:us"
] |
2023-09-05T23:57:27+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "subject", "dtype": "string"}, {"name": "identifier", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6660939.0, "num_examples": 158}], "download_size": 6655808, "dataset_size": 6660939.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-08T18:19:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dreambooth"
More Information needed
|
[
"# Dataset Card for \"dreambooth\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dreambooth\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth\"\n\nMore Information needed"
] |
e3ee0c27d904d11d4a12c452cbc10ace22c72772
|
# Dataset Card for "flare-es-financees"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChanceFocus/flare-es-financees
|
[
"region:us"
] |
2023-09-05T23:58:16+00:00
|
{"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "gold", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1921453, "num_examples": 6359}], "download_size": 740681, "dataset_size": 1921453}}
|
2023-12-15T08:55:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flare-es-financees"
More Information needed
|
[
"# Dataset Card for \"flare-es-financees\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flare-es-financees\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"flare-es-financees\"\n\nMore Information needed"
] |
0339ec53d058363dfb1010df9aa2ee9f9a224fdc
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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]
|
selenalu/data
|
[
"region:us"
] |
2023-09-06T00:10:06+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "**/*.jsonl"}]}]}
|
2023-09-06T01:53:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
### 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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"### Licensing Information",
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"### Contributions"
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8,
24,
6,
10,
4,
6,
6,
5,
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5,
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4,
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9,
8,
8,
7,
8,
7,
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6,
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] |
0435f7917f5f2f2012402841e271874e889720f7
|
# Dataset Card for "waste-classification-audio-helsinki2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
thomasavare/waste-classification-audio-helsinki2
|
[
"region:us"
] |
2023-09-06T00:14:41+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "speaker", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "translation", "dtype": "string"}, {"name": "Class", "dtype": "string"}, {"name": "Class_index", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 190035689.0, "num_examples": 500}], "download_size": 190018067, "dataset_size": 190035689.0}}
|
2023-09-13T00:05:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "waste-classification-audio-helsinki2"
More Information needed
|
[
"# Dataset Card for \"waste-classification-audio-helsinki2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"waste-classification-audio-helsinki2\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"waste-classification-audio-helsinki2\"\n\nMore Information needed"
] |
57a1e53542003787298c904263c97aa87efc617d
|
# Dataset Card for "autotree_pmlb_100000_spambase_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_pmlb_100000_spambase_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-09-06T00:36:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3649158912, "num_examples": 100000}, {"name": "validation", "num_bytes": 364882304, "num_examples": 10000}], "download_size": 643796701, "dataset_size": 4014041216}}
|
2023-09-06T00:37:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_pmlb_100000_spambase_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_pmlb_100000_spambase_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_pmlb_100000_spambase_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_pmlb_100000_spambase_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
948bb0899abc71334ca464625d1561f13b82bf25
|
The data in this datasets repository is associated with the following NER models to identify 6 vulnerability types in Python source code:
https://huggingface.co/mmeberg/RoRo_PyVulDet_NER
https://huggingface.co/mmeberg/RoCo_PyVulDet_NER
https://huggingface.co/mmeberg/DiDi_PyVulDet_NER
https://huggingface.co/mmeberg/CoRo_PyVulDet_NER
https://huggingface.co/mmeberg/CoCo_PyVulDet_NER
In addition, a manuscript paper has been submitted detailing this work to the DevSecOps: Advances for Secure Software Development special issue in Computers & Security.
This research is part of an in-progess dissertation for George Washington University.
|
mmeberg/PyVulDet-NER
|
[
"task_categories:token-classification",
"language:en",
"code",
"region:us"
] |
2023-09-06T00:45:08+00:00
|
{"language": ["en"], "task_categories": ["token-classification"], "tags": ["code"]}
|
2023-09-14T22:59:08+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-token-classification #language-English #code #region-us
|
The data in this datasets repository is associated with the following NER models to identify 6 vulnerability types in Python source code:
URL
URL
URL
URL
URL
In addition, a manuscript paper has been submitted detailing this work to the DevSecOps: Advances for Secure Software Development special issue in Computers & Security.
This research is part of an in-progess dissertation for George Washington University.
|
[] |
[
"TAGS\n#task_categories-token-classification #language-English #code #region-us \n"
] |
[
24
] |
[
"passage: TAGS\n#task_categories-token-classification #language-English #code #region-us \n"
] |
eac4f4e01501730c8e89acae216c0fc0f6db3783
|
The dataset is created by
1. translating English questions of [Evol-instruct-70k](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_70k) into Arabic using **GPT4**, and
2. requesting **GPT4** to generate responses in Arabic.
For more details, please refer to:
- **Repository**:
- https://github.com/FreedomIntelligence/AceGPT
- https://github.com/FreedomIntelligence/LLMZoo
- **Paper**:
- [AceGPT, Localizing Large Language Models in Arabic](https://arxiv.org/abs/2309.12053)
- [Phoenix: Democratizing ChatGPT across Languages](https://arxiv.org/abs/2304.10453)
### BibTeX entry and citation info
```bibtex
@article{huang2023acegpt,
title={AceGPT, Localizing Large Language Models in Arabic},
author={Huang, Huang and Yu, Fei and Zhu, Jianqing and Sun, Xuening and Cheng, Hao and Song, Dingjie and Chen, Zhihong and Alharthi, Abdulmohsen and An, Bang and Liu, Ziche and others},
journal={arXiv preprint arXiv:2309.12053},
year={2023}
}
@article{chen2023phoenix,
title={Phoenix: Democratizing chatgpt across languages},
author={Chen, Zhihong and Jiang, Feng and Chen, Junying and Wang, Tiannan and Yu, Fei and Chen, Guiming and Zhang, Hongbo and Liang, Juhao and Zhang, Chen and Zhang, Zhiyi and others},
journal={arXiv preprint arXiv:2304.10453},
year={2023}
}
```
|
FreedomIntelligence/Evol-Instruct-Arabic-GPT4
|
[
"task_categories:conversational",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:ar",
"license:apache-2.0",
"arxiv:2309.12053",
"arxiv:2304.10453",
"region:us"
] |
2023-09-06T01:03:04+00:00
|
{"language": ["ar"], "license": "apache-2.0", "size_categories": ["10M<n<100M"], "task_categories": ["conversational", "text2text-generation", "text-generation"], "dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 169019459, "num_examples": 69997}], "download_size": 0, "dataset_size": 169019459}}
|
2023-12-06T03:54:06+00:00
|
[
"2309.12053",
"2304.10453"
] |
[
"ar"
] |
TAGS
#task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-10M<n<100M #language-Arabic #license-apache-2.0 #arxiv-2309.12053 #arxiv-2304.10453 #region-us
|
The dataset is created by
1. translating English questions of Evol-instruct-70k into Arabic using GPT4, and
2. requesting GPT4 to generate responses in Arabic.
For more details, please refer to:
- Repository:
- URL
- URL
- Paper:
- AceGPT, Localizing Large Language Models in Arabic
- Phoenix: Democratizing ChatGPT across Languages
### BibTeX entry and citation info
|
[
"### BibTeX entry and citation info"
] |
[
"TAGS\n#task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-10M<n<100M #language-Arabic #license-apache-2.0 #arxiv-2309.12053 #arxiv-2304.10453 #region-us \n",
"### BibTeX entry and citation info"
] |
[
81,
11
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[
"passage: TAGS\n#task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-10M<n<100M #language-Arabic #license-apache-2.0 #arxiv-2309.12053 #arxiv-2304.10453 #region-us \n### BibTeX entry and citation info"
] |
1856d76a7be8f965c3f6f148ec3b9d81aed9ffbe
|
# Dataset Card for "esco_occupations_details_multilingual"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
serbog/esco_occupations_details_multilingual
|
[
"region:us"
] |
2023-09-06T01:34:49+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "el", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "lt", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "code", "dtype": "string"}, {"name": "uk", "struct": [{"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "ga", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "sv", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "cs", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "bg", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "no", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "en", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "lv", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "ar", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "es", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "et", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "fi", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "sk", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "da", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "nl", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "is", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "sl", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "hr", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "pl", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "it", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "de", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "url", "dtype": "string"}, {"name": "mt", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "hu", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "fr", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "pt", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}, {"name": "ro", "struct": [{"name": "alternativeLabel", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "preferredLabel", "dtype": "string"}, {"name": "preferredTerm", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 52470213, "num_examples": 3629}], "download_size": 22696020, "dataset_size": 52470213}}
|
2023-09-06T01:34:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "esco_occupations_details_multilingual"
More Information needed
|
[
"# Dataset Card for \"esco_occupations_details_multilingual\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"esco_occupations_details_multilingual\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"esco_occupations_details_multilingual\"\n\nMore Information needed"
] |
c4aad1451c0ee050945c0963ce39770818e44661
|
## RobuT Dataset
A benchmark based on existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and includes human-annotated adversarial perturbations in terms of table header, table content, and question.
## Code
Please refer to our [github repo](https://github.com/yilunzhao/RobuT) for code implementation.
## Contact
For any issues or questions, kindly email us at: Yilun Zhao ([email protected]).
## Citation
```
@inproceedings{zhao-etal-2023-robut,
title = "{R}obu{T}: A Systematic Study of Table {QA} Robustness Against Human-Annotated Adversarial Perturbations",
author = "Zhao, Yilun and
Zhao, Chen and
Nan, Linyong and
Qi, Zhenting and
Zhang, Wenlin and
Tang, Xiangru and
Mi, Boyu and
Radev, Dragomir",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.334",
doi = "10.18653/v1/2023.acl-long.334",
pages = "6064--6081",
}
```
|
yilunzhao/robut
|
[
"license:mit",
"region:us"
] |
2023-09-06T01:39:27+00:00
|
{"license": "mit"}
|
2023-09-06T01:46:45+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
## RobuT Dataset
A benchmark based on existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and includes human-annotated adversarial perturbations in terms of table header, table content, and question.
## Code
Please refer to our github repo for code implementation.
## Contact
For any issues or questions, kindly email us at: Yilun Zhao (URL@URL).
|
[
"## RobuT Dataset\nA benchmark based on existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and includes human-annotated adversarial perturbations in terms of table header, table content, and question.",
"## Code\nPlease refer to our github repo for code implementation.",
"## Contact\nFor any issues or questions, kindly email us at: Yilun Zhao (URL@URL)."
] |
[
"TAGS\n#license-mit #region-us \n",
"## RobuT Dataset\nA benchmark based on existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and includes human-annotated adversarial perturbations in terms of table header, table content, and question.",
"## Code\nPlease refer to our github repo for code implementation.",
"## Contact\nFor any issues or questions, kindly email us at: Yilun Zhao (URL@URL)."
] |
[
11,
55,
13,
24
] |
[
"passage: TAGS\n#license-mit #region-us \n## RobuT Dataset\nA benchmark based on existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and includes human-annotated adversarial perturbations in terms of table header, table content, and question.## Code\nPlease refer to our github repo for code implementation.## Contact\nFor any issues or questions, kindly email us at: Yilun Zhao (URL@URL)."
] |
2f7707d751d8a13675690d8e1e0880e0ec61dba2
|
# Dataset Card for "hdnj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ancient237/hdnj
|
[
"region:us"
] |
2023-09-06T01:47:41+00:00
|
{"dataset_info": {"features": [{"name": "Paragraphs", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 302224.9504814305, "num_examples": 2147}], "download_size": 188515, "dataset_size": 302224.9504814305}}
|
2023-09-06T03:22:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hdnj"
More Information needed
|
[
"# Dataset Card for \"hdnj\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hdnj\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"hdnj\"\n\nMore Information needed"
] |
c831ac04fda1440834065431ae827d9b0eb1d3c0
|
# Dataset Card for "dolly_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Yehoon/dolly_test
|
[
"region:us"
] |
2023-09-06T01:52:49+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 89631, "num_examples": 100}], "download_size": 61952, "dataset_size": 89631}}
|
2023-09-07T02:04:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dolly_test"
More Information needed
|
[
"# Dataset Card for \"dolly_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dolly_test\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dolly_test\"\n\nMore Information needed"
] |
8451c3b9257e6d9efccdc8c67014738e3d802c88
|
# Dataset Card
Dataset in [ImagenHub](arxiv.org/abs/2310.01596).
# Citation
Please kindly cite our paper if you use our code, data, models or results:
```
@article{ku2023imagenhub,
title={ImagenHub: Standardizing the evaluation of conditional image generation models},
author={Max Ku and Tianle Li and Kai Zhang and Yujie Lu and Xingyu Fu and Wenwen Zhuang and Wenhu Chen},
journal={arXiv preprint arXiv:2310.01596},
year={2023}
}
```
|
ImagenHub/Subject_Driven_Image_Generation
|
[
"arxiv:2310.01596",
"region:us"
] |
2023-09-06T03:06:45+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "full", "path": "data/full-*"}, {"split": "eval", "path": "data/eval-*"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "uid", "dtype": "int32"}, {"name": "subject_id", "dtype": "int32"}], "splits": [{"name": "full", "num_bytes": 15084, "num_examples": 215}, {"name": "eval", "num_bytes": 9252, "num_examples": 150}], "download_size": 15136, "dataset_size": 24336}}
|
2023-11-27T09:26:37+00:00
|
[
"2310.01596"
] |
[] |
TAGS
#arxiv-2310.01596 #region-us
|
# Dataset Card
Dataset in ImagenHub.
Please kindly cite our paper if you use our code, data, models or results:
|
[
"# Dataset Card\n\n\nDataset in ImagenHub. \n\n\nPlease kindly cite our paper if you use our code, data, models or results:"
] |
[
"TAGS\n#arxiv-2310.01596 #region-us \n",
"# Dataset Card\n\n\nDataset in ImagenHub. \n\n\nPlease kindly cite our paper if you use our code, data, models or results:"
] |
[
15,
29
] |
[
"passage: TAGS\n#arxiv-2310.01596 #region-us \n# Dataset Card\n\n\nDataset in ImagenHub. \n\n\nPlease kindly cite our paper if you use our code, data, models or results:"
] |
13e594373a7399696005f0af1caff9aec8d5b14c
|
# Yuri Chat 09062023 raw
* Dataset of Yuri dialogue from DDLC (dataset of ~1300 items augmented by [MythoMax-l2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) to turn into multi-turn chat dialogue)
* Curated version planned
|
922-CA/ly2_09062023_test1_raw_YuChA_1a
|
[
"license:openrail",
"region:us"
] |
2023-09-06T03:13:53+00:00
|
{"license": "openrail"}
|
2023-09-22T07:08:43+00:00
|
[] |
[] |
TAGS
#license-openrail #region-us
|
# Yuri Chat 09062023 raw
* Dataset of Yuri dialogue from DDLC (dataset of ~1300 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)
* Curated version planned
|
[
"# Yuri Chat 09062023 raw\n* Dataset of Yuri dialogue from DDLC (dataset of ~1300 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)\n* Curated version planned"
] |
[
"TAGS\n#license-openrail #region-us \n",
"# Yuri Chat 09062023 raw\n* Dataset of Yuri dialogue from DDLC (dataset of ~1300 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)\n* Curated version planned"
] |
[
12,
52
] |
[
"passage: TAGS\n#license-openrail #region-us \n# Yuri Chat 09062023 raw\n* Dataset of Yuri dialogue from DDLC (dataset of ~1300 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)\n* Curated version planned"
] |
a92ca895052386ae21d2ee5101bf211128b70a94
|
Dolphin 🐬
https://erichartford.com/dolphin
## Dataset details
This dataset is an attempt to replicate the results of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
Our dataset consists of:
- ~1 million of FLANv2 augmented with GPT-4 completions (flan1m-alpaca-uncensored.jsonl)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (flan5m-alpaca-uncensored.jsonl)
We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.
Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.
Token distribution for GPT-3.5 completions
### Loading
```python
## load GPT-4 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan1m-alpaca-uncensored.jsonl")
## load GPT-3.5 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan5m-alpaca-uncensored.jsonl")
```
This dataset is licensed apache-2.0 for commercial or non-commercial use.
We currently plan to release Dolphin on:
- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)
The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)
I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:
- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and chirper.ai for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way.
|
polymer/dolphin-only-gpt-4
|
[
"task_categories:text-generation",
"license:apache-2.0",
"region:us"
] |
2023-09-06T04:01:33+00:00
|
{"license": "apache-2.0", "task_categories": ["text-generation"], "duplicated_from": "ehartford/dolphin"}
|
2023-09-06T04:10:58+00:00
|
[] |
[] |
TAGS
#task_categories-text-generation #license-apache-2.0 #region-us
|
Dolphin
URL
## Dataset details
This dataset is an attempt to replicate the results of Microsoft's Orca
Our dataset consists of:
- ~1 million of FLANv2 augmented with GPT-4 completions (URL)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)
We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.
Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.
Token distribution for GPT-3.5 completions
!dolphin-llama
### Loading
This dataset is licensed apache-2.0 for commercial or non-commercial use.
We currently plan to release Dolphin on:
- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)
The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)
I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:
- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and URL for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way.
|
[
"## Dataset details\n\nThis dataset is an attempt to replicate the results of Microsoft's Orca\n\nOur dataset consists of:\n\n- ~1 million of FLANv2 augmented with GPT-4 completions (URL)\n- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)\n\n\nWe followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.\n\nThen we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.\n\nToken distribution for GPT-3.5 completions\n!dolphin-llama",
"### Loading\n\n\n\nThis dataset is licensed apache-2.0 for commercial or non-commercial use.\n\nWe currently plan to release Dolphin on:\n\n- Xgen 7b 8k\n- LLaMA 13b (Non-commercial)\n- MPT 30b 8k\n- LLaMA 33b (Non-commercial)\n- Falcon 40b\n- LLaMA 65b (Non-commercial)\n\nThe Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)\n\nI would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:\n\n- Wing \"Caseus\" Lian and NanoBit of OpenAccess AI Collective\n- Rohan\n- Teknium\n- Pankaj Mathur\n- Tom \"TheBloke\" Jobbins for quantizing and amplifying\n- Special thanks to EdenCoder and URL for mentorship and financial sponsorship.\n- Special thanks to Kilkonie for his very valued mentorship.\n- All the other people in the Open Source AI community who have taught me and helped me along the way."
] |
[
"TAGS\n#task_categories-text-generation #license-apache-2.0 #region-us \n",
"## Dataset details\n\nThis dataset is an attempt to replicate the results of Microsoft's Orca\n\nOur dataset consists of:\n\n- ~1 million of FLANv2 augmented with GPT-4 completions (URL)\n- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)\n\n\nWe followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.\n\nThen we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.\n\nToken distribution for GPT-3.5 completions\n!dolphin-llama",
"### Loading\n\n\n\nThis dataset is licensed apache-2.0 for commercial or non-commercial use.\n\nWe currently plan to release Dolphin on:\n\n- Xgen 7b 8k\n- LLaMA 13b (Non-commercial)\n- MPT 30b 8k\n- LLaMA 33b (Non-commercial)\n- Falcon 40b\n- LLaMA 65b (Non-commercial)\n\nThe Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)\n\nI would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:\n\n- Wing \"Caseus\" Lian and NanoBit of OpenAccess AI Collective\n- Rohan\n- Teknium\n- Pankaj Mathur\n- Tom \"TheBloke\" Jobbins for quantizing and amplifying\n- Special thanks to EdenCoder and URL for mentorship and financial sponsorship.\n- Special thanks to Kilkonie for his very valued mentorship.\n- All the other people in the Open Source AI community who have taught me and helped me along the way."
] |
[
25,
214,
266
] |
[
"passage: TAGS\n#task_categories-text-generation #license-apache-2.0 #region-us \n## Dataset details\n\nThis dataset is an attempt to replicate the results of Microsoft's Orca\n\nOur dataset consists of:\n\n- ~1 million of FLANv2 augmented with GPT-4 completions (URL)\n- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)\n\n\nWe followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.\n\nThen we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.\n\nToken distribution for GPT-3.5 completions\n!dolphin-llama### Loading\n\n\n\nThis dataset is licensed apache-2.0 for commercial or non-commercial use.\n\nWe currently plan to release Dolphin on:\n\n- Xgen 7b 8k\n- LLaMA 13b (Non-commercial)\n- MPT 30b 8k\n- LLaMA 33b (Non-commercial)\n- Falcon 40b\n- LLaMA 65b (Non-commercial)\n\nThe Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)\n\nI would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:\n\n- Wing \"Caseus\" Lian and NanoBit of OpenAccess AI Collective\n- Rohan\n- Teknium\n- Pankaj Mathur\n- Tom \"TheBloke\" Jobbins for quantizing and amplifying\n- Special thanks to EdenCoder and URL for mentorship and financial sponsorship.\n- Special thanks to Kilkonie for his very valued mentorship.\n- All the other people in the Open Source AI community who have taught me and helped me along the way."
] |
9b70c820eac6156f10f2aeb88e02b0c8100a9aa1
|
Translated from yahma/alpaca-cleaned using NLLB-1.3B
# Dataset Card for "alpaca-cleaned-bengali"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamshnoo/alpaca-cleaned-bengali
|
[
"region:us"
] |
2023-09-06T04:09:30+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86848120, "num_examples": 51760}], "download_size": 31070768, "dataset_size": 86848120}}
|
2023-09-15T22:22:08+00:00
|
[] |
[] |
TAGS
#region-us
|
Translated from yahma/alpaca-cleaned using NLLB-1.3B
# Dataset Card for "alpaca-cleaned-bengali"
More Information needed
|
[
"# Dataset Card for \"alpaca-cleaned-bengali\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"alpaca-cleaned-bengali\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"alpaca-cleaned-bengali\"\n\nMore Information needed"
] |
5944359b1c0f4f95bb49e16cbc17e87d54c18c54
|
Translated from yahma/alpaca-cleaned using NLLB-1.3B
# Dataset Card for "alpaca-cleaned-greek"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iamshnoo/alpaca-cleaned-greek
|
[
"region:us"
] |
2023-09-06T04:14:47+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53753481, "num_examples": 51760}], "download_size": 25664903, "dataset_size": 53753481}}
|
2023-09-15T22:22:28+00:00
|
[] |
[] |
TAGS
#region-us
|
Translated from yahma/alpaca-cleaned using NLLB-1.3B
# Dataset Card for "alpaca-cleaned-greek"
More Information needed
|
[
"# Dataset Card for \"alpaca-cleaned-greek\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"alpaca-cleaned-greek\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"alpaca-cleaned-greek\"\n\nMore Information needed"
] |
d01c8882c316ce7fcee31286d7fe9ec805922351
|
# Dataset Card for "job_matcher_15k_per_example_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
serbog/job_matcher_15k_per_example_llama2
|
[
"region:us"
] |
2023-09-06T04:20:17+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "responses", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 261696280, "num_examples": 127878}], "download_size": 100659850, "dataset_size": 261696280}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-06T05:13:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "job_matcher_15k_per_example_llama2"
More Information needed
|
[
"# Dataset Card for \"job_matcher_15k_per_example_llama2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"job_matcher_15k_per_example_llama2\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"job_matcher_15k_per_example_llama2\"\n\nMore Information needed"
] |
b2465bf93c150011ccff21cf2b8c3dade21d10d8
|
# Dataset Card for "job_matcher_15k_per_example_llama2_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
serbog/job_matcher_15k_per_example_llama2_eval
|
[
"region:us"
] |
2023-09-06T04:23:07+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "responses", "dtype": "string"}], "splits": [{"name": "eval", "num_bytes": 645256836, "num_examples": 271512}], "download_size": 220425393, "dataset_size": 645256836}, "configs": [{"config_name": "default", "data_files": [{"split": "eval", "path": "data/eval-*"}]}]}
|
2023-09-06T05:13:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "job_matcher_15k_per_example_llama2_eval"
More Information needed
|
[
"# Dataset Card for \"job_matcher_15k_per_example_llama2_eval\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"job_matcher_15k_per_example_llama2_eval\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"job_matcher_15k_per_example_llama2_eval\"\n\nMore Information needed"
] |
b699651686e201172eae56828affa08ef3dc8f3e
|
# Dataset Card for "processed_dataset_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pbaoo2705/processed_dataset_v2
|
[
"region:us"
] |
2023-09-06T04:27:24+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "pubid", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "long_answer", "dtype": "string"}, {"name": "final_decision", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9013737, "num_examples": 5000}, {"name": "test", "num_bytes": 1797886, "num_examples": 1000}], "download_size": 6294228, "dataset_size": 10811623}}
|
2023-09-06T04:27:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "processed_dataset_v2"
More Information needed
|
[
"# Dataset Card for \"processed_dataset_v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_dataset_v2\"\n\nMore Information needed"
] |
[
6,
18
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_dataset_v2\"\n\nMore Information needed"
] |
828627fbf829bc92edfdfba1b12255dc98de040f
|
# Dataset Card for "data_for_synthesis_filtered_with_norm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/data_for_synthesis_filtered_with_norm
|
[
"region:us"
] |
2023-09-06T04:41:27+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "sentence_annotation", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}, {"name": "file", "dtype": "string"}, {"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "string"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "origin_transcription", "dtype": "string"}, {"name": "sentence_norm", "dtype": "string"}, {"name": "w2v2_large_transcription", "dtype": "string"}, {"name": "wer", "dtype": "int64"}, {"name": "entities_norm", "list": [{"name": "filler", "dtype": "string"}, {"name": "type", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 819923508, "num_examples": 1660}], "download_size": 192050749, "dataset_size": 819923508}}
|
2023-09-06T04:42:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data_for_synthesis_filtered_with_norm"
More Information needed
|
[
"# Dataset Card for \"data_for_synthesis_filtered_with_norm\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data_for_synthesis_filtered_with_norm\"\n\nMore Information needed"
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[
6,
24
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"data_for_synthesis_filtered_with_norm\"\n\nMore Information needed"
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655d1797b2452d81e6109b5f85e18e5d85272ad2
|
# Link to Model
[https://huggingface.co/a2ran/GPTeacher-llama2-ko-13b](https://huggingface.co/a2ran/GPTeacher-llama2-ko-13b)
|
a2ran/ex_dataset
|
[
"region:us"
] |
2023-09-06T04:56:17+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35553, "num_examples": 15}], "download_size": 30267, "dataset_size": 35553}}
|
2023-09-08T03:57:56+00:00
|
[] |
[] |
TAGS
#region-us
|
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b17802b5cff04e99c94c75c9561a285f4fc3853c
|
# 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
Sohrab Redjai Sani @srsani
|
ift/handwriting_forms
|
[
"task_categories:feature-extraction",
"size_categories:1K<n<10K",
"language:en",
"license:openrail",
"region:us"
] |
2023-09-06T05:18:43+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["1K<n<10K"], "task_categories": ["feature-extraction"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14177871.8, "num_examples": 1400}, {"name": "validation", "num_bytes": 2021857, "num_examples": 199}, {"name": "test", "num_bytes": 5084688, "num_examples": 500}], "download_size": 20674979, "dataset_size": 21284416.8}}
|
2023-09-06T13:13:04+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-feature-extraction #size_categories-1K<n<10K #language-English #license-openrail #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
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### Curation Rationale
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### Dataset Curators
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Sohrab Redjai Sani @srsani
|
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] |
ec7822fe0e16f2ea626bc922d9b8f363937ea21e
|
# Dataset Card for "v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tingchih/v1
|
[
"region:us"
] |
2023-09-06T05:24:42+00:00
|
{"dataset_info": {"features": [{"name": "Documents", "sequence": "string"}, {"name": "Entailment_Claims", "sequence": "string"}, {"name": "Neutral_Claims", "sequence": "string"}, {"name": "Contradiction_Claims", "sequence": "string"}, {"name": "Summary_GT", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 521818685, "num_examples": 35000}, {"name": "test", "num_bytes": 130314128, "num_examples": 8450}], "download_size": 381452241, "dataset_size": 652132813}}
|
2023-09-06T05:26:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "v1"
More Information needed
|
[
"# Dataset Card for \"v1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"v1\"\n\nMore Information needed"
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[
6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"v1\"\n\nMore Information needed"
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cf3cde75b60cb62ab811c9fcb112c63d4d956332
|
# 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]
|
MelonThink/MVP
|
[
"region:us"
] |
2023-09-06T05:33:29+00:00
|
{}
|
2023-09-06T05:34:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
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|
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35fa155085c8f6e28ab98238087a7ce313d2e696
|
# Dataset Card for "pubmed_nonbiomedical_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/pubmed_nonbiomedical_2
|
[
"region:us"
] |
2023-09-06T05:43:15+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 4024571.0, "num_examples": 1000}], "download_size": 2182339, "dataset_size": 4024571.0}}
|
2023-09-06T07:44:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmed_nonbiomedical_2"
More Information needed
|
[
"# Dataset Card for \"pubmed_nonbiomedical_2\"\n\nMore Information needed"
] |
[
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[
6,
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997017a0d9ab108d304f20ccffcb28a72ea95295
|
# Dataset Card for "donut-invoices"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joshuajano/donut-invoices
|
[
"region:us"
] |
2023-09-06T06:23:24+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 234024421.0, "num_examples": 425}, {"name": "test", "num_bytes": 14512665.0, "num_examples": 26}, {"name": "validation", "num_bytes": 27661738.0, "num_examples": 50}], "download_size": 197512744, "dataset_size": 276198824.0}}
|
2023-09-06T06:25:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "donut-invoices"
More Information needed
|
[
"# Dataset Card for \"donut-invoices\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
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] |
[
6,
16
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"donut-invoices\"\n\nMore Information needed"
] |
f6bd59e104010e1d45781ae7309a811399e66cb6
|
# Dataset Card for "MH_updated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Vrushali/MH_updated
|
[
"region:us"
] |
2023-09-06T06:27:33+00:00
|
{"dataset_info": {"features": [{"name": "Query", "dtype": "string"}, {"name": "Answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 135993984, "num_examples": 850738}], "download_size": 42983215, "dataset_size": 135993984}}
|
2023-09-06T06:29:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MH_updated"
More Information needed
|
[
"# Dataset Card for \"MH_updated\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MH_updated\"\n\nMore Information needed"
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[
6,
14
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"MH_updated\"\n\nMore Information needed"
] |
99217215eebbf9f9a7b02820c48b2c8a237377a7
|
# Dataset Card for "final_chat7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Vrushali/final_chat7b
|
[
"region:us"
] |
2023-09-06T06:39:52+00:00
|
{"dataset_info": {"features": [{"name": "Query", "dtype": "string"}, {"name": "Answer", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 330070, "num_examples": 1000}], "download_size": 118800, "dataset_size": 330070}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-06T06:39:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "final_chat7b"
More Information needed
|
[
"# Dataset Card for \"final_chat7b\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"final_chat7b\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"final_chat7b\"\n\nMore Information needed"
] |
74774fb1b2482c9a192a3a096a93a5ed5cb7d217
|
# Dataset Card for "Y_frequency_speed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Gummybear05/Y_frequency_speed
|
[
"region:us"
] |
2023-09-06T07:12:37+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "struct": [{"name": "array", "sequence": "float32"}, {"name": "path", "dtype": "string"}, {"name": "sample_rate", "dtype": "int64"}]}, {"name": "text", "dtype": "string"}, {"name": "scriptId", "dtype": "int64"}, {"name": "fileNm", "dtype": "string"}, {"name": "recrdTime", "dtype": "float64"}, {"name": "recrdQuality", "dtype": "int64"}, {"name": "recrdDt", "dtype": "string"}, {"name": "scriptSetNo", "dtype": "string"}, {"name": "recrdEnvrn", "dtype": "string"}, {"name": "colctUnitCode", "dtype": "string"}, {"name": "cityCode", "dtype": "string"}, {"name": "recrdUnit", "dtype": "string"}, {"name": "convrsThema", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "recorderId", "dtype": "string"}, {"name": "age", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2322247497, "num_examples": 5400}], "download_size": 2348923241, "dataset_size": 2322247497}}
|
2023-09-06T07:14:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Y_frequency_speed"
More Information needed
|
[
"# Dataset Card for \"Y_frequency_speed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Y_frequency_speed\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Y_frequency_speed\"\n\nMore Information needed"
] |
43adb352f6a61939e31632712602fa55dce723ac
|
# Dataset Card for "LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2-gpt4-falcon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sachith-surge/LaMini
|
[
"region:us"
] |
2023-09-06T07:18:58+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "llama2_status", "dtype": "string"}, {"name": "llama2_rating", "dtype": "string"}, {"name": "llama2_reason", "dtype": "string"}, {"name": "gpt4_status", "dtype": "string"}, {"name": "gpt4_rating", "dtype": "string"}, {"name": "gpt4_reason", "dtype": "string"}, {"name": "falcon_status", "dtype": "string"}, {"name": "falcon_rating", "dtype": "string"}, {"name": "falcon_reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3287768, "num_examples": 1504}], "download_size": 1603115, "dataset_size": 3287768}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-06T07:19:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2-gpt4-falcon"
More Information needed
|
[
"# Dataset Card for \"LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2-gpt4-falcon\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2-gpt4-falcon\"\n\nMore Information needed"
] |
[
6,
49
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2-gpt4-falcon\"\n\nMore Information needed"
] |
abd4b5d568d13166299f832e2922c8e3b9c6130d
|
# Dataset Card for "mid-captioning-dataset-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qkrwnstj/mid-captioning-dataset
|
[
"region:us"
] |
2023-09-06T07:20:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5361959.0, "num_examples": 20}], "download_size": 5363150, "dataset_size": 5361959.0}}
|
2023-09-06T07:25:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mid-captioning-dataset-test"
More Information needed
|
[
"# Dataset Card for \"mid-captioning-dataset-test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mid-captioning-dataset-test\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mid-captioning-dataset-test\"\n\nMore Information needed"
] |
ab66c35ebed9632dfafd4bc816365b4dd33f199f
|
# Dataset Card for "t5_predata_nocs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TenzinGayche/t5_predata_nocs
|
[
"region:us"
] |
2023-09-06T07:31:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "file_name", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "uni", "dtype": "string"}, {"name": "wylie", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "dept", "dtype": "string"}, {"name": "timestamp", "dtype": "float64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16669393849.934, "num_examples": 276501}, {"name": "test", "num_bytes": 836705600.272, "num_examples": 14564}], "download_size": 42185941593, "dataset_size": 17506099450.206}}
|
2023-09-06T07:53:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "t5_predata_nocs"
More Information needed
|
[
"# Dataset Card for \"t5_predata_nocs\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"t5_predata_nocs\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"t5_predata_nocs\"\n\nMore Information needed"
] |
ebbdecebaf2b061547999ba53fb58b8851caf8a2
|
# Dataset Card for "test_data_normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
linhqyy/test_data_normalized
|
[
"region:us"
] |
2023-09-06T07:53:57+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2100079911.832, "num_examples": 1299}], "download_size": 1881493177, "dataset_size": 2100079911.832}}
|
2023-09-06T07:55:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test_data_normalized"
More Information needed
|
[
"# Dataset Card for \"test_data_normalized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test_data_normalized\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"test_data_normalized\"\n\nMore Information needed"
] |
d9fe31e2ba2f7b644699c4ba2c07dc33985e40eb
|
# Dataset Card for "female_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/female_prompts
|
[
"region:us"
] |
2023-09-06T07:54:45+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4060495, "num_examples": 10000}], "download_size": 474495, "dataset_size": 4060495}}
|
2023-09-06T07:54:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "female_prompts"
More Information needed
|
[
"# Dataset Card for \"female_prompts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"female_prompts\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"female_prompts\"\n\nMore Information needed"
] |
89230866b668ba8ea21234e5e26d719007a8567d
|
# Dataset Card for "pubmed_nonacademic_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/pubmed_casual_2
|
[
"region:us"
] |
2023-09-06T07:58:13+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 4088611.0, "num_examples": 1000}], "download_size": 2276268, "dataset_size": 4088611.0}}
|
2023-09-06T07:58:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmed_nonacademic_2"
More Information needed
|
[
"# Dataset Card for \"pubmed_nonacademic_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pubmed_nonacademic_2\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pubmed_nonacademic_2\"\n\nMore Information needed"
] |
e5247bddf598e141df0e2e996c37293864399f45
|
# Dataset Card for "pubmed_nonbiomedicalacademic_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/pubmed_nonbiomedicalcasual_2
|
[
"region:us"
] |
2023-09-06T08:04:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 3984781.0, "num_examples": 1000}], "download_size": 2120392, "dataset_size": 3984781.0}}
|
2023-09-06T08:13:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmed_nonbiomedicalacademic_2"
More Information needed
|
[
"# Dataset Card for \"pubmed_nonbiomedicalacademic_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pubmed_nonbiomedicalacademic_2\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pubmed_nonbiomedicalacademic_2\"\n\nMore Information needed"
] |
b76c935441f4479248908ef241610129f7338904
|
# Dataset Card for "autotree_automl_100000_heloc_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_100000_heloc_sgosdt_l256_d3_sd0
|
[
"region:us"
] |
2023-09-06T08:07:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float32"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float32"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3285600000, "num_examples": 100000}, {"name": "validation", "num_bytes": 328560000, "num_examples": 10000}], "download_size": 746799349, "dataset_size": 3614160000}}
|
2023-09-06T08:07:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_100000_heloc_sgosdt_l256_d3_sd0"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_100000_heloc_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_100000_heloc_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_100000_heloc_sgosdt_l256_d3_sd0\"\n\nMore Information needed"
] |
492c23f50ca928f08a61614b1810eb0d3dc80c44
|
# Dataset Card for COCOA
[](https://github.com/shunk031/huggingface-datasets_COCOA/actions/workflows/ci.yaml)
[](https://colab.research.google.com/github/shunk031/huggingface-datasets_COCOA/blob/main/notebooks/COCOA_demo.ipynb)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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: https://github.com/Wakeupbuddy/amodalAPI
- Repository: https://github.com/shunk031/huggingface-datasets_COCOA
- Paper (preprint): https://arxiv.org/abs/1509.01329
- Paper (CVPR2017): https://openaccess.thecvf.com/content_cvpr_2017/html/Zhu_Semantic_Amodal_Segmentation_CVPR_2017_paper.html
### Dataset Summary
COCOA dataset targets amodal segmentation, which aims to recognize and segment objects beyond their visible parts. This dataset includes labels not only for the visible parts of objects, but also for their occluded parts hidden by other objects. This enables learning to understand the full shape and position of objects.
From the paper:
> We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering.
### Dataset Preprocessing
### Supported Tasks and Leaderboards
### Languages
All of annotations use English as primary language.
## Dataset Structure
### Data Instances
To use COCOA, you need to download the annotations from [the google drive](https://drive.google.com/open?id=0B8e3LNo7STslZURoTzhhMFpCelE) in the official repositories (https://github.com/Wakeupbuddy/amodalAPI#setup). Downloading of annotations currently appears to be restricted, but the author will allow us to download them if we request access privileges.
When loading a specific configuration, users has to append a version dependent suffix:
```python
import datasets as ds
dataset = ds.load_dataset(
path="shunk031/COCOA",
name="COCO",
data_dir="/path/to/cocoa_annotation.tar.gz",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)
```
#### COCO
An example of looks as follows.
```json
{
"image_id": 321,
"license_id": 1,
"file_name": "COCO_train2014_000000000321.jpg",
"height": 480,
"width": 640,
"date_captured": "2013-11-20 12: 36: 25",
"flickr_url": "http: //farm5.staticflickr.com/4096/4750559893_49fb0baf7f_z.jpg",
"image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FD21970F5E0>,
"coco_url": "http://mscoco.org/images/321",
"annotations": {
"author": ["ash2"],
"url": ["https://s3-us-west-1.amazonaws.com/coco-ann/coco-train/COCO_train2014_000000000321.jpg"],
"regions": [
{
"segmentation": [
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FBE0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970F8E0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970F400>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970F790>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FCA0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FF40>
],
"name": ["sandwich", "container", "hot dog", "hot dog", "container", "table"],
"area": [63328.0, 141246.0, 31232.0, 28735.0, 265844.0, 307200.0],
"is_stuff": [False, False, False, False, False, True],
"occlude_rate": [0.0, 0.44835251569747925, 0.0, 0.022307291626930237, 0.7122523188591003, 0.9019140601158142],
"order": [1, 2, 3, 4, 5, 6],
"visible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FD90>,
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FB50>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD21970FE80>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479460>
],
"invisible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479160>,
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD2194793A0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479490>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=640x480 at 0x7FD219479130>
]
}
],
"image_id": [321],
"depth_constraint": ["1-2,1-5,1-6,2-5,2-6,3-4,3-5,3-6,4-5,4-6,5-6"],
"size": [6]
}
}
```
#### BSDS
An example of looks as follows.
```json
{
"image_id": 100075,
"license_id": -100,
"file_name": "100075.jpg",
"height": 321,
"width": 481,
"date_captured": "?",
"flickr_url": "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg",
"image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=481x321 at 0x7FD22A328CA0>,
"bsds_url": "https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg",
"annotations": {
"author": ["acherian", "amorgan", "dromero", "jdayal", "kjyou", "ttouneh"],
"url": [
"https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg",
"https://s3-us-west-1.amazonaws.com/coco-ann/BSDS/BSDS_train_100075.jpg"
],
"regions": [
{
"segmentation": [
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3288E0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328430>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328070>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328610>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3280D0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328BE0>
],
"name": ["rocks", "bear", "bear", "bear", "sand", "water"],
"area": [31872.0, 5603.0, 38819.0, 12869.0, 27883.0, 124695.0],
"is_stuff": [False, False, False, False, False, False],
"occlude_rate": [0.0, 0.0, 0.0, 0.3645193874835968, 0.13043789565563202, 0.6487349271774292],
"order": [1, 2, 3, 4, 5, 6],
"visible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328AF0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328A30>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328220>
],
"invisible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3282E0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328400>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328310>
]
},
{
"segmentation": [
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328340>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328B80>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328670>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328520>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328460>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328D00>
],
"name": ["bear", "bear", "bear", "shore line", "water", "shore line"],
"area": [38772.0, 5178.0, 13575.0, 31977.0, 84224.0, 37418.0],
"is_stuff": [False, False, False, False, False, False],
"occlude_rate": [0.0, 0.0, 0.35889503359794617, 0.1458861082792282, 0.5715591907501221, 0.0],
"order": [1, 2, 3, 4, 5, 6],
"visible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328A00>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328D60>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3285E0>,
None
],
"invisible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3286A0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328490>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328100>,
None
]
},
{
"segmentation": [
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3282B0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328EE0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3284C0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A3285B0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328C40>
],
"name": ["bear", "bear", "bear", "beach", "ocean"],
"area": [38522.0, 5496.0, 12581.0, 27216.0, 126090.0],
"is_stuff": [False, False, False, False, False],
"occlude_rate": [0.0, 0.0, 0.3449646234512329, 0.11258083581924438, 0.39141881465911865],
"order": [1, 2, 3, 4, 5],
"visible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328940>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD22A328880>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830A00>
],
"invisible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830CD0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830BB0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830940>
]
},
{
"segmentation": [
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830910>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2198308E0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830C70>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830970>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830CA0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2198309A0>
],
"name": ["Bear", "Bear", "Bear", "Water", "ground", "Ground"],
"area": [39133.0, 7120.0, 13053.0, 97052.0, 33441.0, 26313.0],
"is_stuff": [False, False, False, False, False, False],
"occlude_rate": [0.0, 0.0, 0.4422737956047058, 0.5332708358764648, 0.007117012050002813, 0.1584388017654419],
"order": [1, 2, 3, 4, 5, 6],
"visible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830A30>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830C40>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219830B80>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6820>
],
"invisible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A68B0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6610>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A69D0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6730>
]
},
{
"segmentation": [
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6790>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6550>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6850>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6940>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A66D0>
],
"name": ["bear", "bear", "bear", "water", "rock beach"],
"area": [38378.0, 6130.0, 12649.0, 98377.0, 153118.0],
"is_stuff": [False, False, False, False, False],
"occlude_rate": [0.0, 0.0, 0.41094157099723816, 0.5013265013694763, 0.65973299741745],
"order": [1, 2, 3, 4, 5],
"visible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD268700F10>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2687004F0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2687002B0>
],
"invisible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A64C0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD28805FB50>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD28805F580>
]
},
{
"segmentation": [
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2191A6880>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB190>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB8E0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB070>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2480FB610>
],
"name": ["bear", "bear", "bear", "sand", "water"],
"area": [38802.0, 5926.0, 12248.0, 27857.0, 126748.0],
"is_stuff": [False, False, False, False, False],
"occlude_rate": [0.0, 0.0, 0.37026453018188477, 0.13170836865901947, 0.3872092664241791],
"order": [1, 2, 3, 4, 5],
"visible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479DC0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479C70>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479A90>
],
"invisible_mask": [
None,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479AF0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD2194795B0>,
<PIL.PngImagePlugin.PngImageFile image mode=L size=481x321 at 0x7FD219479670>
]
}
],
"image_id": [100075, 100075, 100075, 100075, 100075, 100075],
"depth_constraint": [
"1-6,2-4,2-5,2-6,3-4,3-5,3-6,4-5,4-6,5-6",
"1-3,1-4,1-5,2-3,2-4,2-5,3-4,3-5,4-5",
"1-3,1-4,1-6,2-3,2-4,2-6,3-4,3-6,4-5,4-6",
"1-4,1-5,2-3,2-4,2-5,3-4,3-5,4-5",
"1-3,1-4,1-5,2-3,2-4,2-5,3-4,3-5,4-5"
],
"size": [6, 6, 5, 6, 5, 5]
}
}
```
### Data Fields
#### COCO
- `image_id`: Unique numeric ID of the image.
- `license_id`: Unique numeric ID of the image license.
- `file_name`: File name of the image.
- `width`: Image width.
- `height`: Image height.
- `date_captured`: Date of capturing data
- `flickr_url`: Original flickr url of the image.
- `image`: A `PIL.Image.Image` object containing the image.
- `coco_url`: COCO url of the image.
- `annotations`: Holds a list of `Annotation` data classes:
- `author`: TBD
- `url`: TBD
- `image_id`: TBD
- `depth_constraint`: TBD
- `size`: TBD
- `regions`: TBD
- `segmentation`: TBD
- `name`: TBD
- `area`: TBD
- `is_stuff`: TBD
- `occlude_rate`: TBD
- `order`: TBD
- `visible_mask`: TBD
- `invisible_mask`: TBD
#### BSDS
- `image_id`: Unique numeric ID of the image.
- `license_id`: Unique numeric ID of the image license.
- `file_name`: File name of the image.
- `width`: Image width.
- `height`: Image height.
- `date_captured`: Date of capturing data
- `flickr_url`: Original flickr url of the image.
- `image`: A `PIL.Image.Image` object containing the image.
- `bsds_url`: BSDS url of the image.
- `annotations`: Holds a list of `Annotation` data classes:
- `author`: TBD
- `url`: TBD
- `image_id`: TBD
- `depth_constraint`: TBD
- `size`: TBD
- `regions`: TBD
- `segmentation`: TBD
- `name`: TBD
- `area`: TBD
- `is_stuff`: TBD
- `occlude_rate`: TBD
- `order`: TBD
- `visible_mask`: TBD
- `invisible_mask`: TBD
### Data Splits
| name | train | validation | test |
|------|------:|-----------:|------:|
| COCO | 2,500 | 1,323 | 1,250 |
| BSDS | 200 | 100 | 200 |
## 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
COCOA is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply:
- COCO images: [Flickr Terms of use](http://cocodataset.org/#termsofuse)
- COCO annotations: [Creative Commons Attribution 4.0 License](http://cocodataset.org/#termsofuse)
### Citation Information
```bibtex
@inproceedings{zhu2017semantic,
title={Semantic amodal segmentation},
author={Zhu, Yan and Tian, Yuandong and Metaxas, Dimitris and Doll{\'a}r, Piotr},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={1464--1472},
year={2017}
}
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13},
pages={740--755},
year={2014},
organization={Springer}
}
@article{arbelaez2010contour,
title={Contour detection and hierarchical image segmentation},
author={Arbelaez, Pablo and Maire, Michael and Fowlkes, Charless and Malik, Jitendra},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={33},
number={5},
pages={898--916},
year={2010},
publisher={IEEE}
}
```
### Contributions
Thanks to [@Wakeupbuddy](https://github.com/Wakeupbuddy) for publishing the COCOA dataset.
|
shunk031/COCOA
|
[
"language:en",
"license:cc-by-4.0",
"computer-vision",
"instance-segmentation",
"ms-coco",
"bsds",
"arxiv:1509.01329",
"region:us"
] |
2023-09-06T08:07:59+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "tags": ["computer-vision", "instance-segmentation", "ms-coco", "bsds"], "datasets": ["COCO", "BSDS"], "metrics": ["iou"]}
|
2023-09-16T10:59:03+00:00
|
[
"1509.01329"
] |
[
"en"
] |
TAGS
#language-English #license-cc-by-4.0 #computer-vision #instance-segmentation #ms-coco #bsds #arxiv-1509.01329 #region-us
|
Dataset Card for COCOA
======================
: URL
* Paper (CVPR2017): URL
### Dataset Summary
COCOA dataset targets amodal segmentation, which aims to recognize and segment objects beyond their visible parts. This dataset includes labels not only for the visible parts of objects, but also for their occluded parts hidden by other objects. This enables learning to understand the full shape and position of objects.
From the paper:
>
> We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering.
>
>
>
### Dataset Preprocessing
### Supported Tasks and Leaderboards
### Languages
All of annotations use English as primary language.
Dataset Structure
-----------------
### Data Instances
To use COCOA, you need to download the annotations from the google drive in the official repositories (URL Downloading of annotations currently appears to be restricted, but the author will allow us to download them if we request access privileges.
When loading a specific configuration, users has to append a version dependent suffix:
#### COCO
An example of looks as follows.
#### BSDS
An example of looks as follows.
### Data Fields
#### COCO
* 'image\_id': Unique numeric ID of the image.
* 'license\_id': Unique numeric ID of the image license.
* 'file\_name': File name of the image.
* 'width': Image width.
* 'height': Image height.
* 'date\_captured': Date of capturing data
* 'flickr\_url': Original flickr url of the image.
* 'image': A 'PIL.Image.Image' object containing the image.
* 'coco\_url': COCO url of the image.
* 'annotations': Holds a list of 'Annotation' data classes:
+ 'author': TBD
+ 'url': TBD
+ 'image\_id': TBD
+ 'depth\_constraint': TBD
+ 'size': TBD
+ 'regions': TBD
- 'segmentation': TBD
* 'name': TBD
* 'area': TBD
* 'is\_stuff': TBD
* 'occlude\_rate': TBD
* 'order': TBD
* 'visible\_mask': TBD
* 'invisible\_mask': TBD
#### BSDS
* 'image\_id': Unique numeric ID of the image.
* 'license\_id': Unique numeric ID of the image license.
* 'file\_name': File name of the image.
* 'width': Image width.
* 'height': Image height.
* 'date\_captured': Date of capturing data
* 'flickr\_url': Original flickr url of the image.
* 'image': A 'PIL.Image.Image' object containing the image.
* 'bsds\_url': BSDS url of the image.
* 'annotations': Holds a list of 'Annotation' data classes:
+ 'author': TBD
+ 'url': TBD
+ 'image\_id': TBD
+ 'depth\_constraint': TBD
+ 'size': TBD
+ 'regions': TBD
- 'segmentation': TBD
* 'name': TBD
* 'area': TBD
* 'is\_stuff': TBD
* 'occlude\_rate': TBD
* 'order': TBD
* 'visible\_mask': TBD
* 'invisible\_mask': TBD
### 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
COCOA is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply:
* COCO images: Flickr Terms of use
* COCO annotations: Creative Commons Attribution 4.0 License
### Contributions
Thanks to @Wakeupbuddy for publishing the COCOA dataset.
|
[
"### Dataset Summary\n\n\nCOCOA dataset targets amodal segmentation, which aims to recognize and segment objects beyond their visible parts. This dataset includes labels not only for the visible parts of objects, but also for their occluded parts hidden by other objects. This enables learning to understand the full shape and position of objects.\n\n\nFrom the paper:\n\n\n\n> \n> We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering.\n> \n> \n>",
"### Dataset Preprocessing",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nAll of annotations use English as primary language.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nTo use COCOA, you need to download the annotations from the google drive in the official repositories (URL Downloading of annotations currently appears to be restricted, but the author will allow us to download them if we request access privileges.\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:",
"#### COCO\n\n\nAn example of looks as follows.",
"#### BSDS\n\n\nAn example of looks as follows.",
"### Data Fields",
"#### COCO\n\n\n* 'image\\_id': Unique numeric ID of the image.\n* 'license\\_id': Unique numeric ID of the image license.\n* 'file\\_name': File name of the image.\n* 'width': Image width.\n* 'height': Image height.\n* 'date\\_captured': Date of capturing data\n* 'flickr\\_url': Original flickr url of the image.\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'coco\\_url': COCO url of the image.\n* 'annotations': Holds a list of 'Annotation' data classes:\n\t+ 'author': TBD\n\t+ 'url': TBD\n\t+ 'image\\_id': TBD\n\t+ 'depth\\_constraint': TBD\n\t+ 'size': TBD\n\t+ 'regions': TBD\n\t\t- 'segmentation': TBD\n\t\t\t* 'name': TBD\n\t\t\t* 'area': TBD\n\t\t\t* 'is\\_stuff': TBD\n\t\t\t* 'occlude\\_rate': TBD\n\t\t\t* 'order': TBD\n\t\t\t* 'visible\\_mask': TBD\n\t\t\t* 'invisible\\_mask': TBD",
"#### BSDS\n\n\n* 'image\\_id': Unique numeric ID of the image.\n* 'license\\_id': Unique numeric ID of the image license.\n* 'file\\_name': File name of the image.\n* 'width': Image width.\n* 'height': Image height.\n* 'date\\_captured': Date of capturing data\n* 'flickr\\_url': Original flickr url of the image.\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'bsds\\_url': BSDS url of the image.\n* 'annotations': Holds a list of 'Annotation' data classes:\n\t+ 'author': TBD\n\t+ 'url': TBD\n\t+ 'image\\_id': TBD\n\t+ 'depth\\_constraint': TBD\n\t+ 'size': TBD\n\t+ 'regions': TBD\n\t\t- 'segmentation': TBD\n\t\t\t* 'name': TBD\n\t\t\t* 'area': TBD\n\t\t\t* 'is\\_stuff': TBD\n\t\t\t* 'occlude\\_rate': TBD\n\t\t\t* 'order': TBD\n\t\t\t* 'visible\\_mask': TBD\n\t\t\t* 'invisible\\_mask': TBD",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\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\n\n\nCOCOA is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply:\n\n\n* COCO images: Flickr Terms of use\n* COCO annotations: Creative Commons Attribution 4.0 License",
"### Contributions\n\n\nThanks to @Wakeupbuddy for publishing the COCOA dataset."
] |
[
"TAGS\n#language-English #license-cc-by-4.0 #computer-vision #instance-segmentation #ms-coco #bsds #arxiv-1509.01329 #region-us \n",
"### Dataset Summary\n\n\nCOCOA dataset targets amodal segmentation, which aims to recognize and segment objects beyond their visible parts. This dataset includes labels not only for the visible parts of objects, but also for their occluded parts hidden by other objects. This enables learning to understand the full shape and position of objects.\n\n\nFrom the paper:\n\n\n\n> \n> We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering.\n> \n> \n>",
"### Dataset Preprocessing",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nAll of annotations use English as primary language.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nTo use COCOA, you need to download the annotations from the google drive in the official repositories (URL Downloading of annotations currently appears to be restricted, but the author will allow us to download them if we request access privileges.\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:",
"#### COCO\n\n\nAn example of looks as follows.",
"#### BSDS\n\n\nAn example of looks as follows.",
"### Data Fields",
"#### COCO\n\n\n* 'image\\_id': Unique numeric ID of the image.\n* 'license\\_id': Unique numeric ID of the image license.\n* 'file\\_name': File name of the image.\n* 'width': Image width.\n* 'height': Image height.\n* 'date\\_captured': Date of capturing data\n* 'flickr\\_url': Original flickr url of the image.\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'coco\\_url': COCO url of the image.\n* 'annotations': Holds a list of 'Annotation' data classes:\n\t+ 'author': TBD\n\t+ 'url': TBD\n\t+ 'image\\_id': TBD\n\t+ 'depth\\_constraint': TBD\n\t+ 'size': TBD\n\t+ 'regions': TBD\n\t\t- 'segmentation': TBD\n\t\t\t* 'name': TBD\n\t\t\t* 'area': TBD\n\t\t\t* 'is\\_stuff': TBD\n\t\t\t* 'occlude\\_rate': TBD\n\t\t\t* 'order': TBD\n\t\t\t* 'visible\\_mask': TBD\n\t\t\t* 'invisible\\_mask': TBD",
"#### BSDS\n\n\n* 'image\\_id': Unique numeric ID of the image.\n* 'license\\_id': Unique numeric ID of the image license.\n* 'file\\_name': File name of the image.\n* 'width': Image width.\n* 'height': Image height.\n* 'date\\_captured': Date of capturing data\n* 'flickr\\_url': Original flickr url of the image.\n* 'image': A 'PIL.Image.Image' object containing the image.\n* 'bsds\\_url': BSDS url of the image.\n* 'annotations': Holds a list of 'Annotation' data classes:\n\t+ 'author': TBD\n\t+ 'url': TBD\n\t+ 'image\\_id': TBD\n\t+ 'depth\\_constraint': TBD\n\t+ 'size': TBD\n\t+ 'regions': TBD\n\t\t- 'segmentation': TBD\n\t\t\t* 'name': TBD\n\t\t\t* 'area': TBD\n\t\t\t* 'is\\_stuff': TBD\n\t\t\t* 'occlude\\_rate': TBD\n\t\t\t* 'order': TBD\n\t\t\t* 'visible\\_mask': TBD\n\t\t\t* 'invisible\\_mask': TBD",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\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\n\n\nCOCOA is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply:\n\n\n* COCO images: Flickr Terms of use\n* COCO annotations: Creative Commons Attribution 4.0 License",
"### Contributions\n\n\nThanks to @Wakeupbuddy for publishing the COCOA dataset."
] |
[
47,
313,
7,
10,
22,
79,
12,
12,
5,
292,
292,
11,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
64,
23
] |
[
"passage: TAGS\n#language-English #license-cc-by-4.0 #computer-vision #instance-segmentation #ms-coco #bsds #arxiv-1509.01329 #region-us \n### Dataset Summary\n\n\nCOCOA dataset targets amodal segmentation, which aims to recognize and segment objects beyond their visible parts. This dataset includes labels not only for the visible parts of objects, but also for their occluded parts hidden by other objects. This enables learning to understand the full shape and position of objects.\n\n\nFrom the paper:\n\n\n\n> \n> We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering.\n> \n> \n>### Dataset Preprocessing### Supported Tasks and Leaderboards### Languages\n\n\nAll of annotations use English as primary language.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nTo use COCOA, you need to download the annotations from the google drive in the official repositories (URL Downloading of annotations currently appears to be restricted, but the author will allow us to download them if we request access privileges.\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:#### COCO\n\n\nAn example of looks as follows.#### BSDS\n\n\nAn example of looks as follows.### Data Fields"
] |
417db77766c87b63cec1e53932deb4cf49ba6aeb
|
# Dataset Card for "pfa-sustain-quantized-7-7-7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roszcz/pfa-sustain-quantized-7-7-7
|
[
"region:us"
] |
2023-09-06T08:11:31+00:00
|
{"dataset_info": {"features": [{"name": "midi_filename", "dtype": "string"}, {"name": "pitch", "sequence": "int16", "length": 128}, {"name": "dstart", "sequence": "float32", "length": 128}, {"name": "duration", "sequence": "float32", "length": 128}, {"name": "velocity", "sequence": "int16", "length": 128}, {"name": "dstart_bin", "sequence": "int8", "length": 128}, {"name": "duration_bin", "sequence": "int8", "length": 128}, {"name": "velocity_bin", "sequence": "int8", "length": 128}], "splits": [{"name": "train", "num_bytes": 430530730, "num_examples": 217628}, {"name": "validation", "num_bytes": 10502399, "num_examples": 5312}, {"name": "test", "num_bytes": 11577313, "num_examples": 5855}], "download_size": 0, "dataset_size": 452610442}}
|
2023-09-15T09:37:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pfa-sustain-quantized-7-7-7"
More Information needed
|
[
"# Dataset Card for \"pfa-sustain-quantized-7-7-7\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pfa-sustain-quantized-7-7-7\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pfa-sustain-quantized-7-7-7\"\n\nMore Information needed"
] |
14947ab91a5ad2a6b684a2b3b94a89a4aee70dcc
|
# Bangumi Image Base of Senki Zesshou Symphogear
This is the image base of bangumi Senki Zesshou Symphogear, we detected 10 characters, 1545 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 | 852 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 9 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 9 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 9 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 18 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 8 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 6 | [Download](6/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 7 | 5 | [Download](7/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 8 | 6 | [Download](8/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 623 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/senkizesshousymphogearnoise
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-09-06T08:14:00+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-29T05:03:50+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Senki Zesshou Symphogear
==============================================
This is the image base of bangumi Senki Zesshou Symphogear, we detected 10 characters, 1545 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-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
c4bd69acf5dc2d1fb4680c435432fddc22dff811
|
# Dataset Card for "tlc_interduplication"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
antolin/tlc_interduplication
|
[
"region:us"
] |
2023-09-06T08:27:24+00:00
|
{"dataset_info": {"features": [{"name": "id_within_dataset", "dtype": "int64"}, {"name": "snippet", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "nl", "dtype": "string"}, {"name": "split_within_dataset", "dtype": "string"}, {"name": "is_duplicated", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 70652063.18677872, "num_examples": 53327}, {"name": "test", "num_bytes": 8799876.304434607, "num_examples": 6642}, {"name": "valid", "num_bytes": 8831673.508786675, "num_examples": 6666}], "download_size": 33772946, "dataset_size": 88283613.00000001}}
|
2023-11-10T11:59:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tlc_interduplication"
More Information needed
|
[
"# Dataset Card for \"tlc_interduplication\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tlc_interduplication\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tlc_interduplication\"\n\nMore Information needed"
] |
9ccaf5f9086a2a1db1d43ae0d108b1501c380f1a
|
# Dataset Card for Evaluation run of TaylorAI/Flash-Llama-30M-20001
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TaylorAI/Flash-Llama-30M-20001
- **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 [TaylorAI/Flash-Llama-30M-20001](https://huggingface.co/TaylorAI/Flash-Llama-30M-20001) 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_TaylorAI__Flash-Llama-30M-20001",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T02:44:26.412393](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__Flash-Llama-30M-20001/blob/main/results_2023-09-17T02-44-26.412393.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": 0.002307046979865772,
"em_stderr": 0.0004913221265094458,
"f1": 0.006848783557046977,
"f1_stderr": 0.0006387737069456149,
"acc": 0.2541436464088398,
"acc_stderr": 0.007025277661412097
},
"harness|drop|3": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094458,
"f1": 0.006848783557046977,
"f1_stderr": 0.0006387737069456149
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5082872928176796,
"acc_stderr": 0.014050555322824194
}
}
```
### 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_TaylorAI__Flash-Llama-30M-20001
|
[
"region:us"
] |
2023-09-06T08:54:11+00:00
|
{"pretty_name": "Evaluation run of TaylorAI/Flash-Llama-30M-20001", "dataset_summary": "Dataset automatically created during the evaluation run of model [TaylorAI/Flash-Llama-30M-20001](https://huggingface.co/TaylorAI/Flash-Llama-30M-20001) 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_TaylorAI__Flash-Llama-30M-20001\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T02:44:26.412393](https://huggingface.co/datasets/open-llm-leaderboard/details_TaylorAI__Flash-Llama-30M-20001/blob/main/results_2023-09-17T02-44-26.412393.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\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094458,\n \"f1\": 0.006848783557046977,\n \"f1_stderr\": 0.0006387737069456149,\n \"acc\": 0.2541436464088398,\n \"acc_stderr\": 0.007025277661412097\n },\n \"harness|drop|3\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094458,\n \"f1\": 0.006848783557046977,\n \"f1_stderr\": 0.0006387737069456149\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5082872928176796,\n \"acc_stderr\": 0.014050555322824194\n }\n}\n```", "repo_url": "https://huggingface.co/TaylorAI/Flash-Llama-30M-20001", "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_06T09_53_56.209295", "path": ["**/details_harness|arc:challenge|25_2023-09-06T09-53-56.209295.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-06T09-53-56.209295.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_17T02_44_26.412393", "path": ["**/details_harness|drop|3_2023-09-17T02-44-26.412393.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T02-44-26.412393.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T02_44_26.412393", "path": ["**/details_harness|gsm8k|5_2023-09-17T02-44-26.412393.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T02-44-26.412393.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_06T09_53_56.209295", "path": ["**/details_harness|hellaswag|10_2023-09-06T09-53-56.209295.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-06T09-53-56.209295.parquet"]}]}, 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2023-09-17T01:44:38+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of TaylorAI/Flash-Llama-30M-20001
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TaylorAI/Flash-Llama-30M-20001 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 2023-09-17T02:44:26.412393(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 TaylorAI/Flash-Llama-30M-20001",
"## 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 TaylorAI/Flash-Llama-30M-20001 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 2023-09-17T02:44:26.412393(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 TaylorAI/Flash-Llama-30M-20001",
"## 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 TaylorAI/Flash-Llama-30M-20001 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 2023-09-17T02:44:26.412393(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,
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169,
67,
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4,
6,
6,
5,
5,
5,
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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 TaylorAI/Flash-Llama-30M-20001## 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 TaylorAI/Flash-Llama-30M-20001 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 2023-09-17T02:44:26.412393(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"
] |
3114c6cfe42ee0434225ff4b8d41e7848c5dc447
|
# Dataset Card for "common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mohammadh128/common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0
|
[
"region:us"
] |
2023-09-06T08:58:52+00:00
|
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 51773704672.0, "num_examples": 53902}, {"name": "validation", "num_bytes": 9881781552.0, "num_examples": 10288}], "download_size": 8718461806, "dataset_size": 61655486224.0}}
|
2023-09-06T13:41:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0"
More Information needed
|
[
"# Dataset Card for \"common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0\"\n\nMore Information needed"
] |
[
6,
38
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"common_voice_fa_preprocessed_and_augmented_training_and_evaluation_11_0\"\n\nMore Information needed"
] |
73ffa1ecb304f6752768722a7baf8f3467b59aff
|
# Dataset Card for "aug_on_fly_60ep_spkn_50ep_data_normed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
linhqyy/aug_on_fly_60ep_spkn_50ep_data_normed
|
[
"region:us"
] |
2023-09-06T09:01:11+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "id", "dtype": "string"}, {"name": "w2v2_baseline_transcription", "dtype": "string"}, {"name": "w2v2_baseline_norm", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 174371769.027, "num_examples": 1299}], "download_size": 167963769, "dataset_size": 174371769.027}}
|
2023-09-06T09:01:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "aug_on_fly_60ep_spkn_50ep_data_normed"
More Information needed
|
[
"# Dataset Card for \"aug_on_fly_60ep_spkn_50ep_data_normed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"aug_on_fly_60ep_spkn_50ep_data_normed\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"aug_on_fly_60ep_spkn_50ep_data_normed\"\n\nMore Information needed"
] |
f9ea3c121a7aac7e57ed019272d2bf7f7b82a5d3
|
# Dataset Card for "LinMinMei"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hahaday2022/LinMinMei
|
[
"region:us"
] |
2023-09-06T09:11:50+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 15940192.0, "num_examples": 48}], "download_size": 15604646, "dataset_size": 15940192.0}}
|
2023-09-06T09:12:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "LinMinMei"
More Information needed
|
[
"# Dataset Card for \"LinMinMei\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"LinMinMei\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"LinMinMei\"\n\nMore Information needed"
] |
6d349851cc296d6e8e320599e52339f16f27a377
|
# Dataset Card for WikiMatrix English-Vietnamese Parallel Sentences
### Dataset Summary
The WikiMatrix English-Vietnamese Parallel Sentences dataset contains parallel sentences in English and Vietnamese extracted from the WikiMatrix project. This dataset is a valuable resource for tasks such as machine translation and cross-lingual understanding.
### Supported Tasks and Leaderboards
- Machine Translation
- Cross-lingual Understanding
### Languages
- English
- Vietnamese
## Additional Information
### Licensing Information
The dataset is distributed under the Creative Commons Attribution-ShareAlike License.
### Citation Information
[1] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,
[*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*](https://arxiv.org/abs/1907.05791)
arXiv, July 11 2019.
[2] Mikel Artetxe and Holger Schwenk,
[*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*](https://arxiv.org/abs/1811.01136)
arXiv, Nov 3 2018.
[3] Mikel Artetxe and Holger Schwenk,
[*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464)
arXiv, Dec 26 2018.
[4] Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan and Graham Neubig,
[*When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?*](https://www.aclweb.org/anthology/papers/N/N18/N18-2084/)
NAACL, pages 529-535, 2018.
|
ngoan/WikiMatrix.en-vi
|
[
"arxiv:1907.05791",
"arxiv:1811.01136",
"arxiv:1812.10464",
"region:us"
] |
2023-09-06T09:17:00+00:00
|
{}
|
2023-09-06T09:31:37+00:00
|
[
"1907.05791",
"1811.01136",
"1812.10464"
] |
[] |
TAGS
#arxiv-1907.05791 #arxiv-1811.01136 #arxiv-1812.10464 #region-us
|
# Dataset Card for WikiMatrix English-Vietnamese Parallel Sentences
### Dataset Summary
The WikiMatrix English-Vietnamese Parallel Sentences dataset contains parallel sentences in English and Vietnamese extracted from the WikiMatrix project. This dataset is a valuable resource for tasks such as machine translation and cross-lingual understanding.
### Supported Tasks and Leaderboards
- Machine Translation
- Cross-lingual Understanding
### Languages
- English
- Vietnamese
## Additional Information
### Licensing Information
The dataset is distributed under the Creative Commons Attribution-ShareAlike License.
[1] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,
*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*
arXiv, July 11 2019.
[2] Mikel Artetxe and Holger Schwenk,
*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*
arXiv, Nov 3 2018.
[3] Mikel Artetxe and Holger Schwenk,
*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*
arXiv, Dec 26 2018.
[4] Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan and Graham Neubig,
*When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?*
NAACL, pages 529-535, 2018.
|
[
"# Dataset Card for WikiMatrix English-Vietnamese Parallel Sentences",
"### Dataset Summary\n\nThe WikiMatrix English-Vietnamese Parallel Sentences dataset contains parallel sentences in English and Vietnamese extracted from the WikiMatrix project. This dataset is a valuable resource for tasks such as machine translation and cross-lingual understanding.",
"### Supported Tasks and Leaderboards\n\n- Machine Translation\n- Cross-lingual Understanding",
"### Languages\n\n- English\n- Vietnamese",
"## Additional Information",
"### Licensing Information\n\nThe dataset is distributed under the Creative Commons Attribution-ShareAlike License.\n\n\n[1] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,\n *WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*\n arXiv, July 11 2019.\n\n[2] Mikel Artetxe and Holger Schwenk,\n *Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*\n arXiv, Nov 3 2018.\n\n[3] Mikel Artetxe and Holger Schwenk,\n *Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*\n arXiv, Dec 26 2018.\n\n[4] Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan and Graham Neubig,\n *When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?*\n NAACL, pages 529-535, 2018."
] |
[
"TAGS\n#arxiv-1907.05791 #arxiv-1811.01136 #arxiv-1812.10464 #region-us \n",
"# Dataset Card for WikiMatrix English-Vietnamese Parallel Sentences",
"### Dataset Summary\n\nThe WikiMatrix English-Vietnamese Parallel Sentences dataset contains parallel sentences in English and Vietnamese extracted from the WikiMatrix project. This dataset is a valuable resource for tasks such as machine translation and cross-lingual understanding.",
"### Supported Tasks and Leaderboards\n\n- Machine Translation\n- Cross-lingual Understanding",
"### Languages\n\n- English\n- Vietnamese",
"## Additional Information",
"### Licensing Information\n\nThe dataset is distributed under the Creative Commons Attribution-ShareAlike License.\n\n\n[1] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,\n *WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*\n arXiv, July 11 2019.\n\n[2] Mikel Artetxe and Holger Schwenk,\n *Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*\n arXiv, Nov 3 2018.\n\n[3] Mikel Artetxe and Holger Schwenk,\n *Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*\n arXiv, Dec 26 2018.\n\n[4] Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan and Graham Neubig,\n *When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?*\n NAACL, pages 529-535, 2018."
] |
[
33,
17,
61,
20,
9,
5,
217
] |
[
"passage: TAGS\n#arxiv-1907.05791 #arxiv-1811.01136 #arxiv-1812.10464 #region-us \n# Dataset Card for WikiMatrix English-Vietnamese Parallel Sentences### Dataset Summary\n\nThe WikiMatrix English-Vietnamese Parallel Sentences dataset contains parallel sentences in English and Vietnamese extracted from the WikiMatrix project. This dataset is a valuable resource for tasks such as machine translation and cross-lingual understanding.### Supported Tasks and Leaderboards\n\n- Machine Translation\n- Cross-lingual Understanding### Languages\n\n- English\n- Vietnamese## Additional Information### Licensing Information\n\nThe dataset is distributed under the Creative Commons Attribution-ShareAlike License.\n\n\n[1] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,\n *WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*\n arXiv, July 11 2019.\n\n[2] Mikel Artetxe and Holger Schwenk,\n *Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*\n arXiv, Nov 3 2018.\n\n[3] Mikel Artetxe and Holger Schwenk,\n *Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*\n arXiv, Dec 26 2018.\n\n[4] Ye Qi, Devendra Sachan, Matthieu Felix, Sarguna Padmanabhan and Graham Neubig,\n *When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?*\n NAACL, pages 529-535, 2018."
] |
f643f2afd575f7e934884cabad55b6e81cdda386
|
# Dataset Card for "GPT4-8K"
Sure! Here's a README.md file for your dataset:
# Dataset Description
This dataset was generated using GPT-4, a powerful language model developed by OpenAI. It contains a collection of dialogs between a user and an assistant, along with additional information.
from OpenChat
## Dataset Configurations
The dataset includes the following configurations:
- **Config Name:** default
- **Data Files:**
- **Split:** train
- **Path:** data/train-*
## Dataset Information
The dataset consists of the following features:
- **Dialogs:** A sequence of strings representing the dialog between the user and the assistant.
- **User:** A sequence of strings representing the user's input during the dialog.
- **Assistant:** A sequence of strings representing the assistant's responses during the dialog.
- **Llama2 Prompt:** A string representing additional prompt information related to the Llama2 model.
The dataset is divided into the following splits:
- **Train:**
- **Number of Bytes:** 193,605,433
- **Number of Examples:** 6,144
## Dataset Size and Download
- **Download Size:** 90,877,640 bytes
- **Dataset Size:** 193,605,433 bytes
Please note that this dataset was generated by GPT-4 and may contain synthetic or simulated data. It is intended for research and experimentation purposes.
For more information or inquiries, please contact the dataset owner.
Thank you for using this dataset!
|
erfanzar/GPT4-8K
|
[
"task_categories:text-classification",
"task_categories:translation",
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
2023-09-06T09:17:32+00:00
|
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "translation", "conversational", "text-generation", "summarization"], "pretty_name": "GPT4", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "dialogs", "sequence": "string"}, {"name": "user", "sequence": "string"}, {"name": "assistant", "sequence": "string"}, {"name": "llama2_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 193605433, "num_examples": 6144}], "download_size": 90877640, "dataset_size": 193605433}}
|
2023-09-07T10:04:23+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #task_categories-translation #task_categories-conversational #task_categories-text-generation #task_categories-summarization #size_categories-1K<n<10K #language-English #region-us
|
# Dataset Card for "GPT4-8K"
Sure! Here's a URL file for your dataset:
# Dataset Description
This dataset was generated using GPT-4, a powerful language model developed by OpenAI. It contains a collection of dialogs between a user and an assistant, along with additional information.
from OpenChat
## Dataset Configurations
The dataset includes the following configurations:
- Config Name: default
- Data Files:
- Split: train
- Path: data/train-*
## Dataset Information
The dataset consists of the following features:
- Dialogs: A sequence of strings representing the dialog between the user and the assistant.
- User: A sequence of strings representing the user's input during the dialog.
- Assistant: A sequence of strings representing the assistant's responses during the dialog.
- Llama2 Prompt: A string representing additional prompt information related to the Llama2 model.
The dataset is divided into the following splits:
- Train:
- Number of Bytes: 193,605,433
- Number of Examples: 6,144
## Dataset Size and Download
- Download Size: 90,877,640 bytes
- Dataset Size: 193,605,433 bytes
Please note that this dataset was generated by GPT-4 and may contain synthetic or simulated data. It is intended for research and experimentation purposes.
For more information or inquiries, please contact the dataset owner.
Thank you for using this dataset!
|
[
"# Dataset Card for \"GPT4-8K\"\n\nSure! Here's a URL file for your dataset:",
"# Dataset Description\n\nThis dataset was generated using GPT-4, a powerful language model developed by OpenAI. It contains a collection of dialogs between a user and an assistant, along with additional information.\n from OpenChat",
"## Dataset Configurations\n\nThe dataset includes the following configurations:\n\n- Config Name: default\n\n - Data Files:\n - Split: train\n - Path: data/train-*",
"## Dataset Information\n\nThe dataset consists of the following features:\n\n- Dialogs: A sequence of strings representing the dialog between the user and the assistant.\n- User: A sequence of strings representing the user's input during the dialog.\n- Assistant: A sequence of strings representing the assistant's responses during the dialog.\n- Llama2 Prompt: A string representing additional prompt information related to the Llama2 model.\n\nThe dataset is divided into the following splits:\n\n- Train:\n - Number of Bytes: 193,605,433\n - Number of Examples: 6,144",
"## Dataset Size and Download\n\n- Download Size: 90,877,640 bytes\n- Dataset Size: 193,605,433 bytes\n\nPlease note that this dataset was generated by GPT-4 and may contain synthetic or simulated data. It is intended for research and experimentation purposes.\n\nFor more information or inquiries, please contact the dataset owner.\n\nThank you for using this dataset!"
] |
[
"TAGS\n#task_categories-text-classification #task_categories-translation #task_categories-conversational #task_categories-text-generation #task_categories-summarization #size_categories-1K<n<10K #language-English #region-us \n",
"# Dataset Card for \"GPT4-8K\"\n\nSure! Here's a URL file for your dataset:",
"# Dataset Description\n\nThis dataset was generated using GPT-4, a powerful language model developed by OpenAI. It contains a collection of dialogs between a user and an assistant, along with additional information.\n from OpenChat",
"## Dataset Configurations\n\nThe dataset includes the following configurations:\n\n- Config Name: default\n\n - Data Files:\n - Split: train\n - Path: data/train-*",
"## Dataset Information\n\nThe dataset consists of the following features:\n\n- Dialogs: A sequence of strings representing the dialog between the user and the assistant.\n- User: A sequence of strings representing the user's input during the dialog.\n- Assistant: A sequence of strings representing the assistant's responses during the dialog.\n- Llama2 Prompt: A string representing additional prompt information related to the Llama2 model.\n\nThe dataset is divided into the following splits:\n\n- Train:\n - Number of Bytes: 193,605,433\n - Number of Examples: 6,144",
"## Dataset Size and Download\n\n- Download Size: 90,877,640 bytes\n- Dataset Size: 193,605,433 bytes\n\nPlease note that this dataset was generated by GPT-4 and may contain synthetic or simulated data. It is intended for research and experimentation purposes.\n\nFor more information or inquiries, please contact the dataset owner.\n\nThank you for using this dataset!"
] |
[
73,
26,
47,
39,
137,
87
] |
[
"passage: TAGS\n#task_categories-text-classification #task_categories-translation #task_categories-conversational #task_categories-text-generation #task_categories-summarization #size_categories-1K<n<10K #language-English #region-us \n# Dataset Card for \"GPT4-8K\"\n\nSure! Here's a URL file for your dataset:# Dataset Description\n\nThis dataset was generated using GPT-4, a powerful language model developed by OpenAI. It contains a collection of dialogs between a user and an assistant, along with additional information.\n from OpenChat## Dataset Configurations\n\nThe dataset includes the following configurations:\n\n- Config Name: default\n\n - Data Files:\n - Split: train\n - Path: data/train-*## Dataset Information\n\nThe dataset consists of the following features:\n\n- Dialogs: A sequence of strings representing the dialog between the user and the assistant.\n- User: A sequence of strings representing the user's input during the dialog.\n- Assistant: A sequence of strings representing the assistant's responses during the dialog.\n- Llama2 Prompt: A string representing additional prompt information related to the Llama2 model.\n\nThe dataset is divided into the following splits:\n\n- Train:\n - Number of Bytes: 193,605,433\n - Number of Examples: 6,144## Dataset Size and Download\n\n- Download Size: 90,877,640 bytes\n- Dataset Size: 193,605,433 bytes\n\nPlease note that this dataset was generated by GPT-4 and may contain synthetic or simulated data. It is intended for research and experimentation purposes.\n\nFor more information or inquiries, please contact the dataset owner.\n\nThank you for using this dataset!"
] |
4cf2d0d19791d5f98c2b59dbf99463f5d44fd694
|
<!-- header start -->
<div style="min-width:100%">
<center>
<img style="max-width:200px" src="https://huggingface.co/datasets/dylanalloy/swan/resolve/main/swan.png">
<h3>swan</h3>
<small>aggressively updated financial text dataset</small>
<a href="https://github.com/DylanAlloy/swan_scrape" target="_blank">scraping code</a>
</center>
</div>
<!-- header end -->
### usage
```python
from datasets import load_dataset
sets = ["corpus", "corpus_deduped"]
swan_data, swan_deduped = [load_dataset("dylanalloy/swan", data_files=f"{_}.txt") for _ in sets]
swan_data, swan_deduped
```
### data
<center>
| data | added |
| ----------- | ----------- |
| SEC filings | Wed. Aug 30th, 2023 |
| Federal Reserve transcripts | Wed. Aug 30th, 2023 |
| private wealth management releases | Wed. Aug 30th, 2023 |
| large bank releases | Wed. Aug 30th, 2023 |
| large fund releases | Wed. Aug 30th, 2023 |
| large trading firm releases | Wed. Aug 30th, 2023 |
| BLS JOLTS releases | Wed. Aug 30th, 2023 |
| BLS CPI releases | Wed. Aug 30th, 2023 |
| BLS CES releases | Wed. Aug 30th, 2023 |
| BLS historical reports | Wed. Aug 30th, 2023 |
</center>
### updates
<small>this repo updates daily at 6AM EST</small>
| SEC Filngs | Federal Reserve transcripts | releases & reports |
| :--- | :----: | ---: |
| 30 minutes | daily | daily |
<small>🐒 **corpus** ⌨️ updated daily</small>
### stats and delta
<center>
<img style="max-width:100%" src="https://huggingface.co/datasets/dylanalloy/swan/resolve/main/words_sizes.png">
<img style="max-width:100%" src="https://huggingface.co/datasets/dylanalloy/swan/resolve/main/vocab_sizes_time.png">
</center>
### organization
- *.csv: tracker
- corpus.txt: collated text from all documents across all categories (designed for base model training)
- corpus_deduped.txt: unique lines of corpus
|
dylanalloy/swan
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"finance",
"legal",
"region:us"
] |
2023-09-06T09:39:38+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "swan - finance dataset", "tags": ["finance", "legal"], "configs": [{"config_name": "default", "data_files": [{"split": "corpus", "path": "corpus.txt"}, {"split": "corpus_deduped", "path": "corpus_deduped.txt"}, {"split": "sec_tracker", "path": "all_sec_filings.csv"}, {"split": "leaked_tracker", "path": "all_leaked_pdfs.csv"}, {"split": "fed_tracker", "path": "all_fed_filings.csv"}, {"split": "bls_jolts_tracker", "path": "all_bls_jolts.csv"}, {"split": "bls_cpi_tracker", "path": "all_bls_cpi.csv"}, {"split": "bls_ces_tracker", "path": "all_bls_ces.csv"}, {"split": "bls_historical_tracker", "path": "all_bls_historical.csv"}]}]}
|
2023-11-21T11:00:06+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-cc-by-nc-4.0 #finance #legal #region-us
|
<img style="max-width:200px" src="URL
<h3>swan
aggressively updated financial text dataset
<a href="URL target="\_blank">scraping code
### usage
### data
### updates
this repo updates daily at 6AM EST
corpus ⌨️ updated daily
### stats and delta
<img style="max-width:100%" src="URL
<img style="max-width:100%" src="URL
</center>
### organization
* \*.csv: tracker
* URL: collated text from all documents across all categories (designed for base model training)
* corpus\_deduped.txt: unique lines of corpus
|
[
"### usage",
"### data",
"### updates\n\n\nthis repo updates daily at 6AM EST\n\n\n\n corpus ⌨️ updated daily",
"### stats and delta\n\n\n\n<img style=\"max-width:100%\" src=\"URL\n<img style=\"max-width:100%\" src=\"URL\n</center>",
"### organization\n\n\n* \\*.csv: tracker\n* URL: collated text from all documents across all categories (designed for base model training)\n* corpus\\_deduped.txt: unique lines of corpus"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-cc-by-nc-4.0 #finance #legal #region-us \n",
"### usage",
"### data",
"### updates\n\n\nthis repo updates daily at 6AM EST\n\n\n\n corpus ⌨️ updated daily",
"### stats and delta\n\n\n\n<img style=\"max-width:100%\" src=\"URL\n<img style=\"max-width:100%\" src=\"URL\n</center>",
"### organization\n\n\n* \\*.csv: tracker\n* URL: collated text from all documents across all categories (designed for base model training)\n* corpus\\_deduped.txt: unique lines of corpus"
] |
[
49,
3,
3,
17,
39,
48
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-cc-by-nc-4.0 #finance #legal #region-us \n### usage### data### updates\n\n\nthis repo updates daily at 6AM EST\n\n\n\n corpus ⌨️ updated daily### stats and delta\n\n\n\n<img style=\"max-width:100%\" src=\"URL\n<img style=\"max-width:100%\" src=\"URL\n</center>### organization\n\n\n* \\*.csv: tracker\n* URL: collated text from all documents across all categories (designed for base model training)\n* corpus\\_deduped.txt: unique lines of corpus"
] |
8c8d4a6ac6ac7da201baee43db1529c426ebcb75
|
dataset sources:
shark_dataset_location = "https://www.kaggle.com/datasets/mysarahmadbhat/shark-attacks"
nba_dataset_location = "https://zenodo.org/record/6419727"
fec_dataset_location = "https://github.com/wesm/pydata-book/blob/2nd-edition/datasets/fec/P00000001-ALL.csv"
|
vuducanh/b3-userstudy-data
|
[
"license:mit",
"region:us"
] |
2023-09-06T09:41:57+00:00
|
{"license": "mit"}
|
2023-10-23T11:57:27+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
dataset sources:
shark_dataset_location = "URL
nba_dataset_location = "URL
fec_dataset_location = "URL
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
0a82df2174dab2b5bfcf183c1296fed3894e5b8c
|
# Dataset Card for "10-K_sec_filings"
Dataset of 93.5K 10K SEC EDGAR filings since 1999 year. This dataset contains a lot of bad parsed filings and also empty rows
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
winterForestStump/10-K_sec_filings
|
[
"region:us"
] |
2023-09-06T10:14:43+00:00
|
{"dataset_info": {"features": [{"name": "cik", "dtype": "int64"}, {"name": "company_name", "dtype": "string"}, {"name": "filing_date", "dtype": "timestamp[ns]"}, {"name": "Business", "dtype": "string"}, {"name": "Risk Factors", "dtype": "string"}, {"name": "Unresolved Staff Comments", "dtype": "string"}, {"name": "Properties", "dtype": "string"}, {"name": "Legal Proceedings", "dtype": "string"}, {"name": "Mine Safety Disclosures", "dtype": "string"}, {"name": "Market for Registrant\u2019s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities", "dtype": "string"}, {"name": "Selected Financial Data", "dtype": "string"}, {"name": "Management\u2019s Discussion and Analysis of Financial Condition and Results of Operations", "dtype": "string"}, {"name": "Quantitative and Qualitative Disclosures about Market Risk", "dtype": "string"}, {"name": "Financial Statements and Supplementary Data", "dtype": "string"}, {"name": "Changes in and Disagreements with Accountants on Accounting and Financial Disclosure", "dtype": "string"}, {"name": "Controls and Procedures", "dtype": "string"}, {"name": "Other Information", "dtype": "string"}, {"name": "Directors, Executive Officers and Corporate Governance", "dtype": "string"}, {"name": "Executive Compensation", "dtype": "string"}, {"name": "Security Ownership of Certain Beneficial Owners and Management and Related Stockholder Matters", "dtype": "string"}, {"name": "Certain Relationships and Related Transactions, and Director Independence", "dtype": "string"}, {"name": "Principal Accountant Fees and Services", "dtype": "string"}, {"name": "Exhibits, Financial Statement Schedules", "dtype": "string"}], "splits": [{"name": "001", "num_bytes": 1305976147, "num_examples": 5000}, {"name": "002", "num_bytes": 1547107096, "num_examples": 5000}, {"name": "003", "num_bytes": 1500950344, "num_examples": 5000}, {"name": "004", "num_bytes": 938669696, "num_examples": 3000}, {"name": "005", "num_bytes": 1161187900, "num_examples": 4000}, {"name": "006", "num_bytes": 937988835, "num_examples": 3000}, {"name": "007", "num_bytes": 694775532, "num_examples": 2000}, {"name": "008", "num_bytes": 866183252, "num_examples": 3000}, {"name": "009", "num_bytes": 705057218, "num_examples": 3000}, {"name": "010", "num_bytes": 705057218, "num_examples": 3000}, {"name": "011", "num_bytes": 885667244, "num_examples": 2000}, {"name": "012", "num_bytes": 329414277, "num_examples": 2000}, {"name": "013", "num_bytes": 739146986, "num_examples": 3000}, {"name": "014", "num_bytes": 458266896, "num_examples": 1000}, {"name": "015", "num_bytes": 710988934, "num_examples": 2000}, {"name": "016", "num_bytes": 250689742, "num_examples": 2000}, {"name": "017", "num_bytes": 474864951, "num_examples": 2000}, {"name": "018", "num_bytes": 615827939, "num_examples": 2000}, {"name": "019", "num_bytes": 357457451, "num_examples": 1000}, {"name": "020", "num_bytes": 584057786, "num_examples": 2000}, {"name": "021", "num_bytes": 141712850, "num_examples": 2000}, {"name": "022", "num_bytes": 503977366, "num_examples": 2000}, {"name": "023", "num_bytes": 468353001, "num_examples": 2000}, {"name": "024", "num_bytes": 450924639, "num_examples": 1000}, {"name": "025", "num_bytes": 504057453, "num_examples": 2000}, {"name": "026", "num_bytes": 169593248, "num_examples": 2000}, {"name": "027", "num_bytes": 464799632, "num_examples": 2000}, {"name": "028", "num_bytes": 297637001, "num_examples": 1000}, {"name": "029", "num_bytes": 368760540, "num_examples": 1000}, {"name": "030", "num_bytes": 319606303, "num_examples": 1000}, {"name": "031", "num_bytes": 394028378, "num_examples": 2000}, {"name": "032", "num_bytes": 343965348, "num_examples": 2000}, {"name": "033", "num_bytes": 522452994, "num_examples": 1999}, {"name": "034", "num_bytes": 509087440, "num_examples": 1000}, {"name": "035", "num_bytes": 509775862, "num_examples": 1001}, {"name": "036", "num_bytes": 437503604, "num_examples": 1000}, {"name": "037", "num_bytes": 610792518, "num_examples": 2000}, {"name": "038", "num_bytes": 581885486, "num_examples": 2000}, {"name": "039", "num_bytes": 350277811, "num_examples": 1000}, {"name": "040", "num_bytes": 627141247, "num_examples": 1500}, {"name": "041", "num_bytes": 305018992, "num_examples": 700}, {"name": "042", "num_bytes": 555710158, "num_examples": 600}, {"name": "043", "num_bytes": 593433327, "num_examples": 500}, {"name": "044", "num_bytes": 352017311, "num_examples": 700}, {"name": "045", "num_bytes": 342614047, "num_examples": 1000}, {"name": "046", "num_bytes": 323563296, "num_examples": 1000}, {"name": "047", "num_bytes": 236981244, "num_examples": 1000}, {"name": "048", "num_bytes": 622649279, "num_examples": 1000}, {"name": "049", "num_bytes": 358151664, "num_examples": 1000}, {"name": "050", "num_bytes": 661144363, "num_examples": 1000}, {"name": "051", "num_bytes": 421673110, "num_examples": 400}, {"name": "052", "num_bytes": 317359748, "num_examples": 100}], "download_size": 13361256647, "dataset_size": 29477068619}, "configs": [{"config_name": "default", "data_files": [{"split": "001", "path": "data/001-*"}, {"split": "002", "path": "data/002-*"}, {"split": "003", "path": "data/003-*"}, {"split": "004", "path": "data/004-*"}, {"split": "005", "path": "data/005-*"}, {"split": "006", "path": "data/006-*"}, {"split": "007", "path": "data/007-*"}, {"split": "008", "path": "data/008-*"}, {"split": "009", "path": "data/009-*"}, {"split": "010", "path": "data/010-*"}, {"split": "011", "path": "data/011-*"}, {"split": "012", "path": "data/012-*"}, {"split": "013", "path": "data/013-*"}, {"split": "014", "path": "data/014-*"}, {"split": "015", "path": "data/015-*"}, {"split": "016", "path": "data/016-*"}, {"split": "017", "path": "data/017-*"}, {"split": "018", "path": "data/018-*"}, {"split": "019", "path": "data/019-*"}, {"split": "020", "path": "data/020-*"}, {"split": "021", "path": "data/021-*"}, {"split": "022", "path": "data/022-*"}, {"split": "023", "path": "data/023-*"}, {"split": "024", "path": "data/024-*"}, {"split": "025", "path": "data/025-*"}, {"split": "026", "path": "data/026-*"}, {"split": "027", "path": "data/027-*"}, {"split": "028", "path": "data/028-*"}, {"split": "029", "path": "data/029-*"}, {"split": "030", "path": "data/030-*"}, {"split": "031", "path": "data/031-*"}, {"split": "032", "path": "data/032-*"}, {"split": "033", "path": "data/033-*"}, {"split": "034", "path": "data/034-*"}, {"split": "035", "path": "data/035-*"}, {"split": "036", "path": "data/036-*"}, {"split": "037", "path": "data/037-*"}, {"split": "038", "path": "data/038-*"}, {"split": "039", "path": "data/039-*"}, {"split": "040", "path": "data/040-*"}, {"split": "041", "path": "data/041-*"}, {"split": "042", "path": "data/042-*"}, {"split": "043", "path": "data/043-*"}, {"split": "044", "path": "data/044-*"}, {"split": "045", "path": "data/045-*"}, {"split": "046", "path": "data/046-*"}, {"split": "047", "path": "data/047-*"}, {"split": "048", "path": "data/048-*"}, {"split": "049", "path": "data/049-*"}, {"split": "050", "path": "data/050-*"}, {"split": "051", "path": "data/051-*"}, {"split": "052", "path": "data/052-*"}]}]}
|
2023-10-03T18:39:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "10-K_sec_filings"
Dataset of 93.5K 10K SEC EDGAR filings since 1999 year. This dataset contains a lot of bad parsed filings and also empty rows
More Information needed
|
[
"# Dataset Card for \"10-K_sec_filings\"\n\nDataset of 93.5K 10K SEC EDGAR filings since 1999 year. This dataset contains a lot of bad parsed filings and also empty rows\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"10-K_sec_filings\"\n\nDataset of 93.5K 10K SEC EDGAR filings since 1999 year. This dataset contains a lot of bad parsed filings and also empty rows\nMore Information needed"
] |
[
6,
54
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"10-K_sec_filings\"\n\nDataset of 93.5K 10K SEC EDGAR filings since 1999 year. This dataset contains a lot of bad parsed filings and also empty rows\nMore Information needed"
] |
8e199ef88032dd87eab8cf1dc0cf31c6af9b165f
|
# Sayori Chat 09062023 raw
* Dataset of Sayori dialogue from DDLC (dataset of ~600 items augmented by [MythoMax-l2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) to turn into multi-turn chat dialogue)
* Curated version planned
|
922-CA/ls2_09062023_test1_raw_SaChA_1a
|
[
"license:openrail",
"region:us"
] |
2023-09-06T10:33:03+00:00
|
{"license": "openrail"}
|
2023-09-22T07:08:51+00:00
|
[] |
[] |
TAGS
#license-openrail #region-us
|
# Sayori Chat 09062023 raw
* Dataset of Sayori dialogue from DDLC (dataset of ~600 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)
* Curated version planned
|
[
"# Sayori Chat 09062023 raw\n* Dataset of Sayori dialogue from DDLC (dataset of ~600 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)\n* Curated version planned"
] |
[
"TAGS\n#license-openrail #region-us \n",
"# Sayori Chat 09062023 raw\n* Dataset of Sayori dialogue from DDLC (dataset of ~600 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)\n* Curated version planned"
] |
[
12,
54
] |
[
"passage: TAGS\n#license-openrail #region-us \n# Sayori Chat 09062023 raw\n* Dataset of Sayori dialogue from DDLC (dataset of ~600 items augmented by MythoMax-l2-13b to turn into multi-turn chat dialogue)\n* Curated version planned"
] |
2b4474d8ecb1e7baae29ebd39fbb40536b21b378
|
https://huggingface.co/datasets/monology/pile
|
xnywn/pile
|
[
"region:us"
] |
2023-09-06T10:41:33+00:00
|
{}
|
2023-09-06T10:49:54+00:00
|
[] |
[] |
TAGS
#region-us
|
URL
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
ca1a31b06826c9c43230b67d9772a08025d16c03
|
# Dataset Card for "SynTruckUnity"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
malteee/SynTruckObjDet
|
[
"region:us"
] |
2023-09-06T11:11:51+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "mask", "dtype": "image"}, {"name": "bbox", "list": [{"name": "category", "dtype": "int64"}, {"name": "position", "sequence": "float64"}]}], "splits": [{"name": "train", "num_bytes": 100362995.0, "num_examples": 100}], "download_size": 99562410, "dataset_size": 100362995.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-06T12:06:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SynTruckUnity"
More Information needed
|
[
"# Dataset Card for \"SynTruckUnity\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SynTruckUnity\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SynTruckUnity\"\n\nMore Information needed"
] |
a4ef0c02c5b2e8c844b42547fa0b27cea2b5ee99
|
# Dataset Card for "open_assistant_dataset_finetuned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bilalahmadai/open_assistant_dataset_finetuned
|
[
"region:us"
] |
2023-09-06T11:19:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 836135, "num_examples": 2000}], "download_size": 490503, "dataset_size": 836135}}
|
2023-09-06T11:20:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "open_assistant_dataset_finetuned"
More Information needed
|
[
"# Dataset Card for \"open_assistant_dataset_finetuned\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"open_assistant_dataset_finetuned\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"open_assistant_dataset_finetuned\"\n\nMore Information needed"
] |
d37af85ab87e7c7eaf43b01eed93c74f623e1c1f
|
crypto data features in x and future 1/2/6/12/24h ret in y as target.
x is numpy array in shape (33, 769), 33 top liquid coins, 769 features:
h, w = 33, 769
numpy.memmap(x_path, shape=(h, w), dtype=np.float32, mode='r')
y is numpy array in shape (33, 5)
|
joelyu/small_dataset_for_testing
|
[
"region:us"
] |
2023-09-06T11:54:08+00:00
|
{}
|
2023-09-25T10:08:22+00:00
|
[] |
[] |
TAGS
#region-us
|
crypto data features in x and future 1/2/6/12/24h ret in y as target.
x is numpy array in shape (33, 769), 33 top liquid coins, 769 features:
h, w = 33, 769
URL(x_path, shape=(h, w), dtype=np.float32, mode='r')
y is numpy array in shape (33, 5)
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
a2bb94d5e40ab1077b1116c7a809a7b9a883695d
|
# Dataset Card for "data_for_synthesis_with_entities_align"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/data_for_synthesis_with_entities_align
|
[
"region:us"
] |
2023-09-06T12:10:13+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "sentence_annotation", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}, {"name": "file", "dtype": "string"}, {"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "string"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "origin_transcription", "dtype": "string"}, {"name": "sentence_norm", "dtype": "string"}, {"name": "w2v2_large_transcription", "dtype": "string"}, {"name": "wer", "dtype": "int64"}, {"name": "entities_norm", "list": [{"name": "filler", "dtype": "string"}, {"name": "type", "dtype": "string"}]}, {"name": "entities_align", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 698110051.1801205, "num_examples": 1413}], "download_size": 158745470, "dataset_size": 698110051.1801205}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-06T12:10:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data_for_synthesis_with_entities_align"
More Information needed
|
[
"# Dataset Card for \"data_for_synthesis_with_entities_align\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data_for_synthesis_with_entities_align\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data_for_synthesis_with_entities_align\"\n\nMore Information needed"
] |
6fa4634e58256fd66168b54b336724154e4ee53c
|
# Dataset Card for "SamTrain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
malteee/SamTrain
|
[
"region:us"
] |
2023-09-06T12:16:46+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "mask", "dtype": "image"}, {"name": "bbox", "list": [{"name": "category", "dtype": "int64"}, {"name": "position", "sequence": "float64"}]}], "splits": [{"name": "train", "num_bytes": 100362995.0, "num_examples": 100}], "download_size": 99562410, "dataset_size": 100362995.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-06T12:17:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SamTrain"
More Information needed
|
[
"# Dataset Card for \"SamTrain\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SamTrain\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SamTrain\"\n\nMore Information needed"
] |
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