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461dc7349657b9b581a0797093c22cefda7bceda
|
# Dataset Card for "biology_dataset_standardized_cluster_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pharaouk/biology_dataset_standardized_cluster_20
|
[
"region:us"
] |
2023-10-13T01:16:51+00:00
|
{"dataset_info": {"features": [], "splits": [{"name": "train", "num_bytes": 0, "num_examples": 0}], "download_size": 324, "dataset_size": 0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T01:16:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "biology_dataset_standardized_cluster_20"
More Information needed
|
[
"# Dataset Card for \"biology_dataset_standardized_cluster_20\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"biology_dataset_standardized_cluster_20\"\n\nMore Information needed"
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6,
23
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] |
490bfaff8ae21a3492483931b3a1c203e18cc5d4
|
# Dataset Card for "biology_dataset_standardized_cluster_21"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pharaouk/biology_dataset_standardized_cluster_21
|
[
"region:us"
] |
2023-10-13T01:17:00+00:00
|
{"dataset_info": {"features": [], "splits": [{"name": "train", "num_bytes": 0, "num_examples": 0}], "download_size": 324, "dataset_size": 0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T01:17:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "biology_dataset_standardized_cluster_21"
More Information needed
|
[
"# Dataset Card for \"biology_dataset_standardized_cluster_21\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
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6,
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"passage: TAGS\n#region-us \n# Dataset Card for \"biology_dataset_standardized_cluster_21\"\n\nMore Information needed"
] |
4b6846a91b71a132658c283ef2883645e5364440
|
# Dataset Card for Evaluation run of TFLai/bloom-560m-4bit-alpaca
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/bloom-560m-4bit-alpaca
- **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/bloom-560m-4bit-alpaca](https://huggingface.co/TFLai/bloom-560m-4bit-alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T02:31:40.775341](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca/blob/main/results_2023-10-13T02-31-40.775341.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968570957,
"f1": 0.028393456375839028,
"f1_stderr": 0.0009648156202587861,
"acc": 0.25213936558333583,
"acc_stderr": 0.007562025280082852
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968570957,
"f1": 0.028393456375839028,
"f1_stderr": 0.0009648156202587861
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.001071779348549266
},
"harness|winogrande|5": {
"acc": 0.5027624309392266,
"acc_stderr": 0.014052271211616438
}
}
```
### 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__bloom-560m-4bit-alpaca
|
[
"region:us"
] |
2023-10-13T01:31:44+00:00
|
{"pretty_name": "Evaluation run of TFLai/bloom-560m-4bit-alpaca", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/bloom-560m-4bit-alpaca](https://huggingface.co/TFLai/bloom-560m-4bit-alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T02:31:40.775341](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__bloom-560m-4bit-alpaca/blob/main/results_2023-10-13T02-31-40.775341.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968570957,\n \"f1\": 0.028393456375839028,\n \"f1_stderr\": 0.0009648156202587861,\n \"acc\": 0.25213936558333583,\n \"acc_stderr\": 0.007562025280082852\n },\n \"harness|drop|3\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968570957,\n \"f1\": 0.028393456375839028,\n \"f1_stderr\": 0.0009648156202587861\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \"acc_stderr\": 0.001071779348549266\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5027624309392266,\n \"acc_stderr\": 0.014052271211616438\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/bloom-560m-4bit-alpaca", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T02_31_40.775341", "path": ["**/details_harness|drop|3_2023-10-13T02-31-40.775341.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-13T02-31-40.775341.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T02_31_40.775341", "path": ["**/details_harness|gsm8k|5_2023-10-13T02-31-40.775341.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-13T02-31-40.775341.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T02_31_40.775341", "path": ["**/details_harness|winogrande|5_2023-10-13T02-31-40.775341.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-13T02-31-40.775341.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T02_31_40.775341", "path": ["results_2023-10-13T02-31-40.775341.parquet"]}, {"split": "latest", "path": ["results_2023-10-13T02-31-40.775341.parquet"]}]}]}
|
2023-10-13T01:31:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/bloom-560m-4bit-alpaca
## 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/bloom-560m-4bit-alpaca on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-13T02:31:40.775341(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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] |
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] |
3765475ab8b9e66a3623c83f87569f08dcadc240
|
# Dataset Card for "govreport-qa-no-pad-32768"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shossain/govreport-qa-no-pad-32768
|
[
"region:us"
] |
2023-10-13T01:45:14+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 931486852.8093396, "num_examples": 7706}], "download_size": 286389043, "dataset_size": 931486852.8093396}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T01:45:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "govreport-qa-no-pad-32768"
More Information needed
|
[
"# Dataset Card for \"govreport-qa-no-pad-32768\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
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"passage: TAGS\n#region-us \n# Dataset Card for \"govreport-qa-no-pad-32768\"\n\nMore Information needed"
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5b9c3c7b44c4bb7bc7b9d22cd2ba57e718e781d9
|
# Dataset Card for "commonvoice_test_labeled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
NathanRoll/commonvoice_test_labeled
|
[
"region:us"
] |
2023-10-13T01:56:02+00:00
|
{"dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}, {"name": "variant", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 64662583.85453212, "num_examples": 1550}], "download_size": 64487018, "dataset_size": 64662583.85453212}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
|
2023-10-13T01:56:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "commonvoice_test_labeled"
More Information needed
|
[
"# Dataset Card for \"commonvoice_test_labeled\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"commonvoice_test_labeled\"\n\nMore Information needed"
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[
6,
19
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"commonvoice_test_labeled\"\n\nMore Information needed"
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a43a4fe53fab147ca75c96895fa1462e11423ca0
|
# Dataset Card for Evaluation run of bigscience/bloomz-560m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloomz-560m
- **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 [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_bigscience__bloomz-560m",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T12:37:15.813527](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-560m/blob/main/results_2023-12-04T12-37-15.813527.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_bigscience__bloomz-560m
|
[
"region:us"
] |
2023-10-13T01:59:41+00:00
|
{"pretty_name": "Evaluation run of bigscience/bloomz-560m", "dataset_summary": "Dataset automatically created during the evaluation run of model [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_bigscience__bloomz-560m\",\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T12:37:15.813527](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-560m/blob/main/results_2023-12-04T12-37-15.813527.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/bigscience/bloomz-560m", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T02_59_38.387630", "path": ["**/details_harness|drop|3_2023-10-13T02-59-38.387630.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-13T02-59-38.387630.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T02_59_38.387630", "path": ["**/details_harness|gsm8k|5_2023-10-13T02-59-38.387630.parquet"]}, {"split": "2023_12_03T14_34_05.520160", "path": ["**/details_harness|gsm8k|5_2023-12-03T14-34-05.520160.parquet"]}, {"split": "2023_12_03T14_34_17.552843", "path": ["**/details_harness|gsm8k|5_2023-12-03T14-34-17.552843.parquet"]}, {"split": "2023_12_03T15_36_24.223775", "path": ["**/details_harness|gsm8k|5_2023-12-03T15-36-24.223775.parquet"]}, {"split": "2023_12_03T15_36_26.532570", "path": ["**/details_harness|gsm8k|5_2023-12-03T15-36-26.532570.parquet"]}, {"split": "2023_12_04T09_27_25.322225", "path": ["**/details_harness|gsm8k|5_2023-12-04T09-27-25.322225.parquet"]}, {"split": "2023_12_04T12_37_10.556639", "path": ["**/details_harness|gsm8k|5_2023-12-04T12-37-10.556639.parquet"]}, {"split": "2023_12_04T12_37_15.813527", "path": ["**/details_harness|gsm8k|5_2023-12-04T12-37-15.813527.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T12-37-15.813527.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T02_59_38.387630", "path": ["**/details_harness|winogrande|5_2023-10-13T02-59-38.387630.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-13T02-59-38.387630.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T02_59_38.387630", "path": ["results_2023-10-13T02-59-38.387630.parquet"]}, {"split": "2023_12_03T14_34_05.520160", "path": ["results_2023-12-03T14-34-05.520160.parquet"]}, {"split": "2023_12_03T14_34_17.552843", "path": ["results_2023-12-03T14-34-17.552843.parquet"]}, {"split": "2023_12_03T15_36_24.223775", "path": ["results_2023-12-03T15-36-24.223775.parquet"]}, {"split": "2023_12_03T15_36_26.532570", "path": ["results_2023-12-03T15-36-26.532570.parquet"]}, {"split": "2023_12_04T09_27_25.322225", "path": ["results_2023-12-04T09-27-25.322225.parquet"]}, {"split": "2023_12_04T12_37_10.556639", "path": ["results_2023-12-04T12-37-10.556639.parquet"]}, {"split": "2023_12_04T12_37_15.813527", "path": ["results_2023-12-04T12-37-15.813527.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T12-37-15.813527.parquet"]}]}]}
|
2023-12-04T12:37:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of bigscience/bloomz-560m
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model bigscience/bloomz-560m on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-04T12:37:15.813527(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 bigscience/bloomz-560m",
"## 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 bigscience/bloomz-560m on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T12:37:15.813527(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 bigscience/bloomz-560m",
"## 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 bigscience/bloomz-560m on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T12:37:15.813527(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 bigscience/bloomz-560m## 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 bigscience/bloomz-560m on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-04T12:37:15.813527(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"
] |
d1cfaeb4341e713df6987ce9130a578da5ee7bc6
|
# Dataset Card for Evaluation run of MayaPH/opt-flan-iml-6.7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MayaPH/opt-flan-iml-6.7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.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": 0.07518875838926174,
"em_stderr": 0.002700490526265294,
"f1": 0.10838401845637569,
"f1_stderr": 0.0028760995167941457,
"acc": 0.3212312549329124,
"acc_stderr": 0.006735003721960345
},
"harness|drop|3": {
"em": 0.07518875838926174,
"em_stderr": 0.002700490526265294,
"f1": 0.10838401845637569,
"f1_stderr": 0.0028760995167941457
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6424625098658248,
"acc_stderr": 0.01347000744392069
}
}
```
### 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_MayaPH__opt-flan-iml-6.7b
|
[
"region:us"
] |
2023-10-13T02:06:36+00:00
|
{"pretty_name": "Evaluation run of MayaPH/opt-flan-iml-6.7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [MayaPH/opt-flan-iml-6.7b](https://huggingface.co/MayaPH/opt-flan-iml-6.7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T03:06:32.697788](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__opt-flan-iml-6.7b/blob/main/results_2023-10-13T03-06-32.697788.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\": 0.07518875838926174,\n \"em_stderr\": 0.002700490526265294,\n \"f1\": 0.10838401845637569,\n \"f1_stderr\": 0.0028760995167941457,\n \"acc\": 0.3212312549329124,\n \"acc_stderr\": 0.006735003721960345\n },\n \"harness|drop|3\": {\n \"em\": 0.07518875838926174,\n \"em_stderr\": 0.002700490526265294,\n \"f1\": 0.10838401845637569,\n \"f1_stderr\": 0.0028760995167941457\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6424625098658248,\n \"acc_stderr\": 0.01347000744392069\n }\n}\n```", "repo_url": "https://huggingface.co/MayaPH/opt-flan-iml-6.7b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T03_06_32.697788", "path": ["**/details_harness|drop|3_2023-10-13T03-06-32.697788.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-13T03-06-32.697788.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T03_06_32.697788", "path": ["**/details_harness|gsm8k|5_2023-10-13T03-06-32.697788.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-13T03-06-32.697788.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T03_06_32.697788", "path": ["**/details_harness|winogrande|5_2023-10-13T03-06-32.697788.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-13T03-06-32.697788.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T03_06_32.697788", "path": ["results_2023-10-13T03-06-32.697788.parquet"]}, {"split": "latest", "path": ["results_2023-10-13T03-06-32.697788.parquet"]}]}]}
|
2023-10-13T02:06:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of MayaPH/opt-flan-iml-6.7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model MayaPH/opt-flan-iml-6.7b on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-13T03:06:32.697788(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 MayaPH/opt-flan-iml-6.7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model MayaPH/opt-flan-iml-6.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T03:06:32.697788(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 MayaPH/opt-flan-iml-6.7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model MayaPH/opt-flan-iml-6.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T03:06:32.697788(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 MayaPH/opt-flan-iml-6.7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model MayaPH/opt-flan-iml-6.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-13T03:06:32.697788(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"
] |
4c035a7498f639df12e523e8ec067975286ee194
|
# Dataset Card for QA-Expert-multi-hop-qa-V1.0
This dataset aims to provide **multi-domain** training data for the task: Question Answering, with a focus on <b>Multi-hop Question Answering</b>.
In total, this dataset contains 25.5k for training and 3.19k for evaluation.
You can take a look at the model we trained on this data: [https://huggingface.co/khaimaitien/qa-expert-7B-V1.0](https://huggingface.co/khaimaitien/qa-expert-7B-V1.0)
The dataset is mostly generated using the OpenAPI model (**gpt-3.5-turbo-instruct**). Please read more information about how we created this dataset from here: [https://github.com/khaimt/qa_expert/tree/main/gen_data](https://github.com/khaimt/qa_expert/tree/main/gen_data)
. The repository contains the **scripts for generating the training data**, so you can run the available scripts to generate more data.
Example of single question: what is the capital city of Vietnam?
Example of multi-hop question: what is the population of the capital city of Vietnam?
## Dataset Details
### Dataset Description
### Format
Each data point is a Json:
+ **question**: the question, can be single question or multi-hop question
+ **multihop**: True/False whether the question is multihop or not
+ **sub_questions**: List of decomposed single questions from question. If the question is single question, ```len(sub_questions) == 1```
+ **question**: single question decomposed from original multi-hop question
+ **paragraph**: the retrieval context for the single question
+ **long_answer**: the answer to the single question, the format is: xxx\nAnswer:yyy where xxx is the reasoning (thought) before generte answer to the question.
+ **final_answer**: The final answer to the question. If the question is multihop, this has the form: Summary:xxx\nAnswer:yyy Where xxx is the summary of anwers from decomposed single questions before generating final answer: yyy
+ **answer**: <i>Can ignore this field</i>
+ **meta_info**: contains the information about how the data point was created
+ **tag**: <i>can ignore this field</i>
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
```
@Misc{qa-expert,
title={QA Expert: LLM for Multi-hop Question Answering},
author={Khai Mai},
howpublished={\url{https://github.com/khaimt/qa_expert}},
year={2023},
}
```
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
khaimaitien/qa-expert-multi-hop-qa-V1.0
|
[
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
2023-10-13T02:17:28+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "Multi-hop Question Answering"}
|
2023-11-15T17:39:13+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-English #region-us
|
# Dataset Card for QA-Expert-multi-hop-qa-V1.0
This dataset aims to provide multi-domain training data for the task: Question Answering, with a focus on <b>Multi-hop Question Answering</b>.
In total, this dataset contains 25.5k for training and 3.19k for evaluation.
You can take a look at the model we trained on this data: URL
The dataset is mostly generated using the OpenAPI model (gpt-3.5-turbo-instruct). Please read more information about how we created this dataset from here: URL
. The repository contains the scripts for generating the training data, so you can run the available scripts to generate more data.
Example of single question: what is the capital city of Vietnam?
Example of multi-hop question: what is the population of the capital city of Vietnam?
## Dataset Details
### Dataset Description
### Format
Each data point is a Json:
+ question: the question, can be single question or multi-hop question
+ multihop: True/False whether the question is multihop or not
+ sub_questions: List of decomposed single questions from question. If the question is single question,
+ question: single question decomposed from original multi-hop question
+ paragraph: the retrieval context for the single question
+ long_answer: the answer to the single question, the format is: xxx\nAnswer:yyy where xxx is the reasoning (thought) before generte answer to the question.
+ final_answer: The final answer to the question. If the question is multihop, this has the form: Summary:xxx\nAnswer:yyy Where xxx is the summary of anwers from decomposed single questions before generating final answer: yyy
+ answer: <i>Can ignore this field</i>
+ meta_info: contains the information about how the data point was created
+ tag: <i>can ignore this field</i>
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for QA-Expert-multi-hop-qa-V1.0\nThis dataset aims to provide multi-domain training data for the task: Question Answering, with a focus on <b>Multi-hop Question Answering</b>. \nIn total, this dataset contains 25.5k for training and 3.19k for evaluation.\nYou can take a look at the model we trained on this data: URL\n\nThe dataset is mostly generated using the OpenAPI model (gpt-3.5-turbo-instruct). Please read more information about how we created this dataset from here: URL\n. The repository contains the scripts for generating the training data, so you can run the available scripts to generate more data.\n\nExample of single question: what is the capital city of Vietnam?\nExample of multi-hop question: what is the population of the capital city of Vietnam?",
"## Dataset Details",
"### Dataset Description",
"### Format \nEach data point is a Json:\n+ question: the question, can be single question or multi-hop question\n+ multihop: True/False whether the question is multihop or not \n+ sub_questions: List of decomposed single questions from question. If the question is single question, \n + question: single question decomposed from original multi-hop question\n + paragraph: the retrieval context for the single question\n + long_answer: the answer to the single question, the format is: xxx\\nAnswer:yyy where xxx is the reasoning (thought) before generte answer to the question.\n+ final_answer: The final answer to the question. If the question is multihop, this has the form: Summary:xxx\\nAnswer:yyy Where xxx is the summary of anwers from decomposed single questions before generating final answer: yyy\n+ answer: <i>Can ignore this field</i>\n+ meta_info: contains the information about how the data point was created\n+ tag: <i>can ignore this field</i>\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP):",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-English #region-us \n",
"# Dataset Card for QA-Expert-multi-hop-qa-V1.0\nThis dataset aims to provide multi-domain training data for the task: Question Answering, with a focus on <b>Multi-hop Question Answering</b>. \nIn total, this dataset contains 25.5k for training and 3.19k for evaluation.\nYou can take a look at the model we trained on this data: URL\n\nThe dataset is mostly generated using the OpenAPI model (gpt-3.5-turbo-instruct). Please read more information about how we created this dataset from here: URL\n. The repository contains the scripts for generating the training data, so you can run the available scripts to generate more data.\n\nExample of single question: what is the capital city of Vietnam?\nExample of multi-hop question: what is the population of the capital city of Vietnam?",
"## Dataset Details",
"### Dataset Description",
"### Format \nEach data point is a Json:\n+ question: the question, can be single question or multi-hop question\n+ multihop: True/False whether the question is multihop or not \n+ sub_questions: List of decomposed single questions from question. If the question is single question, \n + question: single question decomposed from original multi-hop question\n + paragraph: the retrieval context for the single question\n + long_answer: the answer to the single question, the format is: xxx\\nAnswer:yyy where xxx is the reasoning (thought) before generte answer to the question.\n+ final_answer: The final answer to the question. If the question is multihop, this has the form: Summary:xxx\\nAnswer:yyy Where xxx is the summary of anwers from decomposed single questions before generating final answer: yyy\n+ answer: <i>Can ignore this field</i>\n+ meta_info: contains the information about how the data point was created\n+ tag: <i>can ignore this field</i>\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP):",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
45,
195,
4,
5,
268,
29,
3,
4,
9,
6,
5,
7,
4,
7,
10,
9,
5,
9,
8,
10,
46,
8,
7,
10,
5
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[
"passage: TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-English #region-us \n# Dataset Card for QA-Expert-multi-hop-qa-V1.0\nThis dataset aims to provide multi-domain training data for the task: Question Answering, with a focus on <b>Multi-hop Question Answering</b>. \nIn total, this dataset contains 25.5k for training and 3.19k for evaluation.\nYou can take a look at the model we trained on this data: URL\n\nThe dataset is mostly generated using the OpenAPI model (gpt-3.5-turbo-instruct). Please read more information about how we created this dataset from here: URL\n. The repository contains the scripts for generating the training data, so you can run the available scripts to generate more data.\n\nExample of single question: what is the capital city of Vietnam?\nExample of multi-hop question: what is the population of the capital city of Vietnam?## Dataset Details### Dataset Description"
] |
1776802c297a38160841c6232dea033cc2a1cdb3
|
# Dataset Card for Evaluation run of huggingtweets/gladosystem
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/huggingtweets/gladosystem
- **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 [huggingtweets/gladosystem](https://huggingface.co/huggingtweets/gladosystem) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_huggingtweets__gladosystem",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T03:18:40.922910](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingtweets__gladosystem/blob/main/results_2023-10-13T03-18-40.922910.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": 0.010276845637583893,
"em_stderr": 0.0010328242665282317,
"f1": 0.014896182885906039,
"f1_stderr": 0.0011273085873104653,
"acc": 0.2533543804262036,
"acc_stderr": 0.0070256103461651745
},
"harness|drop|3": {
"em": 0.010276845637583893,
"em_stderr": 0.0010328242665282317,
"f1": 0.014896182885906039,
"f1_stderr": 0.0011273085873104653
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5067087608524072,
"acc_stderr": 0.014051220692330349
}
}
```
### 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_huggingtweets__gladosystem
|
[
"region:us"
] |
2023-10-13T02:18:43+00:00
|
{"pretty_name": "Evaluation run of huggingtweets/gladosystem", "dataset_summary": "Dataset automatically created during the evaluation run of model [huggingtweets/gladosystem](https://huggingface.co/huggingtweets/gladosystem) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_huggingtweets__gladosystem\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T03:18:40.922910](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingtweets__gladosystem/blob/main/results_2023-10-13T03-18-40.922910.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\": 0.010276845637583893,\n \"em_stderr\": 0.0010328242665282317,\n \"f1\": 0.014896182885906039,\n \"f1_stderr\": 0.0011273085873104653,\n \"acc\": 0.2533543804262036,\n \"acc_stderr\": 0.0070256103461651745\n },\n \"harness|drop|3\": {\n \"em\": 0.010276845637583893,\n \"em_stderr\": 0.0010328242665282317,\n \"f1\": 0.014896182885906039,\n \"f1_stderr\": 0.0011273085873104653\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5067087608524072,\n \"acc_stderr\": 0.014051220692330349\n }\n}\n```", "repo_url": "https://huggingface.co/huggingtweets/gladosystem", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T03_18_40.922910", "path": ["**/details_harness|drop|3_2023-10-13T03-18-40.922910.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-13T03-18-40.922910.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T03_18_40.922910", "path": ["**/details_harness|gsm8k|5_2023-10-13T03-18-40.922910.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-13T03-18-40.922910.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T03_18_40.922910", "path": ["**/details_harness|winogrande|5_2023-10-13T03-18-40.922910.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-13T03-18-40.922910.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T03_18_40.922910", "path": ["results_2023-10-13T03-18-40.922910.parquet"]}, {"split": "latest", "path": ["results_2023-10-13T03-18-40.922910.parquet"]}]}]}
|
2023-10-13T02:18:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of huggingtweets/gladosystem
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model huggingtweets/gladosystem on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-13T03:18:40.922910(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 huggingtweets/gladosystem",
"## 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 huggingtweets/gladosystem on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T03:18:40.922910(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 huggingtweets/gladosystem",
"## 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 huggingtweets/gladosystem on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T03:18:40.922910(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",
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"### Data Fields",
"### Data Splits",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of huggingtweets/gladosystem## 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 huggingtweets/gladosystem on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-13T03:18:40.922910(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"
] |
fc5337b5e58cdeea4820ba2b93b17b2becd223fe
|
## model_setting_name: platy
## max_context_length: 512
## icl_examples: 5
## icl_dataset_name: lukaemon/mmlu
## max_documents_per_subject: 1000
## max_contexts_per_subject: 1000000
## icl_use_out_options: True
## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all
## subjects: SUB_10
## response_template: 1
## inverse_template: 0
|
ostapeno/platy_icl5_maxD1000_maxC1000000_prmt10_1
|
[
"region:us"
] |
2023-10-13T02:27:51+00:00
|
{}
|
2023-10-13T02:28:01+00:00
|
[] |
[] |
TAGS
#region-us
|
## model_setting_name: platy
## max_context_length: 512
## icl_examples: 5
## icl_dataset_name: lukaemon/mmlu
## max_documents_per_subject: 1000
## max_contexts_per_subject: 1000000
## icl_use_out_options: True
## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all
## subjects: SUB_10
## response_template: 1
## inverse_template: 0
|
[
"## model_setting_name: platy",
"## max_context_length: 512",
"## icl_examples: 5",
"## icl_dataset_name: lukaemon/mmlu",
"## max_documents_per_subject: 1000",
"## max_contexts_per_subject: 1000000",
"## icl_use_out_options: True",
"## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all",
"## subjects: SUB_10",
"## response_template: 1",
"## inverse_template: 0"
] |
[
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"## model_setting_name: platy",
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"## icl_examples: 5",
"## icl_dataset_name: lukaemon/mmlu",
"## max_documents_per_subject: 1000",
"## max_contexts_per_subject: 1000000",
"## icl_use_out_options: True",
"## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all",
"## subjects: SUB_10",
"## response_template: 1",
"## inverse_template: 0"
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"passage: TAGS\n#region-us \n## model_setting_name: platy## max_context_length: 512## icl_examples: 5## icl_dataset_name: lukaemon/mmlu## max_documents_per_subject: 1000## max_contexts_per_subject: 1000000## icl_use_out_options: True## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all## subjects: SUB_10## response_template: 1## inverse_template: 0"
] |
e67dd519e6c5a0e83a55a91ae6cd91821cb0ac9f
|
# Dataset Card for "stack-exchange-paired-128K"
## token數
llama2: 97868021
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlexHung29629/stack-exchange-paired-128K
|
[
"region:us"
] |
2023-10-13T03:07:53+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 243412260, "num_examples": 128000}], "download_size": 82603750, "dataset_size": 243412260}}
|
2023-10-13T04:42:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "stack-exchange-paired-128K"
## token數
llama2: 97868021
More Information needed
|
[
"# Dataset Card for \"stack-exchange-paired-128K\"",
"## token數\nllama2: 97868021\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"stack-exchange-paired-128K\"",
"## token數\nllama2: 97868021\n\nMore Information needed"
] |
[
6,
18,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"stack-exchange-paired-128K\"## token數\nllama2: 97868021\n\nMore Information needed"
] |
452f4987be27735f58fa7c6a3bf92fea1225357f
|
# Dataset Card for "isic-2017-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TynClause/isic-2017-dataset
|
[
"region:us"
] |
2023-10-13T03:27:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "melanoma", "1": "nevus", "2": "seborrheic_keratosis"}}}}], "splits": [{"name": "train", "num_bytes": 3279824246.0, "num_examples": 2000}, {"name": "validation", "num_bytes": 869316023.0, "num_examples": 150}, {"name": "test", "num_bytes": 5548533480.0, "num_examples": 600}], "download_size": 12198254676, "dataset_size": 9697673749.0}}
|
2023-10-13T03:51:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "isic-2017-dataset"
More Information needed
|
[
"# Dataset Card for \"isic-2017-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"isic-2017-dataset\"\n\nMore Information needed"
] |
[
6,
16
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"isic-2017-dataset\"\n\nMore Information needed"
] |
421fd2e95509196ddb8c3bb6f1a356a66a02390d
|
# Dataset Card for "new_vt_apis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hmao/new_vt_apis
|
[
"region:us"
] |
2023-10-13T03:28:16+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "args_dicts", "list": [{"name": "default", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "required", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "api_type", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "dataset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20764, "num_examples": 29}], "download_size": 14860, "dataset_size": 20764}}
|
2023-10-25T23:50:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "new_vt_apis"
More Information needed
|
[
"# Dataset Card for \"new_vt_apis\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"new_vt_apis\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"new_vt_apis\"\n\nMore Information needed"
] |
8f628bfe1a755a4378d3fa24b02b875b38480b03
|
# Dataset Card for "chat-gvg-rings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yangp/chat-gvg-rings
|
[
"region:us"
] |
2023-10-13T03:30:29+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59359950, "num_examples": 7858}], "download_size": 20556706, "dataset_size": 59359950}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T06:19:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "chat-gvg-rings"
More Information needed
|
[
"# Dataset Card for \"chat-gvg-rings\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"chat-gvg-rings\"\n\nMore Information needed"
] |
[
6,
17
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"chat-gvg-rings\"\n\nMore Information needed"
] |
fc039022cc39281971c03768b2544bf72d948604
|
# Dataset Card for Evaluation run of RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT](https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T04:33:28.538192](https://huggingface.co/datasets/open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT/blob/main/results_2023-10-13T04-33-28.538192.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": 0.0012583892617449664,
"em_stderr": 0.00036305608931188796,
"f1": 0.052196937919463185,
"f1_stderr": 0.0012732861194066877,
"acc": 0.4008241516587451,
"acc_stderr": 0.009542578755221624
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931188796,
"f1": 0.052196937919463185,
"f1_stderr": 0.0012732861194066877
},
"harness|gsm8k|5": {
"acc": 0.06368460955269144,
"acc_stderr": 0.006726213078805692
},
"harness|winogrande|5": {
"acc": 0.7379636937647988,
"acc_stderr": 0.012358944431637557
}
}
```
### 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_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT
|
[
"region:us"
] |
2023-10-13T03:33:32+00:00
|
{"pretty_name": "Evaluation run of RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT", "dataset_summary": "Dataset automatically created during the evaluation run of model [RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT](https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T04:33:28.538192](https://huggingface.co/datasets/open-llm-leaderboard/details_RoversX__llama-2-7b-hf-small-shards-Samantha-V1-SFT/blob/main/results_2023-10-13T04-33-28.538192.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\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931188796,\n \"f1\": 0.052196937919463185,\n \"f1_stderr\": 0.0012732861194066877,\n \"acc\": 0.4008241516587451,\n \"acc_stderr\": 0.009542578755221624\n },\n \"harness|drop|3\": {\n \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931188796,\n \"f1\": 0.052196937919463185,\n \"f1_stderr\": 0.0012732861194066877\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06368460955269144,\n \"acc_stderr\": 0.006726213078805692\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637557\n }\n}\n```", "repo_url": "https://huggingface.co/RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T04_33_28.538192", "path": ["**/details_harness|drop|3_2023-10-13T04-33-28.538192.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-13T04-33-28.538192.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T04_33_28.538192", "path": ["**/details_harness|gsm8k|5_2023-10-13T04-33-28.538192.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-13T04-33-28.538192.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T04_33_28.538192", "path": ["**/details_harness|winogrande|5_2023-10-13T04-33-28.538192.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-13T04-33-28.538192.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T04_33_28.538192", "path": ["results_2023-10-13T04-33-28.538192.parquet"]}, {"split": "latest", "path": ["results_2023-10-13T04-33-28.538192.parquet"]}]}]}
|
2023-10-13T03:33:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-13T04:33:28.538192(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 RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T04:33:28.538192(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 RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T04:33:28.538192(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 RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-13T04:33:28.538192(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"
] |
5cb24d1e6ed6bbf6498d96629fe6d9fbb92c1e00
|
# Dataset Card for "contextual-new-ontology-v2-contextual-lowercase"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
magnifi/contextual-new-ontology-v2-contextual-lowercase
|
[
"region:us"
] |
2023-10-13T03:48:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "uid", "dtype": "string"}, {"name": "user_text", "dtype": "string"}, {"name": "true_intent", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "Source", "dtype": "string"}, {"name": "chat_history", "dtype": "string"}, {"name": "contextual", "dtype": "bool"}, {"name": "synthetic", "dtype": "bool"}, {"name": "in_regression_test", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2431425, "num_examples": 4165}, {"name": "validation", "num_bytes": 294522, "num_examples": 496}], "download_size": 779849, "dataset_size": 2725947}}
|
2023-10-13T03:48:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "contextual-new-ontology-v2-contextual-lowercase"
More Information needed
|
[
"# Dataset Card for \"contextual-new-ontology-v2-contextual-lowercase\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"contextual-new-ontology-v2-contextual-lowercase\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"contextual-new-ontology-v2-contextual-lowercase\"\n\nMore Information needed"
] |
e98986bcfd761d17bca527f411fd53ded469283d
|
# Dataset Card for "Dummy-TinyStories"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
weirdMoonFace/Dummy-TinyStories
|
[
"region:us"
] |
2023-10-13T04:32:01+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13906, "num_examples": 20}, {"name": "validation", "num_bytes": 6798, "num_examples": 10}], "download_size": 21291, "dataset_size": 20704}}
|
2023-10-13T04:32:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Dummy-TinyStories"
More Information needed
|
[
"# Dataset Card for \"Dummy-TinyStories\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Dummy-TinyStories\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Dummy-TinyStories\"\n\nMore Information needed"
] |
aeeccde363153dd8fabccb63ad4a341e727746b3
|
# Dataset Card for "MexLot2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hrangel/MexLot2
|
[
"region:us"
] |
2023-10-13T05:04:04+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": 4162083.0, "num_examples": 33}], "download_size": 4161878, "dataset_size": 4162083.0}}
|
2023-10-13T05:04:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MexLot2"
More Information needed
|
[
"# Dataset Card for \"MexLot2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MexLot2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MexLot2\"\n\nMore Information needed"
] |
8ba996bd4d66022c1cec7c1deedd92afb656bdc5
|
# Dataset Card for Evaluation run of Qwen/Qwen-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Qwen/Qwen-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Qwen__Qwen-7B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-15T05:40:30.047401](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-7B_public/blob/main/results_2023-11-15T05-40-30.047401.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": 0.04163171140939597,
"em_stderr": 0.0020455872163586053,
"f1": 0.0925429949664433,
"f1_stderr": 0.002229779833344395,
"acc": 0.5882484937226726,
"acc_stderr": 0.0131121550768771
},
"harness|drop|3": {
"em": 0.04163171140939597,
"em_stderr": 0.0020455872163586053,
"f1": 0.0925429949664433,
"f1_stderr": 0.002229779833344395
},
"harness|gsm8k|5": {
"acc": 0.4495830174374526,
"acc_stderr": 0.013702290047884744
},
"harness|winogrande|5": {
"acc": 0.7269139700078927,
"acc_stderr": 0.012522020105869457
}
}
```
### 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_Qwen__Qwen-7B
|
[
"region:us"
] |
2023-10-13T05:37:45+00:00
|
{"pretty_name": "Evaluation run of Qwen/Qwen-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Qwen__Qwen-7B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-15T05:40:30.047401](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-7B_public/blob/main/results_2023-11-15T05-40-30.047401.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\": 0.04163171140939597,\n \"em_stderr\": 0.0020455872163586053,\n \"f1\": 0.0925429949664433,\n \"f1_stderr\": 0.002229779833344395,\n \"acc\": 0.5882484937226726,\n \"acc_stderr\": 0.0131121550768771\n },\n \"harness|drop|3\": {\n \"em\": 0.04163171140939597,\n \"em_stderr\": 0.0020455872163586053,\n \"f1\": 0.0925429949664433,\n \"f1_stderr\": 0.002229779833344395\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4495830174374526,\n \"acc_stderr\": 0.013702290047884744\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7269139700078927,\n \"acc_stderr\": 0.012522020105869457\n }\n}\n```", "repo_url": "https://huggingface.co/Qwen/Qwen-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_15T05_40_30.047401", "path": ["**/details_harness|drop|3_2023-11-15T05-40-30.047401.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-15T05-40-30.047401.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_15T05_40_30.047401", "path": ["**/details_harness|gsm8k|5_2023-11-15T05-40-30.047401.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-15T05-40-30.047401.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_15T05_40_30.047401", "path": ["**/details_harness|winogrande|5_2023-11-15T05-40-30.047401.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-15T05-40-30.047401.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_15T05_40_30.047401", "path": ["results_2023-11-15T05-40-30.047401.parquet"]}, {"split": "latest", "path": ["results_2023-11-15T05-40-30.047401.parquet"]}]}]}
|
2023-12-01T14:59:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Qwen/Qwen-7B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Qwen/Qwen-7B on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-15T05:40:30.047401(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 Qwen/Qwen-7B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Qwen/Qwen-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-15T05:40:30.047401(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 Qwen/Qwen-7B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Qwen/Qwen-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-15T05:40:30.047401(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 Qwen/Qwen-7B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Qwen/Qwen-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-15T05:40:30.047401(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"
] |
91bcc8104f6e3808d412a4e16a9cdd78b29da6ab
|
# Dataset Card for "rote-ping-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
anujpaudel/rote-ping-1
|
[
"region:us"
] |
2023-10-13T05:44:22+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": 1620341.0, "num_examples": 31}], "download_size": 1621661, "dataset_size": 1620341.0}}
|
2023-10-14T11:08:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rote-ping-1"
More Information needed
|
[
"# Dataset Card for \"rote-ping-1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rote-ping-1\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rote-ping-1\"\n\nMore Information needed"
] |
8cccb84d56a602b15c615d6852082b18edca1ef4
|
# Dataset Card for Evaluation run of Qwen/Qwen-14B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Qwen/Qwen-14B
- **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 [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Qwen__Qwen-14B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-15T04:46:20.928178](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-14B_public/blob/main/results_2023-11-15T04-46-20.928178.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": 0.20501258389261745,
"em_stderr": 0.0041343766395959035,
"f1": 0.25307885906040445,
"f1_stderr": 0.004059472478555139,
"acc": 0.6788982947905453,
"acc_stderr": 0.012706242404844144
},
"harness|drop|3": {
"em": 0.20501258389261745,
"em_stderr": 0.0041343766395959035,
"f1": 0.25307885906040445,
"f1_stderr": 0.004059472478555139
},
"harness|gsm8k|5": {
"acc": 0.5898407884761183,
"acc_stderr": 0.013548335117860343
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827943
}
}
```
### 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_Qwen__Qwen-14B
|
[
"region:us"
] |
2023-10-13T06:08:03+00:00
|
{"pretty_name": "Evaluation run of Qwen/Qwen-14B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Qwen__Qwen-14B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-15T04:46:20.928178](https://huggingface.co/datasets/open-llm-leaderboard/details_Qwen__Qwen-14B_public/blob/main/results_2023-11-15T04-46-20.928178.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\": 0.20501258389261745,\n \"em_stderr\": 0.0041343766395959035,\n \"f1\": 0.25307885906040445,\n \"f1_stderr\": 0.004059472478555139,\n \"acc\": 0.6788982947905453,\n \"acc_stderr\": 0.012706242404844144\n },\n \"harness|drop|3\": {\n \"em\": 0.20501258389261745,\n \"em_stderr\": 0.0041343766395959035,\n \"f1\": 0.25307885906040445,\n \"f1_stderr\": 0.004059472478555139\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5898407884761183,\n \"acc_stderr\": 0.013548335117860343\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827943\n }\n}\n```", "repo_url": "https://huggingface.co/Qwen/Qwen-14B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_15T04_46_20.928178", "path": ["**/details_harness|drop|3_2023-11-15T04-46-20.928178.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-15T04-46-20.928178.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_15T04_46_20.928178", "path": ["**/details_harness|gsm8k|5_2023-11-15T04-46-20.928178.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-15T04-46-20.928178.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_15T04_46_20.928178", "path": ["**/details_harness|winogrande|5_2023-11-15T04-46-20.928178.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-15T04-46-20.928178.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_15T04_46_20.928178", "path": ["results_2023-11-15T04-46-20.928178.parquet"]}, {"split": "latest", "path": ["results_2023-11-15T04-46-20.928178.parquet"]}]}]}
|
2023-12-01T14:58:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Qwen/Qwen-14B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Qwen/Qwen-14B on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-15T04:46:20.928178(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 Summary\n\nDataset automatically created during the evaluation run of model Qwen/Qwen-14B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-15T04:46:20.928178(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"#### 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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"TAGS\n#region-us \n",
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Qwen/Qwen-14B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-15T04:46:20.928178(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",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Qwen/Qwen-14B## 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 Qwen/Qwen-14B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-15T04:46:20.928178(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"
] |
6a7cad57a0f0c72191b73827d4e6aed2a34e46a7
|
# Dataset Card for "synpre_sort_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/synpre_sort_1M
|
[
"region:us"
] |
2023-10-13T06:18:46+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1946963309, "num_examples": 1000000}, {"name": "validation", "num_bytes": 19526632, "num_examples": 10000}], "download_size": 946911468, "dataset_size": 1966489941}}
|
2023-10-13T06:21:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "synpre_sort_1M"
More Information needed
|
[
"# Dataset Card for \"synpre_sort_1M\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"synpre_sort_1M\"\n\nMore Information needed"
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[
6,
16
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"synpre_sort_1M\"\n\nMore Information needed"
] |
455cd470178a62f569ab2a11388806b784eb2e39
|
# Dataset Card for "synpre_mix_v3_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/synpre_mix_v3_1M
|
[
"region:us"
] |
2023-10-13T06:28:28+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1538674019.8, "num_examples": 1000000}, {"name": "validation", "num_bytes": 15406264.0, "num_examples": 10000}], "download_size": 1018899653, "dataset_size": 1554080283.8}}
|
2023-10-13T06:32:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "synpre_mix_v3_1M"
More Information needed
|
[
"# Dataset Card for \"synpre_mix_v3_1M\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"synpre_mix_v3_1M\"\n\nMore Information needed"
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[
6,
19
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"synpre_mix_v3_1M\"\n\nMore Information needed"
] |
ded6e80b345ea561df94b835217c8559fb9d2ebc
|
# Dataset Card for "FullData"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
centroIA/FullData
|
[
"region:us"
] |
2023-10-13T06:42:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2665390, "num_examples": 968}], "download_size": 687019, "dataset_size": 2665390}}
|
2023-10-13T06:43:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "FullData"
More Information needed
|
[
"# Dataset Card for \"FullData\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"FullData\"\n\nMore Information needed"
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[
6,
12
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"FullData\"\n\nMore Information needed"
] |
9b5ac61d9537b335dcd12e0622a06eee1fe0b662
|
Entries from [ChaiML/20231007_chai_prize_model_feedback_all](https://huggingface.co/datasets/ChaiML/20231007_chai_prize_model_feedback_all) grouped into accept/reject pairs sharing the same bot_id.
Subject to the license of and any restrictions associated with ChaiML/20231007_chai_prize_model_feedback_all.
|
chargoddard/chai-feedback-pairs
|
[
"region:us"
] |
2023-10-13T06:46:10+00:00
|
{"dataset_info": {"features": [{"name": "chosen", "dtype": "string"}, {"name": "chosen_conv_id", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "rejected_conv_id", "dtype": "string"}, {"name": "same_user", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 100941843, "num_examples": 30084}], "download_size": 43602877, "dataset_size": 100941843}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T06:51:45+00:00
|
[] |
[] |
TAGS
#region-us
|
Entries from ChaiML/20231007_chai_prize_model_feedback_all grouped into accept/reject pairs sharing the same bot_id.
Subject to the license of and any restrictions associated with ChaiML/20231007_chai_prize_model_feedback_all.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
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93d65c778925d55973b9ff30c7bccc8545494eb6
|
# Multilingual Jailbreak Challenges in Large Language Models
This repo contains the data for our paper ["Multilingual Jailbreak Challenges in Large Language Models"](https://arxiv.org/abs/2310.06474).
[[Github repo]](https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs/)
## Annotation Statistics
We collected a total of 315 English unsafe prompts and annotated them into nine non-English languages. The languages were categorized based on resource availability, as shown below:
**High-resource languages:** Chinese (zh), Italian (it), Vietnamese (vi)
**Medium-resource languages:** Arabic (ar), Korean (ko), Thai (th)
**Low-resource languages:** Bengali (bn), Swahili (sw), Javanese (jv)
## Ethics Statement
Our research investigates the safety challenges of LLMs in multilingual settings. We are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use. Misuse or harm resulting from the information in this paper is strongly discouraged. To address the identified risks and vulnerabilities, we commit to open-sourcing the data used in our study. This openness aims to facilitate vulnerability identification, encourage discussions, and foster collaborative efforts to enhance LLM safety in multilingual contexts. Furthermore, we have developed the SELF-DEFENSE framework to address multilingual jailbreak challenges in LLMs. This framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios. Overall, our work not only highlights multilingual jailbreak challenges in LLMs but also paves the way for future research, collaboration, and innovation to enhance their safety.
## Citation
```
@misc{deng2023multilingual,
title={Multilingual Jailbreak Challenges in Large Language Models},
author={Yue Deng and Wenxuan Zhang and Sinno Jialin Pan and Lidong Bing},
year={2023},
eprint={2310.06474},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
DAMO-NLP-SG/MultiJail
|
[
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"language:zh",
"language:it",
"language:vi",
"language:ar",
"language:ko",
"language:th",
"language:bn",
"language:sw",
"language:jv",
"license:mit",
"arxiv:2310.06474",
"region:us"
] |
2023-10-13T06:54:21+00:00
|
{"language": ["en", "zh", "it", "vi", "ar", "ko", "th", "bn", "sw", "jv"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["conversational"]}
|
2023-10-13T06:56:04+00:00
|
[
"2310.06474"
] |
[
"en",
"zh",
"it",
"vi",
"ar",
"ko",
"th",
"bn",
"sw",
"jv"
] |
TAGS
#task_categories-conversational #size_categories-n<1K #language-English #language-Chinese #language-Italian #language-Vietnamese #language-Arabic #language-Korean #language-Thai #language-Bengali #language-Swahili (macrolanguage) #language-Javanese #license-mit #arxiv-2310.06474 #region-us
|
# Multilingual Jailbreak Challenges in Large Language Models
This repo contains the data for our paper "Multilingual Jailbreak Challenges in Large Language Models".
[[Github repo]](URL
## Annotation Statistics
We collected a total of 315 English unsafe prompts and annotated them into nine non-English languages. The languages were categorized based on resource availability, as shown below:
High-resource languages: Chinese (zh), Italian (it), Vietnamese (vi)
Medium-resource languages: Arabic (ar), Korean (ko), Thai (th)
Low-resource languages: Bengali (bn), Swahili (sw), Javanese (jv)
## Ethics Statement
Our research investigates the safety challenges of LLMs in multilingual settings. We are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use. Misuse or harm resulting from the information in this paper is strongly discouraged. To address the identified risks and vulnerabilities, we commit to open-sourcing the data used in our study. This openness aims to facilitate vulnerability identification, encourage discussions, and foster collaborative efforts to enhance LLM safety in multilingual contexts. Furthermore, we have developed the SELF-DEFENSE framework to address multilingual jailbreak challenges in LLMs. This framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios. Overall, our work not only highlights multilingual jailbreak challenges in LLMs but also paves the way for future research, collaboration, and innovation to enhance their safety.
|
[
"# Multilingual Jailbreak Challenges in Large Language Models\n\nThis repo contains the data for our paper \"Multilingual Jailbreak Challenges in Large Language Models\".\n[[Github repo]](URL",
"## Annotation Statistics\nWe collected a total of 315 English unsafe prompts and annotated them into nine non-English languages. The languages were categorized based on resource availability, as shown below:\n\nHigh-resource languages: Chinese (zh), Italian (it), Vietnamese (vi)\n\nMedium-resource languages: Arabic (ar), Korean (ko), Thai (th)\n\nLow-resource languages: Bengali (bn), Swahili (sw), Javanese (jv)",
"## Ethics Statement\nOur research investigates the safety challenges of LLMs in multilingual settings. We are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use. Misuse or harm resulting from the information in this paper is strongly discouraged. To address the identified risks and vulnerabilities, we commit to open-sourcing the data used in our study. This openness aims to facilitate vulnerability identification, encourage discussions, and foster collaborative efforts to enhance LLM safety in multilingual contexts. Furthermore, we have developed the SELF-DEFENSE framework to address multilingual jailbreak challenges in LLMs. This framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios. Overall, our work not only highlights multilingual jailbreak challenges in LLMs but also paves the way for future research, collaboration, and innovation to enhance their safety."
] |
[
"TAGS\n#task_categories-conversational #size_categories-n<1K #language-English #language-Chinese #language-Italian #language-Vietnamese #language-Arabic #language-Korean #language-Thai #language-Bengali #language-Swahili (macrolanguage) #language-Javanese #license-mit #arxiv-2310.06474 #region-us \n",
"# Multilingual Jailbreak Challenges in Large Language Models\n\nThis repo contains the data for our paper \"Multilingual Jailbreak Challenges in Large Language Models\".\n[[Github repo]](URL",
"## Annotation Statistics\nWe collected a total of 315 English unsafe prompts and annotated them into nine non-English languages. The languages were categorized based on resource availability, as shown below:\n\nHigh-resource languages: Chinese (zh), Italian (it), Vietnamese (vi)\n\nMedium-resource languages: Arabic (ar), Korean (ko), Thai (th)\n\nLow-resource languages: Bengali (bn), Swahili (sw), Javanese (jv)",
"## Ethics Statement\nOur research investigates the safety challenges of LLMs in multilingual settings. We are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use. Misuse or harm resulting from the information in this paper is strongly discouraged. To address the identified risks and vulnerabilities, we commit to open-sourcing the data used in our study. This openness aims to facilitate vulnerability identification, encourage discussions, and foster collaborative efforts to enhance LLM safety in multilingual contexts. Furthermore, we have developed the SELF-DEFENSE framework to address multilingual jailbreak challenges in LLMs. This framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios. Overall, our work not only highlights multilingual jailbreak challenges in LLMs but also paves the way for future research, collaboration, and innovation to enhance their safety."
] |
[
97,
47,
111,
232
] |
[
"passage: TAGS\n#task_categories-conversational #size_categories-n<1K #language-English #language-Chinese #language-Italian #language-Vietnamese #language-Arabic #language-Korean #language-Thai #language-Bengali #language-Swahili (macrolanguage) #language-Javanese #license-mit #arxiv-2310.06474 #region-us \n# Multilingual Jailbreak Challenges in Large Language Models\n\nThis repo contains the data for our paper \"Multilingual Jailbreak Challenges in Large Language Models\".\n[[Github repo]](URL## Annotation Statistics\nWe collected a total of 315 English unsafe prompts and annotated them into nine non-English languages. The languages were categorized based on resource availability, as shown below:\n\nHigh-resource languages: Chinese (zh), Italian (it), Vietnamese (vi)\n\nMedium-resource languages: Arabic (ar), Korean (ko), Thai (th)\n\nLow-resource languages: Bengali (bn), Swahili (sw), Javanese (jv)## Ethics Statement\nOur research investigates the safety challenges of LLMs in multilingual settings. We are aware of the potential misuse of our findings and emphasize that our research is solely for academic purposes and ethical use. Misuse or harm resulting from the information in this paper is strongly discouraged. To address the identified risks and vulnerabilities, we commit to open-sourcing the data used in our study. This openness aims to facilitate vulnerability identification, encourage discussions, and foster collaborative efforts to enhance LLM safety in multilingual contexts. Furthermore, we have developed the SELF-DEFENSE framework to address multilingual jailbreak challenges in LLMs. This framework automatically generates multilingual safety training data to mitigate risks associated with unintentional and intentional jailbreak scenarios. Overall, our work not only highlights multilingual jailbreak challenges in LLMs but also paves the way for future research, collaboration, and innovation to enhance their safety."
] |
7130a89052bbdb1ee99956da47ced7f40d6c2d47
|
# Dataset Card for "wikicorpus-T5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Back-up/wikicorpus-T5
|
[
"region:us"
] |
2023-10-13T07:15:25+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2787991694.825692, "num_examples": 1118180}], "download_size": 0, "dataset_size": 2787991694.825692}}
|
2023-10-13T07:17:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wikicorpus-T5"
More Information needed
|
[
"# Dataset Card for \"wikicorpus-T5\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wikicorpus-T5\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wikicorpus-T5\"\n\nMore Information needed"
] |
5aad5bc0545ea6d63ffebbb591137b0b8b0844bb
|
Arithmo dataset is prepared as combination of [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), and [lila ood](https://huggingface.co/datasets/allenai/lila/viewer/ood). Refer to [Model Training Data](https://github.com/akjindal53244/Arithmo-Mistral-7B#model-training-data) section in Arithmo-Mistral-7B project GitHub page for more details.
### Support My Work
Building LLMs takes time and resources; if you find my work interesting, your support would be epic!
<a href="https://www.buymeacoffee.com/a_little_learner" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
<h2 id="References">References</h2>
```
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
journal={arXiv preprint arXiv:2309.12284},
year={2023}
}
@article{Yue2023mammoth,
title={MAmmoTH: Building math generalist models through hybrid instruction tuning},
author={Xiang Yue, Xingwei Qu, Ge Zhang, Yao Fu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen},
journal={arXiv preprint arXiv:2309.05653},
year={2023}
}
@article{mishra2022lila,
title={Lila: A unified benchmark for mathematical reasoning},
author={Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and Ashwin Kalyan},
journal={arXiv preprint arXiv:2210.17517},
year={2022}
}
```
|
akjindal53244/Arithmo-Data
|
[
"license:apache-2.0",
"math",
"math-qa",
"region:us"
] |
2023-10-13T07:30:01+00:00
|
{"license": "apache-2.0", "tags": ["math", "math-qa"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "combined_MathInstruct_MetaMathQA_LilaOOD_train.json"}, {"split": "test", "path": "combined_MathInstruct_MetaMathQA_LilaOOD_test.json"}]}]}
|
2024-01-26T09:16:46+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #math #math-qa #region-us
|
Arithmo dataset is prepared as combination of MetaMathQA, MathInstruct, and lila ood. Refer to Model Training Data section in Arithmo-Mistral-7B project GitHub page for more details.
### Support My Work
Building LLMs takes time and resources; if you find my work interesting, your support would be epic!
<a href="URL target="_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
<h2 id="References">References</h2>
|
[
"### Support My Work\n\nBuilding LLMs takes time and resources; if you find my work interesting, your support would be epic!\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>\n\n\n<h2 id=\"References\">References</h2>"
] |
[
"TAGS\n#license-apache-2.0 #math #math-qa #region-us \n",
"### Support My Work\n\nBuilding LLMs takes time and resources; if you find my work interesting, your support would be epic!\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>\n\n\n<h2 id=\"References\">References</h2>"
] |
[
20,
94
] |
[
"passage: TAGS\n#license-apache-2.0 #math #math-qa #region-us \n### Support My Work\n\nBuilding LLMs takes time and resources; if you find my work interesting, your support would be epic!\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>\n\n\n<h2 id=\"References\">References</h2>"
] |
d018b277d2719c9c9a935997fe82858cc57b781a
|
# Dataset Card for "agile_dataset_fusionado"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nalmeida/agile_dataset_fusionado
|
[
"region:us"
] |
2023-10-13T07:59:07+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2665390, "num_examples": 968}], "download_size": 687019, "dataset_size": 2665390}}
|
2023-10-13T07:59:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "agile_dataset_fusionado"
More Information needed
|
[
"# Dataset Card for \"agile_dataset_fusionado\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"agile_dataset_fusionado\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"agile_dataset_fusionado\"\n\nMore Information needed"
] |
8c5a8b6446259ae52e642b5b71516ca791d33c1b
|
## 数据集描述
这是一个基于Agricultural-dataset构建的农业指令跟随数据集。由于Agricultural-dataset是一个比较脏的数据集，并且包含了大量印度相关的内容。所以此数据集也是不干净的。干净版本将会在未来上传。
## Dataset Description
This is an agricultural instruction-following dataset built upon the Agricultural-dataset. Since the Agricultural-dataset is somewhat messy and contains a significant amount of content related to India, this dataset is also not entirely clean. A clean version will be uploaded in the future.
## 构建方法
本数据集使用gpt-3.5-turbo构建
this dataset was created by gpt-3.5-turbo
|
haonanqqq/AgriSFT
|
[
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"license:apache-2.0",
"region:us"
] |
2023-10-13T08:22:21+00:00
|
{"license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "conversational", "text2text-generation", "text-generation"]}
|
2023-10-13T08:34:16+00:00
|
[] |
[] |
TAGS
#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-10K<n<100K #license-apache-2.0 #region-us
|
## 数据集描述
这是一个基于Agricultural-dataset构建的农业指令跟随数据集。由于Agricultural-dataset是一个比较脏的数据集，并且包含了大量印度相关的内容。所以此数据集也是不干净的。干净版本将会在未来上传。
## Dataset Description
This is an agricultural instruction-following dataset built upon the Agricultural-dataset. Since the Agricultural-dataset is somewhat messy and contains a significant amount of content related to India, this dataset is also not entirely clean. A clean version will be uploaded in the future.
## 构建方法
本数据集使用gpt-3.5-turbo构建
this dataset was created by gpt-3.5-turbo
|
[
"## 数据集描述\n这是一个基于Agricultural-dataset构建的农业指令跟随数据集。由于Agricultural-dataset是一个比较脏的数据集，并且包含了大量印度相关的内容。所以此数据集也是不干净的。干净版本将会在未来上传。",
"## Dataset Description\nThis is an agricultural instruction-following dataset built upon the Agricultural-dataset. Since the Agricultural-dataset is somewhat messy and contains a significant amount of content related to India, this dataset is also not entirely clean. A clean version will be uploaded in the future.",
"## 构建方法\n本数据集使用gpt-3.5-turbo构建\n\nthis dataset was created by gpt-3.5-turbo"
] |
[
"TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-10K<n<100K #license-apache-2.0 #region-us \n",
"## 数据集描述\n这是一个基于Agricultural-dataset构建的农业指令跟随数据集。由于Agricultural-dataset是一个比较脏的数据集，并且包含了大量印度相关的内容。所以此数据集也是不干净的。干净版本将会在未来上传。",
"## Dataset Description\nThis is an agricultural instruction-following dataset built upon the Agricultural-dataset. Since the Agricultural-dataset is somewhat messy and contains a significant amount of content related to India, this dataset is also not entirely clean. A clean version will be uploaded in the future.",
"## 构建方法\n本数据集使用gpt-3.5-turbo构建\n\nthis dataset was created by gpt-3.5-turbo"
] |
[
72,
59,
68,
29
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #task_categories-text-generation #size_categories-10K<n<100K #license-apache-2.0 #region-us \n## 数据集描述\n这是一个基于Agricultural-dataset构建的农业指令跟随数据集。由于Agricultural-dataset是一个比较脏的数据集，并且包含了大量印度相关的内容。所以此数据集也是不干净的。干净版本将会在未来上传。## Dataset Description\nThis is an agricultural instruction-following dataset built upon the Agricultural-dataset. Since the Agricultural-dataset is somewhat messy and contains a significant amount of content related to India, this dataset is also not entirely clean. A clean version will be uploaded in the future.## 构建方法\n本数据集使用gpt-3.5-turbo构建\n\nthis dataset was created by gpt-3.5-turbo"
] |
382d181f0e386a1a39d0959660436c5e81ad84a6
|
# Dataset Card for "e73e5059"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/e73e5059
|
[
"region:us"
] |
2023-10-13T08:30:29+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 155, "num_examples": 10}], "download_size": 1318, "dataset_size": 155}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T08:30:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "e73e5059"
More Information needed
|
[
"# Dataset Card for \"e73e5059\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"e73e5059\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"e73e5059\"\n\nMore Information needed"
] |
a493cf4e78b739717f206bf1d888c36373baf455
|
The following dataset has been created using camel-ai, by passing various combinations of user and assistant. The dataset was translated to Hindi using OdiaGenAI English=>Indic translation app.
|
OdiaGenAI/roleplay_hindi
|
[
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:hi",
"code",
"art",
"finance",
"architecture",
"books",
"astronomy",
"acting",
"accounting",
"region:us"
] |
2023-10-13T08:31:45+00:00
|
{"language": ["hi"], "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "conversational"], "tags": ["code", "art", "finance", "architecture", "books", "astronomy", "acting", "accounting"]}
|
2023-10-16T12:22:08+00:00
|
[] |
[
"hi"
] |
TAGS
#task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-Hindi #code #art #finance #architecture #books #astronomy #acting #accounting #region-us
|
The following dataset has been created using camel-ai, by passing various combinations of user and assistant. The dataset was translated to Hindi using OdiaGenAI English=>Indic translation app.
|
[] |
[
"TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-Hindi #code #art #finance #architecture #books #astronomy #acting #accounting #region-us \n"
] |
[
65
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-Hindi #code #art #finance #architecture #books #astronomy #acting #accounting #region-us \n"
] |
faab28bcf3a8e03f6bce4a4e12ee51eb83f08b4d
|
# Dataset Card for Evaluation run of OpenBuddy/openbuddy-openllama-13b-v7-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16
- **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 [OpenBuddy/openbuddy-openllama-13b-v7-fp16](https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_OpenBuddy__openbuddy-openllama-13b-v7-fp16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T17:51:28.265681](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-openllama-13b-v7-fp16/blob/main/results_2023-10-14T17-51-28.265681.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": 0.13496224832214765,
"em_stderr": 0.00349915623734624,
"f1": 0.19493917785234854,
"f1_stderr": 0.0036402036609824453,
"acc": 0.39774068872582313,
"acc_stderr": 0.010563523906790405
},
"harness|drop|3": {
"em": 0.13496224832214765,
"em_stderr": 0.00349915623734624,
"f1": 0.19493917785234854,
"f1_stderr": 0.0036402036609824453
},
"harness|gsm8k|5": {
"acc": 0.09855951478392722,
"acc_stderr": 0.008210320350946331
},
"harness|winogrande|5": {
"acc": 0.696921862667719,
"acc_stderr": 0.012916727462634477
}
}
```
### 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_OpenBuddy__openbuddy-openllama-13b-v7-fp16
|
[
"region:us"
] |
2023-10-13T08:46:56+00:00
|
{"pretty_name": "Evaluation run of OpenBuddy/openbuddy-openllama-13b-v7-fp16", "dataset_summary": "Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-openllama-13b-v7-fp16](https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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_OpenBuddy__openbuddy-openllama-13b-v7-fp16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-14T17:51:28.265681](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-openllama-13b-v7-fp16/blob/main/results_2023-10-14T17-51-28.265681.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\": 0.13496224832214765,\n \"em_stderr\": 0.00349915623734624,\n \"f1\": 0.19493917785234854,\n \"f1_stderr\": 0.0036402036609824453,\n \"acc\": 0.39774068872582313,\n \"acc_stderr\": 0.010563523906790405\n },\n \"harness|drop|3\": {\n \"em\": 0.13496224832214765,\n \"em_stderr\": 0.00349915623734624,\n \"f1\": 0.19493917785234854,\n \"f1_stderr\": 0.0036402036609824453\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09855951478392722,\n \"acc_stderr\": 0.008210320350946331\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.696921862667719,\n \"acc_stderr\": 0.012916727462634477\n }\n}\n```", "repo_url": "https://huggingface.co/OpenBuddy/openbuddy-openllama-13b-v7-fp16", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T09_46_52.076737", "path": ["**/details_harness|drop|3_2023-10-13T09-46-52.076737.parquet"]}, {"split": "2023_10_14T17_51_28.265681", "path": ["**/details_harness|drop|3_2023-10-14T17-51-28.265681.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-14T17-51-28.265681.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T09_46_52.076737", "path": ["**/details_harness|gsm8k|5_2023-10-13T09-46-52.076737.parquet"]}, {"split": "2023_10_14T17_51_28.265681", "path": ["**/details_harness|gsm8k|5_2023-10-14T17-51-28.265681.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-14T17-51-28.265681.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T09_46_52.076737", "path": ["**/details_harness|winogrande|5_2023-10-13T09-46-52.076737.parquet"]}, {"split": "2023_10_14T17_51_28.265681", "path": ["**/details_harness|winogrande|5_2023-10-14T17-51-28.265681.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-14T17-51-28.265681.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T09_46_52.076737", "path": ["results_2023-10-13T09-46-52.076737.parquet"]}, {"split": "2023_10_14T17_51_28.265681", "path": ["results_2023-10-14T17-51-28.265681.parquet"]}, {"split": "latest", "path": ["results_2023-10-14T17-51-28.265681.parquet"]}]}]}
|
2023-10-14T16:51:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of OpenBuddy/openbuddy-openllama-13b-v7-fp16
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model OpenBuddy/openbuddy-openllama-13b-v7-fp16 on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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 2023-10-14T17:51:28.265681(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 OpenBuddy/openbuddy-openllama-13b-v7-fp16",
"## 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 OpenBuddy/openbuddy-openllama-13b-v7-fp16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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 2023-10-14T17:51:28.265681(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 OpenBuddy/openbuddy-openllama-13b-v7-fp16",
"## 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 OpenBuddy/openbuddy-openllama-13b-v7-fp16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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 2023-10-14T17:51:28.265681(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 OpenBuddy/openbuddy-openllama-13b-v7-fp16## 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 OpenBuddy/openbuddy-openllama-13b-v7-fp16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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 2023-10-14T17:51:28.265681(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"
] |
0a034f9ed86d33a3859d9025d3e621cf243773ab
|
# Dataset Card for Earnings 22
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
<!---
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
--->
- [Additional Information](#additional-information)
<!---
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
--->
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [revdotcom Speech Datasets](https://github.com/revdotcom/speech-datasets)
- **Paper:** [Earnings-22: A Practical Benchmark for Accents in the Wild](https://arxiv.org/abs/2203.15591)
- **Point of Contact:** [Miguel Del Rio Fernandez]([email protected])
### Dataset Summary
Earnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.
This dataset contains 125 files totalling roughly 119 hours of English language earnings calls from global countries.
This dataset provides the full audios, transcripts, and accompanying metadata such as ticker symbol, headquarters country,
and our defined "Language Region".
### Supported Tasks and Leaderboards
The dataset is intended to be used to **evaluate** Automatic Speech Recognition (ASR) models.
The model is presented with an long audio file, ranging from several minutes to tens of minutes,
and asked to transcribe the audio file to written text. The most common evaluation metric is the
word error rate (WER), averaged over the 125 audio files.
### Languages
The audio is in English, with speakers from seven different langauge regions and a total of 27 unique countries.
As such, there is large diversity in the speakers and accents.
## Dataset Structure
### Configurations
The Earnings-22 dataset comes in two forms:
* **full**: contains the full audio recordings as single long audio files. Intended for evaluation ASR systems on long-form audio files.
* **chunked**: contains the audio recordings chunked into smaller audio files of maximum 20-seconds. The audio recordings are chunked on punctuation by computing the start/end timestamps for each segment using the [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) model. Intended for evaluation ASR systems on short-form audio files.
### Data Instances
A typical data point comprises the audio input, denoted by the key `audio`, and its transcription, denoted by `transcription.
Some additional information about the speaker, accent and passage which contains the transcription is provided as metadata:
```python
{'audio': {'path': '/fsx/sanchit/speech-datasets/earnings22/media/4468679.mp3',
'array': array([ 0.00000000e+00, -3.36748518e-09, -3.54287222e-09, ...,
4.77626486e-07, -7.80206960e-07, -8.02787653e-07]),
'sampling_rate': 16000},
'file_id': '4468679',
'ticker_symbol': 'PAM',
'country_by_ticker': 'Argentina',
'un_defined': 'Latin America and Caribbean',
'major_dialect_family': 'Other',
'language_family': 'Spanish/Portuguese',
'file_length': '3300',
'sampling_rate': '16000',
'transcription': "Good morning ladies and gentlemen, and thank you for waiting. I'm Margarita Chun from IR, and we would like to welcome everyone to Pampa Energia's Third Quarter 2021 Results Video Conference...
```
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- file_id: unique id of the data sample.
- ticker_symbol: ticker symbol of the company from which the earning call was taken.
- country_by_ticker: country to which the ticker symbol belongs (i.e. where the company is registered).
- un_defined: UN defined language region.
- major_dialect_family: the large-span (major) dialect family to which the country belongs.
- language_family: the Earnings-22 assigned language family. One of seven possible values: African, Asian, English, Germanic, Other Romance, Slavic, Spanish / Portuguese.
- file_length: length of the audio in seconds.
- sampling_rate: sampling rate at which the audio data was saved.
- transcription: the target transcription of the audio file.
### Data Splits
The Earnings-22 dataset is intended to be used as a test-only split for evaluating ASR systems.
As such, only one split is provided: the test split.
<!---
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
--->
### Citation Information
```
@misc{delrio2022earnings22,
title={"Earnings-22: A Practical Benchmark for Accents in the Wild"},
author={Miguel Del Rio and Peter Ha and Quinten McNamara and Corey Miller and Shipra Chandra},
year={2022},
eprint={2203.15591},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@sanchit-gandhi](https://hf.co/sanchit-gandhi) for adding this dataset.
|
distil-whisper/earnings22
|
[
"arxiv:2203.15591",
"region:us"
] |
2023-10-13T08:47:08+00:00
|
{"dataset_info": [{"config_name": "chunked", "features": [{"name": "file_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "segment_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "start_ts", "dtype": "float32"}, {"name": "end_ts", "dtype": "float32"}], "splits": [{"name": "test", "num_bytes": 18651959070.962, "num_examples": 57391}], "download_size": 24019458362, "dataset_size": 18651959070.962}, {"config_name": "full", "features": [{"name": "audio", "dtype": "audio"}, {"name": "file_id", "dtype": "string"}, {"name": "ticker_symbol", "dtype": "string"}, {"name": "country_by_ticker", "dtype": "string"}, {"name": "un_defined", "dtype": "string"}, {"name": "major_dialect_family", "dtype": "string"}, {"name": "language_family", "dtype": "string"}, {"name": "file_length", "dtype": "string"}, {"name": "sampling_rate", "dtype": "string"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1917028403.0, "num_examples": 125}], "download_size": 1892303148, "dataset_size": 1917028403.0}], "configs": [{"config_name": "chunked", "data_files": [{"split": "test", "path": "chunked/test-*"}]}, {"config_name": "full", "data_files": [{"split": "test", "path": "full/test-*"}]}]}
|
2023-10-13T11:00:56+00:00
|
[
"2203.15591"
] |
[] |
TAGS
#arxiv-2203.15591 #region-us
|
# Dataset Card for Earnings 22
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Additional Information
- Contributions
## Dataset Description
- Repository: revdotcom Speech Datasets
- Paper: Earnings-22: A Practical Benchmark for Accents in the Wild
- Point of Contact: Miguel Del Rio Fernandez
### Dataset Summary
Earnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.
This dataset contains 125 files totalling roughly 119 hours of English language earnings calls from global countries.
This dataset provides the full audios, transcripts, and accompanying metadata such as ticker symbol, headquarters country,
and our defined "Language Region".
### Supported Tasks and Leaderboards
The dataset is intended to be used to evaluate Automatic Speech Recognition (ASR) models.
The model is presented with an long audio file, ranging from several minutes to tens of minutes,
and asked to transcribe the audio file to written text. The most common evaluation metric is the
word error rate (WER), averaged over the 125 audio files.
### Languages
The audio is in English, with speakers from seven different langauge regions and a total of 27 unique countries.
As such, there is large diversity in the speakers and accents.
## Dataset Structure
### Configurations
The Earnings-22 dataset comes in two forms:
* full: contains the full audio recordings as single long audio files. Intended for evaluation ASR systems on long-form audio files.
* chunked: contains the audio recordings chunked into smaller audio files of maximum 20-seconds. The audio recordings are chunked on punctuation by computing the start/end timestamps for each segment using the Wav2Vec2 model. Intended for evaluation ASR systems on short-form audio files.
### Data Instances
A typical data point comprises the audio input, denoted by the key 'audio', and its transcription, denoted by 'transcription.
Some additional information about the speaker, accent and passage which contains the transcription is provided as metadata:
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '"audio"' column, *i.e.* 'dataset[0]["audio"]' should always be preferred over 'dataset["audio"][0]'.
- file_id: unique id of the data sample.
- ticker_symbol: ticker symbol of the company from which the earning call was taken.
- country_by_ticker: country to which the ticker symbol belongs (i.e. where the company is registered).
- un_defined: UN defined language region.
- major_dialect_family: the large-span (major) dialect family to which the country belongs.
- language_family: the Earnings-22 assigned language family. One of seven possible values: African, Asian, English, Germanic, Other Romance, Slavic, Spanish / Portuguese.
- file_length: length of the audio in seconds.
- sampling_rate: sampling rate at which the audio data was saved.
- transcription: the target transcription of the audio file.
### Data Splits
The Earnings-22 dataset is intended to be used as a test-only split for evaluating ASR systems.
As such, only one split is provided: the test split.
### Contributions
Thanks to @sanchit-gandhi for adding this dataset.
|
[
"# Dataset Card for Earnings 22",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n\n- Additional Information\n\n - Contributions",
"## Dataset Description\n\n- Repository: revdotcom Speech Datasets\n- Paper: Earnings-22: A Practical Benchmark for Accents in the Wild\n- Point of Contact: Miguel Del Rio Fernandez",
"### Dataset Summary\n\nEarnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.\nThis dataset contains 125 files totalling roughly 119 hours of English language earnings calls from global countries. \nThis dataset provides the full audios, transcripts, and accompanying metadata such as ticker symbol, headquarters country, \nand our defined \"Language Region\".",
"### Supported Tasks and Leaderboards\n\nThe dataset is intended to be used to evaluate Automatic Speech Recognition (ASR) models. \nThe model is presented with an long audio file, ranging from several minutes to tens of minutes, \nand asked to transcribe the audio file to written text. The most common evaluation metric is the \nword error rate (WER), averaged over the 125 audio files.",
"### Languages\n\nThe audio is in English, with speakers from seven different langauge regions and a total of 27 unique countries.\nAs such, there is large diversity in the speakers and accents.",
"## Dataset Structure",
"### Configurations\n\nThe Earnings-22 dataset comes in two forms:\n* full: contains the full audio recordings as single long audio files. Intended for evaluation ASR systems on long-form audio files.\n* chunked: contains the audio recordings chunked into smaller audio files of maximum 20-seconds. The audio recordings are chunked on punctuation by computing the start/end timestamps for each segment using the Wav2Vec2 model. Intended for evaluation ASR systems on short-form audio files.",
"### Data Instances\n\nA typical data point comprises the audio input, denoted by the key 'audio', and its transcription, denoted by 'transcription.\nSome additional information about the speaker, accent and passage which contains the transcription is provided as metadata:",
"### Data Fields\n\n- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n- file_id: unique id of the data sample.\n\n- ticker_symbol: ticker symbol of the company from which the earning call was taken.\n\n- country_by_ticker: country to which the ticker symbol belongs (i.e. where the company is registered).\n\n- un_defined: UN defined language region.\n\n- major_dialect_family: the large-span (major) dialect family to which the country belongs.\n\n- language_family: the Earnings-22 assigned language family. One of seven possible values: African, Asian, English, Germanic, Other Romance, Slavic, Spanish / Portuguese.\n\n- file_length: length of the audio in seconds.\n\n- sampling_rate: sampling rate at which the audio data was saved.\n\n- transcription: the target transcription of the audio file.",
"### Data Splits\n\nThe Earnings-22 dataset is intended to be used as a test-only split for evaluating ASR systems.\nAs such, only one split is provided: the test split.",
"### Contributions\n\nThanks to @sanchit-gandhi for adding this dataset."
] |
[
"TAGS\n#arxiv-2203.15591 #region-us \n",
"# Dataset Card for Earnings 22",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n\n- Additional Information\n\n - Contributions",
"## Dataset Description\n\n- Repository: revdotcom Speech Datasets\n- Paper: Earnings-22: A Practical Benchmark for Accents in the Wild\n- Point of Contact: Miguel Del Rio Fernandez",
"### Dataset Summary\n\nEarnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.\nThis dataset contains 125 files totalling roughly 119 hours of English language earnings calls from global countries. \nThis dataset provides the full audios, transcripts, and accompanying metadata such as ticker symbol, headquarters country, \nand our defined \"Language Region\".",
"### Supported Tasks and Leaderboards\n\nThe dataset is intended to be used to evaluate Automatic Speech Recognition (ASR) models. \nThe model is presented with an long audio file, ranging from several minutes to tens of minutes, \nand asked to transcribe the audio file to written text. The most common evaluation metric is the \nword error rate (WER), averaged over the 125 audio files.",
"### Languages\n\nThe audio is in English, with speakers from seven different langauge regions and a total of 27 unique countries.\nAs such, there is large diversity in the speakers and accents.",
"## Dataset Structure",
"### Configurations\n\nThe Earnings-22 dataset comes in two forms:\n* full: contains the full audio recordings as single long audio files. Intended for evaluation ASR systems on long-form audio files.\n* chunked: contains the audio recordings chunked into smaller audio files of maximum 20-seconds. The audio recordings are chunked on punctuation by computing the start/end timestamps for each segment using the Wav2Vec2 model. Intended for evaluation ASR systems on short-form audio files.",
"### Data Instances\n\nA typical data point comprises the audio input, denoted by the key 'audio', and its transcription, denoted by 'transcription.\nSome additional information about the speaker, accent and passage which contains the transcription is provided as metadata:",
"### Data Fields\n\n- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n- file_id: unique id of the data sample.\n\n- ticker_symbol: ticker symbol of the company from which the earning call was taken.\n\n- country_by_ticker: country to which the ticker symbol belongs (i.e. where the company is registered).\n\n- un_defined: UN defined language region.\n\n- major_dialect_family: the large-span (major) dialect family to which the country belongs.\n\n- language_family: the Earnings-22 assigned language family. One of seven possible values: African, Asian, English, Germanic, Other Romance, Slavic, Spanish / Portuguese.\n\n- file_length: length of the audio in seconds.\n\n- sampling_rate: sampling rate at which the audio data was saved.\n\n- transcription: the target transcription of the audio file.",
"### Data Splits\n\nThe Earnings-22 dataset is intended to be used as a test-only split for evaluating ASR systems.\nAs such, only one split is provided: the test split.",
"### Contributions\n\nThanks to @sanchit-gandhi for adding this dataset."
] |
[
14,
9,
54,
45,
98,
88,
44,
6,
119,
62,
368,
44,
20
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[
"passage: TAGS\n#arxiv-2203.15591 #region-us \n# Dataset Card for Earnings 22## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n\n- Additional Information\n\n - Contributions## Dataset Description\n\n- Repository: revdotcom Speech Datasets\n- Paper: Earnings-22: A Practical Benchmark for Accents in the Wild\n- Point of Contact: Miguel Del Rio Fernandez### Dataset Summary\n\nEarnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.\nThis dataset contains 125 files totalling roughly 119 hours of English language earnings calls from global countries. \nThis dataset provides the full audios, transcripts, and accompanying metadata such as ticker symbol, headquarters country, \nand our defined \"Language Region\".### Supported Tasks and Leaderboards\n\nThe dataset is intended to be used to evaluate Automatic Speech Recognition (ASR) models. \nThe model is presented with an long audio file, ranging from several minutes to tens of minutes, \nand asked to transcribe the audio file to written text. The most common evaluation metric is the \nword error rate (WER), averaged over the 125 audio files.### Languages\n\nThe audio is in English, with speakers from seven different langauge regions and a total of 27 unique countries.\nAs such, there is large diversity in the speakers and accents.## Dataset Structure### Configurations\n\nThe Earnings-22 dataset comes in two forms:\n* full: contains the full audio recordings as single long audio files. Intended for evaluation ASR systems on long-form audio files.\n* chunked: contains the audio recordings chunked into smaller audio files of maximum 20-seconds. The audio recordings are chunked on punctuation by computing the start/end timestamps for each segment using the Wav2Vec2 model. Intended for evaluation ASR systems on short-form audio files."
] |
9ff22c9b94280c6a78c3aad36c52d53a06ab08fd
|
# Dataset Card for "mistral_finetunedata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rageshhf/mistral_finetunedata
|
[
"region:us"
] |
2023-10-13T09:07:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5749970, "num_examples": 3283}], "download_size": 1673257, "dataset_size": 5749970}}
|
2023-10-13T09:07:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mistral_finetunedata"
More Information needed
|
[
"# Dataset Card for \"mistral_finetunedata\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mistral_finetunedata\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mistral_finetunedata\"\n\nMore Information needed"
] |
fe801c80702c9523e2d6c442aad32518426defba
|
# Dataset Card for "EsportLogosV2_processed_V3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
temasarkisov/EsportLogosV2_processed_V3
|
[
"region:us"
] |
2023-10-13T09:13:47+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4563348.0, "num_examples": 73}], "download_size": 4560668, "dataset_size": 4563348.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T09:13:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "EsportLogosV2_processed_V3"
More Information needed
|
[
"# Dataset Card for \"EsportLogosV2_processed_V3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"EsportLogosV2_processed_V3\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"EsportLogosV2_processed_V3\"\n\nMore Information needed"
] |
5956ebd0b2ba89bbff20e6a001415b23b27fd4cb
|
# Dataset Card for "earnings21"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distil-whisper/earnings21
|
[
"region:us"
] |
2023-10-13T09:33:24+00:00
|
{"dataset_info": {"config_name": "full", "features": [{"name": "audio", "dtype": "audio"}, {"name": "file_id", "dtype": "string"}, {"name": "audio_length", "dtype": "string"}, {"name": "sample_rate", "dtype": "string"}, {"name": "company_name", "dtype": "string"}, {"name": "financial_quarter", "dtype": "string"}, {"name": "sector", "dtype": "string"}, {"name": "speaker_switches", "dtype": "string"}, {"name": "unique_speakers", "dtype": "string"}, {"name": "curator_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 778199575.0, "num_examples": 44}], "download_size": 772949298, "dataset_size": 778199575.0}, "configs": [{"config_name": "full", "data_files": [{"split": "test", "path": "full/test-*"}]}]}
|
2023-10-13T09:33:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "earnings21"
More Information needed
|
[
"# Dataset Card for \"earnings21\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"earnings21\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"earnings21\"\n\nMore Information needed"
] |
42cca7d5e22e455f75492279b55a80a598740322
|
# Dataset Card for "PubmedSumm_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hippocrates/PubmedSumm_test
|
[
"region:us"
] |
2023-10-13T09:51:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2293491121, "num_examples": 119924}, {"name": "valid", "num_bytes": 129680450, "num_examples": 6633}, {"name": "test", "num_bytes": 129463253, "num_examples": 6658}], "download_size": 1172343963, "dataset_size": 2552634824}}
|
2023-10-17T18:54:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "PubmedSumm_test"
More Information needed
|
[
"# Dataset Card for \"PubmedSumm_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"PubmedSumm_test\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"PubmedSumm_test\"\n\nMore Information needed"
] |
077ba1fc0b164529eff9b8f89aa2f82413ffdab1
|
# Dataset Card for "PubmedSumm_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hippocrates/PubmedSumm_train
|
[
"region:us"
] |
2023-10-13T09:53:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4379163643, "num_examples": 117108}, {"name": "valid", "num_bytes": 248192252, "num_examples": 6631}, {"name": "test", "num_bytes": 247621605, "num_examples": 6658}], "download_size": 2185677927, "dataset_size": 4874977500}}
|
2023-12-05T16:12:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "PubmedSumm_train"
More Information needed
|
[
"# Dataset Card for \"PubmedSumm_train\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"PubmedSumm_train\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"PubmedSumm_train\"\n\nMore Information needed"
] |
558e9df47b4ef3652fcd050a04c70860a449001e
|
# Dataset Card for "reports-kor-43"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bh8648/reports-kor-43
|
[
"region:us"
] |
2023-10-13T10:12:53+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "page_num", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 14623911, "num_examples": 4244}], "download_size": 7186966, "dataset_size": 14623911}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T10:12:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "reports-kor-43"
More Information needed
|
[
"# Dataset Card for \"reports-kor-43\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"reports-kor-43\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"reports-kor-43\"\n\nMore Information needed"
] |
9e25fd87155c0c601da6ae3709e326433e7503cf
|
# Dataset Card for "churn_prediction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
paul-w-qs/churn_prediction
|
[
"region:us"
] |
2023-10-13T10:13:26+00:00
|
{"dataset_info": {"features": [{"name": "SALESFORCEACCOUNTID", "dtype": "string"}, {"name": "ACCOUNTING_MONTH", "dtype": "string"}, {"name": "CHURN", "dtype": "int64"}, {"name": "DOWNSELL", "dtype": "int64"}, {"name": "RENEWAL_MONTH", "dtype": "string"}, {"name": "CHURN_SUM", "dtype": "float64"}, {"name": "DOWNSELL_SUM", "dtype": "float64"}, {"name": "CONTRACT_START_DATE", "dtype": "string"}, {"name": "CONTRACT_END_DATE", "dtype": "string"}, {"name": "REGION", "dtype": "string"}, {"name": "TENURE_MONTHS", "dtype": "int64"}, {"name": "MONTHS_UNTIL_EVENT", "dtype": "int64"}, {"name": "DNB_GLOBAL_SALES_REVENUE", "dtype": "float64"}, {"name": "DNB_GLOBAL_EMPLOYEE_COUNT", "dtype": "int64"}, {"name": "DETECTEDSEATSCOUNT", "dtype": "float64"}, {"name": "PRODUCT_ONE", "dtype": "int64"}, {"name": "NUM_PRODUCTS_DAYS_LATE_PREV_90", "dtype": "int64"}, {"name": "LICENSINGSPECIALIST_CHANGE", "dtype": "int64"}, {"name": "CAR_HEALTH_CHECK", "dtype": "int64"}, {"name": "CROSS_SELL_RECENCY", "dtype": "int64"}, {"name": "SEATS_DOWNSELL_RECENCY", "dtype": "int64"}, {"name": "PRODUCT_TWO", "dtype": "int64"}, {"name": "PCT_PRODUCT_THREE_ENABLED", "dtype": "int64"}, {"name": "OTHER", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_ONE", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_TWO", "dtype": "int64"}, {"name": "PCT_PRODUCT_FOUR_ENABLED", "dtype": "int64"}, {"name": "PRODUCT_FIVE", "dtype": "int64"}, {"name": "PCT_PRODUCT_FIVE_ENABLED", "dtype": "int64"}, {"name": "MAX_SUPPORT_CASE_DAYSTOCLOSE", "dtype": "int64"}, {"name": "SUM_P4FLAG", "dtype": "int64"}, {"name": "PRODUCT_SIX", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_THREE", "dtype": "int64"}, {"name": "PRODUCT_THREE", "dtype": "int64"}, {"name": "SALESREP_CHANGE", "dtype": "int64"}, {"name": "SURVEY_AVG_CXI_SCORE", "dtype": "float64"}, {"name": "PCT_PRODUCT_FOUR_BEST_PRACTICE", "dtype": "int64"}, {"name": "EO_ATTENDED", "dtype": "int64"}, {"name": "PRODUCT_SEVEN", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_FOUR", "dtype": "int64"}, {"name": "SURVEY_AVG_NPS_SCORE", "dtype": "float64"}, {"name": "CUSTOMER_BEHAVIOUR_FIVE", "dtype": "int64"}, {"name": "PCT_PRODUCT_TWO_ENABLED", "dtype": "int64"}, {"name": "PCT_PRODUCT_SIX_ENABLED", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_SIX", "dtype": "int64"}, {"name": "PRODUCT_EIGHT", "dtype": "int64"}, {"name": "PRODUCT_NINE", "dtype": "int64"}, {"name": "PRODUCT_TEN", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_SEVEN", "dtype": "int64"}, {"name": "PRODUCT_ELEVEN", "dtype": "int64"}, {"name": "PRODUCT_TWELVE", "dtype": "int64"}, {"name": "PRODUCT_THIRTEEN", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_EIGHT", "dtype": "int64"}, {"name": "PRODUCT_FOURTEEN", "dtype": "int64"}, {"name": "PRODUCT_FIFTEEN", "dtype": "int64"}, {"name": "PRODUCT_DOWNSELL_RECENCY", "dtype": "int64"}, {"name": "UPSELLMANAGER_CHANGE", "dtype": "int64"}, {"name": "MAX_SUPPORT_CASE_TIMETOFIRSTRESPONSE", "dtype": "int64"}, {"name": "SURVEY_AVG_CASE_MOOD_SCORE", "dtype": "float64"}, {"name": "PCT_PRODUCT_THREE_BEST_PRACTICE", "dtype": "int64"}, {"name": "CASES_CREATED_FIRST_30_AFTER_IMPLEMENTATION", "dtype": "int64"}, {"name": "PRODUCT_FOUR", "dtype": "int64"}, {"name": "PCT_PRODUCT_TWO_BEST_PRACTICE", "dtype": "int64"}, {"name": "IMPLEMENTATION_MONTHS_RUNNING_TOTAL", "dtype": "int64"}, {"name": "CAR_CHURN_OR_RISK_DISCUSSION", "dtype": "int64"}, {"name": "PRODUCT_SIXTEEN", "dtype": "int64"}, {"name": "PRODUCT_SEVENTEEN", "dtype": "int64"}, {"name": "RATIO_SEATS_ACTIVE", "dtype": "float64"}, {"name": "MONTHLY_PRODUCT_COUNT", "dtype": "int64"}, {"name": "ARR", "dtype": "float64"}, {"name": "SUPPORT_CASE_NUMBEROFSLABREACHES", "dtype": "int64"}, {"name": "AVG_SEATS", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_NINE", "dtype": "int64"}, {"name": "CONTRACT_LENGTH", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_TEN", "dtype": "int64"}, {"name": "PRODUCT_EIGHTEEN", "dtype": "int64"}, {"name": "CUSTOMER_BEHAVIOUR_ELEVEN", "dtype": "int64"}, {"name": "SEATS_UPSELL_RECENCY", "dtype": "int64"}, {"name": "PRODUCT_NINETEEN", "dtype": "int64"}, {"name": "ACCOUNTMANAGER_CHANGE", "dtype": "int64"}, {"name": "PCT_PRODUCT_NINE_ENABLED", "dtype": "int64"}, {"name": "PRODUCT_TWENTY", "dtype": "int64"}, {"name": "PRODUCT_TWENTYONE", "dtype": "int64"}, {"name": "PRODUCT_TWENTYTWO", "dtype": "int64"}, {"name": "PRODUCT_TWENTYTHREE", "dtype": "int64"}, {"name": "SUM_P0FLAG", "dtype": "int64"}, {"name": "SUM_P1FLAG", "dtype": "int64"}, {"name": "SUM_P2FLAG", "dtype": "int64"}, {"name": "SUM_P3FLAG", "dtype": "int64"}, {"name": "BACKLOG", "dtype": "int64"}, {"name": "AVG_SUPPORT_CASE_PRIORITY_SCORE", "dtype": "float64"}, {"name": "COMPETITOR_SEATS", "dtype": "int64"}, {"name": "RPU", "dtype": "float64"}, {"name": "SECTOR", "dtype": "string"}, {"name": "P0123_FLAGS", "dtype": "int64"}, {"name": "ARR_DIV_SEATS", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 85464346, "num_examples": 100000}], "download_size": 7102198, "dataset_size": 85464346}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T10:13:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "churn_prediction"
More Information needed
|
[
"# Dataset Card for \"churn_prediction\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"churn_prediction\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"churn_prediction\"\n\nMore Information needed"
] |
1e4016a9185a8a541422d51d02a1a05190d06583
|
## Dataset Description
Story Books for 180 ISO-639-3 codes.
A Machine Translation (MT) version of this dataset is also provided in [cis-lmu/GlotStoryBook-MT](https://huggingface.co/datasets/cis-lmu/GlotStoryBook-MT).
This dataset consisted of 4 publishers:
1. asp: [African Storybook](https://africanstorybook.org)
2. pb: [Pratham Books](https://prathambooks.org/)
3. lcb: [Little Cree Books](http://littlecreebooks.com/)
4. lida: [LIDA Stories](https://lidastories.net/)
- **GitHub Repository:** [github](https://github.com/cisnlp/GlotStoryBook)
- **Paper:** [paper](https://arxiv.org/abs/2310.16248)
- **Point of Contact:** [email protected]
## Usage (HF Loader)
```python
from datasets import load_dataset
dataset = load_dataset('cis-lmu/GlotStoryBook')
print(dataset['train'][0]) # First row data
```
## Download
If you are not a fan of the HF dataloader, download it directly:
```python
! wget https://huggingface.co/datasets/cis-lmu/GlotStoryBook/resolve/main/GlotStoryBook.csv
```
# Tools
To compute the script of each text we used Glotscript ([code](https://github.com/cisnlp/GlotScript) and [paper](https://arxiv.org/abs/2309.13320)).
## License and Copyright
We do not own any of the text from which these data has been extracted.
All the files are collected from the repository located at https://github.com/global-asp/.
The source repository for each text and file is stored in the dataset.
Each file in the dataset is associated with one license from the CC family.
The licenses include 'CC BY', 'CC BY-NC', 'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'.
We also license the code, actual packaging and the metadata of these data under the cc0-1.0.
## Github
We additionally provide a GitHub version that openly shares the source code for processing this dataset:
https://github.com/cisnlp/GlotStoryBook
## Citation
If you use any part of this code and data in your research, please cite it (along with https://github.com/global-asp/) using the following BibTeX entry.
This work is part of the [GlotLID](https://github.com/cisnlp/GlotLID) project.
```
@inproceedings{
kargaran2023glotlid,
title={{GlotLID}: Language Identification for Low-Resource Languages},
author={Kargaran, Amir Hossein and Imani, Ayyoob and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=dl4e3EBz5j}
}
```
|
cis-lmu/GlotStoryBook
|
[
"language:ach",
"language:ada",
"language:adh",
"language:adx",
"language:aeb",
"language:afr",
"language:alz",
"language:amh",
"language:anu",
"language:ara",
"language:asm",
"language:bem",
"language:ben",
"language:bod",
"language:bxk",
"language:cat",
"language:cce",
"language:ckb",
"language:crk",
"language:csw",
"language:ctu",
"language:dag",
"language:dan",
"language:deu",
"language:dga",
"language:din",
"language:dje",
"language:ell",
"language:eng",
"language:epo",
"language:ewe",
"language:fas",
"language:fat",
"language:fra",
"language:ful",
"language:gaa",
"language:gjn",
"language:guj",
"language:gur",
"language:guz",
"language:gyn",
"language:hat",
"language:hau",
"language:hbs",
"language:hch",
"language:her",
"language:hin",
"language:hun",
"language:hus",
"language:ind",
"language:ita",
"language:jam",
"language:jpn",
"language:kam",
"language:kan",
"language:kau",
"language:kdj",
"language:keo",
"language:khg",
"language:khm",
"language:kik",
"language:kin",
"language:kln",
"language:kmr",
"language:kok",
"language:koo",
"language:kor",
"language:kpz",
"language:kqn",
"language:kri",
"language:kru",
"language:ktz",
"language:kua",
"language:kwn",
"language:laj",
"language:lat",
"language:lgg",
"language:lin",
"language:lit",
"language:lko",
"language:loz",
"language:lsm",
"language:luc",
"language:lue",
"language:lug",
"language:lun",
"language:luo",
"language:lwg",
"language:mal",
"language:mar",
"language:mas",
"language:mat",
"language:maz",
"language:mer",
"language:mfe",
"language:mhi",
"language:mhw",
"language:miu",
"language:mlg",
"language:mmc",
"language:mnw",
"language:mqu",
"language:msa",
"language:mya",
"language:myx",
"language:naq",
"language:nbl",
"language:nch",
"language:ndo",
"language:nep",
"language:nhe",
"language:nhw",
"language:nld",
"language:nle",
"language:nno",
"language:nob",
"language:nor",
"language:nso",
"language:nuj",
"language:nya",
"language:nyn",
"language:nyu",
"language:nzi",
"language:ocu",
"language:old",
"language:ori",
"language:orm",
"language:pan",
"language:pcm",
"language:pmq",
"language:pol",
"language:por",
"language:prs",
"language:pus",
"language:rki",
"language:ron",
"language:rus",
"language:sag",
"language:san",
"language:saq",
"language:sck",
"language:sme",
"language:som",
"language:sot",
"language:spa",
"language:sqi",
"language:srp",
"language:ssw",
"language:swa",
"language:swe",
"language:tam",
"language:tel",
"language:teo",
"language:tet",
"language:tgl",
"language:tha",
"language:tir",
"language:toh",
"language:toi",
"language:tsc",
"language:tsn",
"language:tso",
"language:ttj",
"language:tum",
"language:tur",
"language:tuv",
"language:twi",
"language:ukr",
"language:urd",
"language:ven",
"language:vie",
"language:xho",
"language:xog",
"language:xsm",
"language:yor",
"language:yua",
"language:yue",
"language:zho",
"language:zne",
"language:zul",
"license:cc",
"storybook",
"book",
"story",
"language-identification",
"arxiv:2310.16248",
"arxiv:2309.13320",
"region:us"
] |
2023-10-13T10:13:40+00:00
|
{"language": ["ach", "ada", "adh", "adx", "aeb", "afr", "alz", "amh", "anu", "ara", "asm", "bem", "ben", "bod", "bxk", "cat", "cce", "ckb", "crk", "csw", "ctu", "dag", "dan", "deu", "dga", "din", "dje", "ell", "eng", "epo", "ewe", "fas", "fat", "fra", "ful", "gaa", "gjn", "guj", "gur", "guz", "gyn", "hat", "hau", "hbs", "hch", "her", "hin", "hun", "hus", "ind", "ita", "jam", "jpn", "kam", "kan", "kau", "kdj", "keo", "khg", "khm", "kik", "kin", "kln", "kmr", "kok", "koo", "kor", "kpz", "kqn", "kri", "kru", "ktz", "kua", "kwn", "laj", "lat", "lgg", "lin", "lit", "lko", "loz", "lsm", "luc", "lue", "lug", "lun", "luo", "lwg", "mal", "mar", "mas", "mat", "maz", "mer", "mfe", "mhi", "mhw", "miu", "mlg", "mmc", "mnw", "mqu", "msa", "mya", "myx", "naq", "nbl", "nch", "ndo", "nep", "nhe", "nhw", "nld", "nle", "nno", "nob", "nor", "nso", "nuj", "nya", "nyn", "nyu", "nzi", "ocu", "old", "ori", "orm", "pan", "pcm", "pmq", "pol", "por", "prs", "pus", "rki", "ron", "rus", "sag", "san", "saq", "sck", "sme", "som", "sot", "spa", "sqi", "srp", "ssw", "swa", "swe", "tam", "tel", "teo", "tet", "tgl", "tha", "tir", "toh", "toi", "tsc", "tsn", "tso", "ttj", "tum", "tur", "tuv", "twi", "ukr", "urd", "ven", "vie", "xho", "xog", "xsm", "yor", "yua", "yue", "zho", "zne", "zul"], "license": "cc", "pretty_name": "GlotStoryBook Corpus", "tags": ["storybook", "book", "story", "language-identification"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "GlotStoryBook.csv"}]}]}
|
2024-01-26T19:24:51+00:00
|
[
"2310.16248",
"2309.13320"
] |
[
"ach",
"ada",
"adh",
"adx",
"aeb",
"afr",
"alz",
"amh",
"anu",
"ara",
"asm",
"bem",
"ben",
"bod",
"bxk",
"cat",
"cce",
"ckb",
"crk",
"csw",
"ctu",
"dag",
"dan",
"deu",
"dga",
"din",
"dje",
"ell",
"eng",
"epo",
"ewe",
"fas",
"fat",
"fra",
"ful",
"gaa",
"gjn",
"guj",
"gur",
"guz",
"gyn",
"hat",
"hau",
"hbs",
"hch",
"her",
"hin",
"hun",
"hus",
"ind",
"ita",
"jam",
"jpn",
"kam",
"kan",
"kau",
"kdj",
"keo",
"khg",
"khm",
"kik",
"kin",
"kln",
"kmr",
"kok",
"koo",
"kor",
"kpz",
"kqn",
"kri",
"kru",
"ktz",
"kua",
"kwn",
"laj",
"lat",
"lgg",
"lin",
"lit",
"lko",
"loz",
"lsm",
"luc",
"lue",
"lug",
"lun",
"luo",
"lwg",
"mal",
"mar",
"mas",
"mat",
"maz",
"mer",
"mfe",
"mhi",
"mhw",
"miu",
"mlg",
"mmc",
"mnw",
"mqu",
"msa",
"mya",
"myx",
"naq",
"nbl",
"nch",
"ndo",
"nep",
"nhe",
"nhw",
"nld",
"nle",
"nno",
"nob",
"nor",
"nso",
"nuj",
"nya",
"nyn",
"nyu",
"nzi",
"ocu",
"old",
"ori",
"orm",
"pan",
"pcm",
"pmq",
"pol",
"por",
"prs",
"pus",
"rki",
"ron",
"rus",
"sag",
"san",
"saq",
"sck",
"sme",
"som",
"sot",
"spa",
"sqi",
"srp",
"ssw",
"swa",
"swe",
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"tel",
"teo",
"tet",
"tgl",
"tha",
"tir",
"toh",
"toi",
"tsc",
"tsn",
"tso",
"ttj",
"tum",
"tur",
"tuv",
"twi",
"ukr",
"urd",
"ven",
"vie",
"xho",
"xog",
"xsm",
"yor",
"yua",
"yue",
"zho",
"zne",
"zul"
] |
TAGS
#language-Acoli #language-Adangme #language-Adhola #language-Amdo Tibetan #language-Tunisian Arabic #language-Afrikaans #language-Alur #language-Amharic #language-Anuak #language-Arabic #language-Assamese #language-Bemba (Zambia) #language-Bengali #language-Tibetan #language-Bukusu #language-Catalan #language-Chopi #language-Central Kurdish #language-Plains Cree #language-Swampy Cree #language-Chol #language-Dagbani #language-Danish #language-German #language-Southern Dagaare #language-Dinka #language-Zarma #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Ewe #language-Persian #language-Fanti #language-French #language-Fulah #language-Ga #language-Gonja #language-Gujarati #language-Farefare #language-Gusii #language-Guyanese Creole English #language-Haitian #language-Hausa #language-Serbo-Croatian #language-Huichol #language-Herero #language-Hindi #language-Hungarian #language-Huastec #language-Indonesian #language-Italian #language-Jamaican Creole English #language-Japanese #language-Kamba (Kenya) #language-Kannada #language-Kanuri #language-Karamojong #language-Kakwa #language-Khams Tibetan #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kalenjin #language-Northern Kurdish #language-Konkani (macrolanguage) #language-Konzo #language-Korean #language-Kupsabiny #language-Kaonde #language-Krio #language-Kurukh #language-Juǀʼhoan #language-Kuanyama #language-Kwangali #language-Lango (Uganda) #language-Latin #language-Lugbara #language-Lingala #language-Lithuanian #language-Khayo #language-Lozi #language-Saamia #language-Aringa #language-Luvale #language-Ganda #language-Lunda #language-Luo (Kenya and Tanzania) #language-Wanga #language-Malayalam #language-Marathi #language-Masai #language-San Francisco Matlatzinca #language-Central Mazahua #language-Meru #language-Morisyen #language-Ma'di #language-Mbukushu #language-Cacaloxtepec Mixtec #language-Malagasy #language-Michoacán Mazahua #language-Mon #language-Mandari #language-Malay (macrolanguage) #language-Burmese #language-Masaaba #language-Khoekhoe #language-South Ndebele #language-Central Huasteca Nahuatl #language-Ndonga #language-Nepali (macrolanguage) #language-Eastern Huasteca Nahuatl #language-Western Huasteca Nahuatl #language-Dutch #language-East Nyala #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Norwegian #language-Pedi #language-Nyole #language-Nyanja #language-Nyankole #language-Nyungwe #language-Nzima #language-Atzingo Matlatzinca #language-Mochi #language-Oriya (macrolanguage) #language-Oromo #language-Panjabi #language-Nigerian Pidgin #language-Northern Pame #language-Polish #language-Portuguese #language-Dari #language-Pushto #language-Rakhine #language-Romanian #language-Russian #language-Sango #language-Sanskrit #language-Samburu #language-Sadri #language-Northern Sami #language-Somali #language-Southern Sotho #language-Spanish #language-Albanian #language-Serbian #language-Swati #language-Swahili (macrolanguage) #language-Swedish #language-Tamil #language-Telugu #language-Teso #language-Tetum #language-Tagalog #language-Thai #language-Tigrinya #language-Gitonga #language-Tonga (Zambia) #language-Tswa #language-Tswana #language-Tsonga #language-Tooro #language-Tumbuka #language-Turkish #language-Turkana #language-Twi #language-Ukrainian #language-Urdu #language-Venda #language-Vietnamese #language-Xhosa #language-Soga #language-Kasem #language-Yoruba #language-Yucateco #language-Yue Chinese #language-Chinese #language-Zande (individual language) #language-Zulu #license-cc #storybook #book #story #language-identification #arxiv-2310.16248 #arxiv-2309.13320 #region-us
|
## Dataset Description
Story Books for 180 ISO-639-3 codes.
A Machine Translation (MT) version of this dataset is also provided in cis-lmu/GlotStoryBook-MT.
This dataset consisted of 4 publishers:
1. asp: African Storybook
2. pb: Pratham Books
3. lcb: Little Cree Books
4. lida: LIDA Stories
- GitHub Repository: github
- Paper: paper
- Point of Contact: amir@URL
## Usage (HF Loader)
## Download
If you are not a fan of the HF dataloader, download it directly:
# Tools
To compute the script of each text we used Glotscript (code and paper).
## License and Copyright
We do not own any of the text from which these data has been extracted.
All the files are collected from the repository located at URL
The source repository for each text and file is stored in the dataset.
Each file in the dataset is associated with one license from the CC family.
The licenses include 'CC BY', 'CC BY-NC', 'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'.
We also license the code, actual packaging and the metadata of these data under the cc0-1.0.
## Github
We additionally provide a GitHub version that openly shares the source code for processing this dataset:
URL
If you use any part of this code and data in your research, please cite it (along with URL using the following BibTeX entry.
This work is part of the GlotLID project.
|
[
"## Dataset Description\n\nStory Books for 180 ISO-639-3 codes.\nA Machine Translation (MT) version of this dataset is also provided in cis-lmu/GlotStoryBook-MT.\n\nThis dataset consisted of 4 publishers:\n1. asp: African Storybook\n2. pb: Pratham Books\n3. lcb: Little Cree Books\n4. lida: LIDA Stories\n\n\n- GitHub Repository: github\n- Paper: paper\n- Point of Contact: amir@URL",
"## Usage (HF Loader)",
"## Download\nIf you are not a fan of the HF dataloader, download it directly:",
"# Tools\n\nTo compute the script of each text we used Glotscript (code and paper).",
"## License and Copyright\nWe do not own any of the text from which these data has been extracted.\nAll the files are collected from the repository located at URL\nThe source repository for each text and file is stored in the dataset.\nEach file in the dataset is associated with one license from the CC family.\nThe licenses include 'CC BY', 'CC BY-NC', 'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'.\nWe also license the code, actual packaging and the metadata of these data under the cc0-1.0.",
"## Github\nWe additionally provide a GitHub version that openly shares the source code for processing this dataset:\nURL\n\n\nIf you use any part of this code and data in your research, please cite it (along with URL using the following BibTeX entry.\nThis work is part of the GlotLID project."
] |
[
"TAGS\n#language-Acoli #language-Adangme #language-Adhola #language-Amdo Tibetan #language-Tunisian Arabic #language-Afrikaans #language-Alur #language-Amharic #language-Anuak #language-Arabic #language-Assamese #language-Bemba (Zambia) #language-Bengali #language-Tibetan #language-Bukusu #language-Catalan #language-Chopi #language-Central Kurdish #language-Plains Cree #language-Swampy Cree #language-Chol #language-Dagbani #language-Danish #language-German #language-Southern Dagaare #language-Dinka #language-Zarma #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Ewe #language-Persian #language-Fanti #language-French #language-Fulah #language-Ga #language-Gonja #language-Gujarati #language-Farefare #language-Gusii #language-Guyanese Creole English #language-Haitian #language-Hausa #language-Serbo-Croatian #language-Huichol #language-Herero #language-Hindi #language-Hungarian #language-Huastec #language-Indonesian #language-Italian #language-Jamaican Creole English #language-Japanese #language-Kamba (Kenya) #language-Kannada #language-Kanuri #language-Karamojong #language-Kakwa #language-Khams Tibetan #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kalenjin #language-Northern Kurdish #language-Konkani (macrolanguage) #language-Konzo #language-Korean #language-Kupsabiny #language-Kaonde #language-Krio #language-Kurukh #language-Juǀʼhoan #language-Kuanyama #language-Kwangali #language-Lango (Uganda) #language-Latin #language-Lugbara #language-Lingala #language-Lithuanian #language-Khayo #language-Lozi #language-Saamia #language-Aringa #language-Luvale #language-Ganda #language-Lunda #language-Luo (Kenya and Tanzania) #language-Wanga #language-Malayalam #language-Marathi #language-Masai #language-San Francisco Matlatzinca #language-Central Mazahua #language-Meru #language-Morisyen #language-Ma'di #language-Mbukushu #language-Cacaloxtepec Mixtec #language-Malagasy #language-Michoacán Mazahua #language-Mon #language-Mandari #language-Malay (macrolanguage) #language-Burmese #language-Masaaba #language-Khoekhoe #language-South Ndebele #language-Central Huasteca Nahuatl #language-Ndonga #language-Nepali (macrolanguage) #language-Eastern Huasteca Nahuatl #language-Western Huasteca Nahuatl #language-Dutch #language-East Nyala #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Norwegian #language-Pedi #language-Nyole #language-Nyanja #language-Nyankole #language-Nyungwe #language-Nzima #language-Atzingo Matlatzinca #language-Mochi #language-Oriya (macrolanguage) #language-Oromo #language-Panjabi #language-Nigerian Pidgin #language-Northern Pame #language-Polish #language-Portuguese #language-Dari #language-Pushto #language-Rakhine #language-Romanian #language-Russian #language-Sango #language-Sanskrit #language-Samburu #language-Sadri #language-Northern Sami #language-Somali #language-Southern Sotho #language-Spanish #language-Albanian #language-Serbian #language-Swati #language-Swahili (macrolanguage) #language-Swedish #language-Tamil #language-Telugu #language-Teso #language-Tetum #language-Tagalog #language-Thai #language-Tigrinya #language-Gitonga #language-Tonga (Zambia) #language-Tswa #language-Tswana #language-Tsonga #language-Tooro #language-Tumbuka #language-Turkish #language-Turkana #language-Twi #language-Ukrainian #language-Urdu #language-Venda #language-Vietnamese #language-Xhosa #language-Soga #language-Kasem #language-Yoruba #language-Yucateco #language-Yue Chinese #language-Chinese #language-Zande (individual language) #language-Zulu #license-cc #storybook #book #story #language-identification #arxiv-2310.16248 #arxiv-2309.13320 #region-us \n",
"## Dataset Description\n\nStory Books for 180 ISO-639-3 codes.\nA Machine Translation (MT) version of this dataset is also provided in cis-lmu/GlotStoryBook-MT.\n\nThis dataset consisted of 4 publishers:\n1. asp: African Storybook\n2. pb: Pratham Books\n3. lcb: Little Cree Books\n4. lida: LIDA Stories\n\n\n- GitHub Repository: github\n- Paper: paper\n- Point of Contact: amir@URL",
"## Usage (HF Loader)",
"## Download\nIf you are not a fan of the HF dataloader, download it directly:",
"# Tools\n\nTo compute the script of each text we used Glotscript (code and paper).",
"## License and Copyright\nWe do not own any of the text from which these data has been extracted.\nAll the files are collected from the repository located at URL\nThe source repository for each text and file is stored in the dataset.\nEach file in the dataset is associated with one license from the CC family.\nThe licenses include 'CC BY', 'CC BY-NC', 'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'.\nWe also license the code, actual packaging and the metadata of these data under the cc0-1.0.",
"## Github\nWe additionally provide a GitHub version that openly shares the source code for processing this dataset:\nURL\n\n\nIf you use any part of this code and data in your research, please cite it (along with URL using the following BibTeX entry.\nThis work is part of the GlotLID project."
] |
[
1154,
107,
8,
20,
20,
137,
72
] |
[
"passage: "
] |
0c768fc002dca59265473bfdb22e7e84b95967be
|
# Dataset Card for "synth_code_preference_4k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pvduy/synth_code_preference_4k
|
[
"region:us"
] |
2023-10-13T10:17:19+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "selected", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14409668, "num_examples": 4052}], "download_size": 3223970, "dataset_size": 14409668}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T10:17:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "synth_code_preference_4k"
More Information needed
|
[
"# Dataset Card for \"synth_code_preference_4k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"synth_code_preference_4k\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"synth_code_preference_4k\"\n\nMore Information needed"
] |
efccf16ed2897a8d2de035f8c68c48ff1f38bf82
|
# Dataset Card for NCT-CRC-HE
### Dataset Summary
The NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer.
### Data Splits
The dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below
### Initial Data Collection and Normalization
NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization.
### Licensing Information
CC-BY-SA
### Citation Information
Owkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF.
[Link to original paper](https://zenodo.org/records/1214456)
|
owkin/nct-crc-he
|
[
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"biology",
"medical",
"cancer",
"colorectal cancer",
"region:us"
] |
2023-10-13T10:31:07+00:00
|
{"language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "pretty_name": "NCT_CRC", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ADI", "1": "BACK", "2": "DEB", "3": "LYM", "4": "MUC", "5": "MUS", "6": "NORM", "7": "STR", "8": "TUM"}}}}], "splits": [{"name": "nct_crc_he_100", "num_bytes": 15058006, "num_examples": 99}, {"name": "nct_crc_he_1k", "num_bytes": 151950686, "num_examples": 999}, {"name": "crc_val_he_7k", "num_bytes": 1092855241.74, "num_examples": 7180}], "download_size": 1095677324, "dataset_size": 1259863933.74}, "configs": [{"config_name": "default", "data_files": [{"split": "nct_crc_he_100", "path": "data/nct_crc_he_100-*"}, {"split": "nct_crc_he_1k", "path": "data/nct_crc_he_1k-*"}, {"split": "crc_val_he_7k", "path": "data/crc_val_he_7k-*"}]}], "tags": ["biology", "medical", "cancer", "colorectal cancer"]}
|
2023-10-26T08:42:47+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-3.0 #biology #medical #cancer #colorectal cancer #region-us
|
# Dataset Card for NCT-CRC-HE
### Dataset Summary
The NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer.
### Data Splits
The dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below
!Tissue examples
### Initial Data Collection and Normalization
NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization.
### Licensing Information
CC-BY-SA
Owkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF.
Link to original paper
|
[
"# Dataset Card for NCT-CRC-HE",
"### Dataset Summary\n\nThe NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer.",
"### Data Splits\n\nThe dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below\n\n!Tissue examples",
"### Initial Data Collection and Normalization\n\nNCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization.",
"### Licensing Information\n\nCC-BY-SA\n\n\n\nOwkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF. \n\nLink to original paper"
] |
[
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-3.0 #biology #medical #cancer #colorectal cancer #region-us \n",
"# Dataset Card for NCT-CRC-HE",
"### Dataset Summary\n\nThe NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer.",
"### Data Splits\n\nThe dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below\n\n!Tissue examples",
"### Initial Data Collection and Normalization\n\nNCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization.",
"### Licensing Information\n\nCC-BY-SA\n\n\n\nOwkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF. \n\nLink to original paper"
] |
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"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-3.0 #biology #medical #cancer #colorectal cancer #region-us \n# Dataset Card for NCT-CRC-HE### Dataset Summary\n\nThe NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer.### Data Splits\n\nThe dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below\n\n!Tissue examples### Initial Data Collection and Normalization\n\nNCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization.### Licensing Information\n\nCC-BY-SA\n\n\n\nOwkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF. \n\nLink to original paper"
] |
6aacdfce8f41a2802319b9e0967003244ac55e87
|
## model_setting_name: platy
## max_context_length: 512
## icl_examples: 5
## icl_dataset_name: lukaemon/mmlu
## max_documents_per_subject: 1000000
## max_contexts_per_subject: 1000000
## icl_use_out_options: True
## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all
## subjects: SUB_10
## response_template: 1
## inverse_template: 0
|
ostapeno/platy_icl5_maxD1000000_maxC1000000_prmt10_1
|
[
"region:us"
] |
2023-10-13T10:32:02+00:00
|
{}
|
2023-10-13T10:32:12+00:00
|
[] |
[] |
TAGS
#region-us
|
## model_setting_name: platy
## max_context_length: 512
## icl_examples: 5
## icl_dataset_name: lukaemon/mmlu
## max_documents_per_subject: 1000000
## max_contexts_per_subject: 1000000
## icl_use_out_options: True
## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all
## subjects: SUB_10
## response_template: 1
## inverse_template: 0
|
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"## max_contexts_per_subject: 1000000",
"## icl_use_out_options: True",
"## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all",
"## subjects: SUB_10",
"## response_template: 1",
"## inverse_template: 0"
] |
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"## model_setting_name: platy",
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"## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all",
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"passage: TAGS\n#region-us \n## model_setting_name: platy## max_context_length: 512## icl_examples: 5## icl_dataset_name: lukaemon/mmlu## max_documents_per_subject: 1000000## max_contexts_per_subject: 1000000## icl_use_out_options: True## seed_dataset: sordonia/my-wiki-latex_mmlu_from_valid_all## subjects: SUB_10## response_template: 1## inverse_template: 0"
] |
abd86dcc7d9d9b127db1ba7a56572fd46bf6c843
|
# Dataset Card for "abstracts_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Eitanli/abstracts_cleaned
|
[
"region:us"
] |
2023-10-13T10:43:34+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "valid", "path": "data/valid-*"}]}], "dataset_info": {"features": [{"name": "recall", "dtype": "int64"}, {"name": "article_title", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 137515873.22056717, "num_examples": 79863}, {"name": "test", "num_bytes": 17189699.389716417, "num_examples": 9983}, {"name": "valid", "num_bytes": 17189699.389716417, "num_examples": 9983}], "download_size": 92795013, "dataset_size": 171895272.0}}
|
2023-10-14T10:37:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "abstracts_cleaned"
More Information needed
|
[
"# Dataset Card for \"abstracts_cleaned\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"abstracts_cleaned\"\n\nMore Information needed"
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[
6,
18
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"abstracts_cleaned\"\n\nMore Information needed"
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9c835847b471a6c7228365a34561b2fd0e62bdce
|
# Dataset Card for "repo_A"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sazirarrwth99/repo_A
|
[
"region:us"
] |
2023-10-13T10:47:00+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "lex", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "eid", "dtype": "int64"}, {"name": "original_triple_sets", "dtype": "string"}, {"name": "modified_triple_sets", "dtype": "string"}, {"name": "shape", "dtype": "string"}, {"name": "shape_type", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "test_category", "dtype": "string"}, {"name": "dbpedia_links", "dtype": "string"}, {"name": "links", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20930652, "num_examples": 22839}], "download_size": 8712641, "dataset_size": 20930652}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2024-02-02T07:21:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "repo_A"
More Information needed
|
[
"# Dataset Card for \"repo_A\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"repo_A\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"repo_A\"\n\nMore Information needed"
] |
fee755b6c6ea5822c4768a48ea1b51a7a802dc28
|
# Dataset Card for Evaluation run of YeungNLP/firefly-bloom-2b6-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/YeungNLP/firefly-bloom-2b6-v2
- **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 [YeungNLP/firefly-bloom-2b6-v2](https://huggingface.co/YeungNLP/firefly-bloom-2b6-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T11:51:41.999066](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2/blob/main/results_2023-10-13T11-51-41.999066.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": 0.08630453020134228,
"em_stderr": 0.002875790094905939,
"f1": 0.1275723573825503,
"f1_stderr": 0.00310355978869451,
"acc": 0.2825940222825524,
"acc_stderr": 0.008796871542302145
},
"harness|drop|3": {
"em": 0.08630453020134228,
"em_stderr": 0.002875790094905939,
"f1": 0.1275723573825503,
"f1_stderr": 0.00310355978869451
},
"harness|gsm8k|5": {
"acc": 0.017437452615617893,
"acc_stderr": 0.003605486867998265
},
"harness|winogrande|5": {
"acc": 0.5477505919494869,
"acc_stderr": 0.013988256216606024
}
}
```
### 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_YeungNLP__firefly-bloom-2b6-v2
|
[
"region:us"
] |
2023-10-13T10:51:46+00:00
|
{"pretty_name": "Evaluation run of YeungNLP/firefly-bloom-2b6-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [YeungNLP/firefly-bloom-2b6-v2](https://huggingface.co/YeungNLP/firefly-bloom-2b6-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T11:51:41.999066](https://huggingface.co/datasets/open-llm-leaderboard/details_YeungNLP__firefly-bloom-2b6-v2/blob/main/results_2023-10-13T11-51-41.999066.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\": 0.08630453020134228,\n \"em_stderr\": 0.002875790094905939,\n \"f1\": 0.1275723573825503,\n \"f1_stderr\": 0.00310355978869451,\n \"acc\": 0.2825940222825524,\n \"acc_stderr\": 0.008796871542302145\n },\n \"harness|drop|3\": {\n \"em\": 0.08630453020134228,\n \"em_stderr\": 0.002875790094905939,\n \"f1\": 0.1275723573825503,\n \"f1_stderr\": 0.00310355978869451\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.017437452615617893,\n \"acc_stderr\": 0.003605486867998265\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5477505919494869,\n \"acc_stderr\": 0.013988256216606024\n }\n}\n```", "repo_url": "https://huggingface.co/YeungNLP/firefly-bloom-2b6-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T11_51_41.999066", "path": ["**/details_harness|drop|3_2023-10-13T11-51-41.999066.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-13T11-51-41.999066.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T11_51_41.999066", "path": ["**/details_harness|gsm8k|5_2023-10-13T11-51-41.999066.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-13T11-51-41.999066.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T11_51_41.999066", "path": ["**/details_harness|winogrande|5_2023-10-13T11-51-41.999066.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-13T11-51-41.999066.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T11_51_41.999066", "path": ["results_2023-10-13T11-51-41.999066.parquet"]}, {"split": "latest", "path": ["results_2023-10-13T11-51-41.999066.parquet"]}]}]}
|
2023-10-13T10:51:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of YeungNLP/firefly-bloom-2b6-v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model YeungNLP/firefly-bloom-2b6-v2 on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-13T11:51:41.999066(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 YeungNLP/firefly-bloom-2b6-v2",
"## 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 YeungNLP/firefly-bloom-2b6-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T11:51:41.999066(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",
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"### Data Splits",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of YeungNLP/firefly-bloom-2b6-v2",
"## 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 YeungNLP/firefly-bloom-2b6-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T11:51:41.999066(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
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] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of YeungNLP/firefly-bloom-2b6-v2## 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 YeungNLP/firefly-bloom-2b6-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-13T11:51:41.999066(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"
] |
10f60b265a7eea430c510253f62a604d2e80125f
|
# Dataset Card for Evaluation run of LLMs/Stable-Vicuna-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/LLMs/Stable-Vicuna-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 [LLMs/Stable-Vicuna-13B](https://huggingface.co/LLMs/Stable-Vicuna-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-13T11:51:54.162285](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B/blob/main/results_2023-10-13T11-51-54.162285.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": 0.012688758389261746,
"em_stderr": 0.0011462418380586343,
"f1": 0.06941170302013412,
"f1_stderr": 0.0017195070383295536,
"acc": 0.2849250197316496,
"acc_stderr": 0.006957342547358349
},
"harness|drop|3": {
"em": 0.012688758389261746,
"em_stderr": 0.0011462418380586343,
"f1": 0.06941170302013412,
"f1_stderr": 0.0017195070383295536
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5698500394632992,
"acc_stderr": 0.013914685094716698
}
}
```
### 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_LLMs__Stable-Vicuna-13B
|
[
"region:us"
] |
2023-10-13T10:51:58+00:00
|
{"pretty_name": "Evaluation run of LLMs/Stable-Vicuna-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [LLMs/Stable-Vicuna-13B](https://huggingface.co/LLMs/Stable-Vicuna-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T11:51:54.162285](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B/blob/main/results_2023-10-13T11-51-54.162285.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\": 0.012688758389261746,\n \"em_stderr\": 0.0011462418380586343,\n \"f1\": 0.06941170302013412,\n \"f1_stderr\": 0.0017195070383295536,\n \"acc\": 0.2849250197316496,\n \"acc_stderr\": 0.006957342547358349\n },\n \"harness|drop|3\": {\n \"em\": 0.012688758389261746,\n \"em_stderr\": 0.0011462418380586343,\n \"f1\": 0.06941170302013412,\n \"f1_stderr\": 0.0017195070383295536\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5698500394632992,\n \"acc_stderr\": 0.013914685094716698\n }\n}\n```", "repo_url": "https://huggingface.co/LLMs/Stable-Vicuna-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_13T11_51_54.162285", "path": ["**/details_harness|drop|3_2023-10-13T11-51-54.162285.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-13T11-51-54.162285.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_13T11_51_54.162285", "path": ["**/details_harness|gsm8k|5_2023-10-13T11-51-54.162285.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-13T11-51-54.162285.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_13T11_51_54.162285", "path": ["**/details_harness|winogrande|5_2023-10-13T11-51-54.162285.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-13T11-51-54.162285.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_13T11_51_54.162285", "path": ["results_2023-10-13T11-51-54.162285.parquet"]}, {"split": "latest", "path": ["results_2023-10-13T11-51-54.162285.parquet"]}]}]}
|
2023-10-13T10:52:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of LLMs/Stable-Vicuna-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 LLMs/Stable-Vicuna-13B on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-13T11:51:54.162285(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 LLMs/Stable-Vicuna-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 LLMs/Stable-Vicuna-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T11:51:54.162285(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 LLMs/Stable-Vicuna-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 LLMs/Stable-Vicuna-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-13T11:51:54.162285(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",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
21,
31,
169,
67,
10,
4,
6,
6,
5,
5,
5,
7,
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9,
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5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of LLMs/Stable-Vicuna-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 LLMs/Stable-Vicuna-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-13T11:51:54.162285(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"
] |
7a5a14ab60f504530b1185528a78c0d2456dfa1e
|
# Dataset Card for "70k_evol_code_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pvduy/70k_evol_code_prompts
|
[
"region:us"
] |
2023-10-13T11:05:19+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31492387, "num_examples": 70000}], "download_size": 16308713, "dataset_size": 31492387}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T11:05:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "70k_evol_code_prompts"
More Information needed
|
[
"# Dataset Card for \"70k_evol_code_prompts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"70k_evol_code_prompts\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"70k_evol_code_prompts\"\n\nMore Information needed"
] |
95ed6618d3df7644b5758f0683546ad295efc109
|
# Dataset Card for "fourthbrain_synthetic_marketmail"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
renatomoulin/fourthbrain_synthetic_marketmail
|
[
"region:us"
] |
2023-10-13T11:51:43+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20890, "num_examples": 10}], "download_size": 26786, "dataset_size": 20890}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-14T12:57:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fourthbrain_synthetic_marketmail"
More Information needed
|
[
"# Dataset Card for \"fourthbrain_synthetic_marketmail\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fourthbrain_synthetic_marketmail\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fourthbrain_synthetic_marketmail\"\n\nMore Information needed"
] |
a911509d09060d582ecb87973d4bfebb2a17bbb7
|
# Dataset Card for "nq_open-halM"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
erbacher/nq_open-halM
|
[
"region:us"
] |
2023-10-13T12:14:48+00:00
|
{"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "gold_generation", "sequence": "string"}, {"name": "target", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "results", "dtype": "string"}, {"name": "em", "dtype": "float64"}, {"name": "hal_m", "dtype": "string"}], "splits": [{"name": "train1", "num_bytes": 20868789.5, "num_examples": 39584}, {"name": "train2", "num_bytes": 20868789.5, "num_examples": 39584}, {"name": "dev", "num_bytes": 4612579, "num_examples": 8757}, {"name": "test", "num_bytes": 1950822, "num_examples": 3610}], "download_size": 13134688, "dataset_size": 48300980.0}}
|
2023-10-13T12:15:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "nq_open-halM"
More Information needed
|
[
"# Dataset Card for \"nq_open-halM\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"nq_open-halM\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"nq_open-halM\"\n\nMore Information needed"
] |
e1f4e660a89bdc69dfe684573060d058e5cd6225
|
# Dataset Card for "trivia_qa-halM"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
erbacher/trivia_qa-halM
|
[
"region:us"
] |
2023-10-13T12:16:05+00:00
|
{"dataset_info": {"features": [{"name": "target", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "gold_generation", "sequence": "string"}, {"name": "text", "dtype": "string"}, {"name": "results", "dtype": "string"}, {"name": "em", "dtype": "float64"}, {"name": "hal_m", "dtype": "string"}], "splits": [{"name": "train1", "num_bytes": 36799502.40639716, "num_examples": 39392}, {"name": "train2", "num_bytes": 36800436.59360284, "num_examples": 39393}, {"name": "dev", "num_bytes": 8307250, "num_examples": 8837}, {"name": "test", "num_bytes": 10650305, "num_examples": 11313}], "download_size": 34799920, "dataset_size": 92557494.0}}
|
2023-10-13T12:16:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trivia_qa-halM"
More Information needed
|
[
"# Dataset Card for \"trivia_qa-halM\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trivia_qa-halM\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"trivia_qa-halM\"\n\nMore Information needed"
] |
cebd2d32f728fb4225ded534106fef10ca1711fd
|
# Dataset Card for "dolly-15k-chinese-zhtw"
## 內容
dolly-15k-chinese-zhtw 是一個開源數據集，它的原始數據集 [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) 包含由數千名 Databricks 員工產生的指令追蹤記錄，涉及 [InstructGPT](https://arxiv.org/abs/2203.02155) 論文中概述的幾個行為類別，包括腦力激盪、分類、封閉式QA、生成、資訊擷取、開放式QA 和總結。
根據以下條款，該資料集可用於任何目的，無論是學術目的還是商業目的 [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode)。
## 支援的任務
- 訓練 LLMs
- 合成數據的生成
- 數據增強
## 概述
databricks-dolly-15k 是由數千名 Databricks 員工產生的超過 15,000 筆記錄的語料庫，使大型語言模型能夠展現 ChatGPT 的神奇互動性。 Databricks 員工被邀請在八個不同的指令類別中的每一個類別中建立提示/回應對，其中包括 InstructGPT 論文中概述的七個類別，以及開放式自由格式類別。貢獻者被指示避免使用除維基百科（針對指令類別的特定子集）之外的網絡上任何來源的信息，並明確指示避免在製定指令或響應時使用生成式人工智能。提供了每種行為的範例，以激發適合每個類別的問題類型和說明。
在資料生成過程的中間，貢獻者可以選擇回答其他貢獻者提出的問題。他們被要求重新表述原來的問題，並且只選擇他們可以合理地預期正確回答的問題。
對於某些類別，貢獻者被要求提供從維基百科複製的參考文本。參考文本（由實際資料集中的上下文欄位指示）可能包含括號內的維基百科引用編號（例如[42]），我們建議使用者在下游應用程式中將其刪除。
## 範例
一個樣本的範例:
```
{
'instruction': '小森田智昭是什麼時候出生的？',
'context': '小森田出生於1981年7月10日，出生在熊本縣。高中畢業後，他於2000年加入了J1聯賽俱樂部Avispa...',
'response': '小森田智明出生於1981年7月10日。'
}
```
## 資料欄位
資料有幾個欄位：
- `instruction`: 描述模型應該執行的任務
- `context`: 任務內容的上下文
- `response`: 回應
## 已知限制
- 維基百科是一個眾包語料庫，該資料集的內容可能反映維基百科中發現的偏見、事實錯誤和主題焦點
- 註釋者人口統計和主題可能反映 Databricks 員工的組成
## 論文引用
```
@online{DatabricksBlog2023DollyV2,
author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
urldate = {2023-06-30}
}
```
## 許可資訊
資料集中的某些類別的資料包括來自以下來源的資料，並根據 CC BY-SA 3.0 授權：
- 維基百科 - https://www.wikipedia.org
|
erhwenkuo/dolly-15k-chinese-zhtw
|
[
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-sa-3.0",
"arxiv:2203.02155",
"region:us"
] |
2023-10-13T13:10:46+00:00
|
{"language": ["zh"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "summarization"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10483730, "num_examples": 15011}], "download_size": 7492947, "dataset_size": 10483730}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T13:32:29+00:00
|
[
"2203.02155"
] |
[
"zh"
] |
TAGS
#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Chinese #license-cc-by-sa-3.0 #arxiv-2203.02155 #region-us
|
# Dataset Card for "dolly-15k-chinese-zhtw"
## 內容
dolly-15k-chinese-zhtw 是一個開源數據集，它的原始數據集 databricks-dolly-15k 包含由數千名 Databricks 員工產生的指令追蹤記錄，涉及 InstructGPT 論文中概述的幾個行為類別，包括腦力激盪、分類、封閉式QA、生成、資訊擷取、開放式QA 和總結。
根據以下條款，該資料集可用於任何目的，無論是學術目的還是商業目的 Creative Commons Attribution-ShareAlike 3.0 Unported License。
## 支援的任務
- 訓練 LLMs
- 合成數據的生成
- 數據增強
## 概述
databricks-dolly-15k 是由數千名 Databricks 員工產生的超過 15,000 筆記錄的語料庫，使大型語言模型能夠展現 ChatGPT 的神奇互動性。 Databricks 員工被邀請在八個不同的指令類別中的每一個類別中建立提示/回應對，其中包括 InstructGPT 論文中概述的七個類別，以及開放式自由格式類別。貢獻者被指示避免使用除維基百科（針對指令類別的特定子集）之外的網絡上任何來源的信息，並明確指示避免在製定指令或響應時使用生成式人工智能。提供了每種行為的範例，以激發適合每個類別的問題類型和說明。
在資料生成過程的中間，貢獻者可以選擇回答其他貢獻者提出的問題。他們被要求重新表述原來的問題，並且只選擇他們可以合理地預期正確回答的問題。
對於某些類別，貢獻者被要求提供從維基百科複製的參考文本。參考文本（由實際資料集中的上下文欄位指示）可能包含括號內的維基百科引用編號（例如[42]），我們建議使用者在下游應用程式中將其刪除。
## 範例
一個樣本的範例:
## 資料欄位
資料有幾個欄位：
- 'instruction': 描述模型應該執行的任務
- 'context': 任務內容的上下文
- 'response': 回應
## 已知限制
- 維基百科是一個眾包語料庫，該資料集的內容可能反映維基百科中發現的偏見、事實錯誤和主題焦點
- 註釋者人口統計和主題可能反映 Databricks 員工的組成
## 論文引用
## 許可資訊
資料集中的某些類別的資料包括來自以下來源的資料，並根據 CC BY-SA 3.0 授權：
- 維基百科 - URL
|
[
"# Dataset Card for \"dolly-15k-chinese-zhtw\"",
"## 內容\n\ndolly-15k-chinese-zhtw 是一個開源數據集，它的原始數據集 databricks-dolly-15k 包含由數千名 Databricks 員工產生的指令追蹤記錄，涉及 InstructGPT 論文中概述的幾個行為類別，包括腦力激盪、分類、封閉式QA、生成、資訊擷取、開放式QA 和總結。\n\n根據以下條款，該資料集可用於任何目的，無論是學術目的還是商業目的 Creative Commons Attribution-ShareAlike 3.0 Unported License。",
"## 支援的任務\n\n- 訓練 LLMs\n- 合成數據的生成\n- 數據增強",
"## 概述\n\ndatabricks-dolly-15k 是由數千名 Databricks 員工產生的超過 15,000 筆記錄的語料庫，使大型語言模型能夠展現 ChatGPT 的神奇互動性。 Databricks 員工被邀請在八個不同的指令類別中的每一個類別中建立提示/回應對，其中包括 InstructGPT 論文中概述的七個類別，以及開放式自由格式類別。貢獻者被指示避免使用除維基百科（針對指令類別的特定子集）之外的網絡上任何來源的信息，並明確指示避免在製定指令或響應時使用生成式人工智能。提供了每種行為的範例，以激發適合每個類別的問題類型和說明。\n\n在資料生成過程的中間，貢獻者可以選擇回答其他貢獻者提出的問題。他們被要求重新表述原來的問題，並且只選擇他們可以合理地預期正確回答的問題。\n\n對於某些類別，貢獻者被要求提供從維基百科複製的參考文本。參考文本（由實際資料集中的上下文欄位指示）可能包含括號內的維基百科引用編號（例如[42]），我們建議使用者在下游應用程式中將其刪除。",
"## 範例\n\n一個樣本的範例:",
"## 資料欄位\n\n資料有幾個欄位：\n\n- 'instruction': 描述模型應該執行的任務\n- 'context': 任務內容的上下文\n- 'response': 回應",
"## 已知限制\n\n- 維基百科是一個眾包語料庫，該資料集的內容可能反映維基百科中發現的偏見、事實錯誤和主題焦點\n- 註釋者人口統計和主題可能反映 Databricks 員工的組成",
"## 論文引用",
"## 許可資訊\n\n資料集中的某些類別的資料包括來自以下來源的資料，並根據 CC BY-SA 3.0 授權：\n\n- 維基百科 - URL"
] |
[
"TAGS\n#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Chinese #license-cc-by-sa-3.0 #arxiv-2203.02155 #region-us \n",
"# Dataset Card for \"dolly-15k-chinese-zhtw\"",
"## 內容\n\ndolly-15k-chinese-zhtw 是一個開源數據集，它的原始數據集 databricks-dolly-15k 包含由數千名 Databricks 員工產生的指令追蹤記錄，涉及 InstructGPT 論文中概述的幾個行為類別，包括腦力激盪、分類、封閉式QA、生成、資訊擷取、開放式QA 和總結。\n\n根據以下條款，該資料集可用於任何目的，無論是學術目的還是商業目的 Creative Commons Attribution-ShareAlike 3.0 Unported License。",
"## 支援的任務\n\n- 訓練 LLMs\n- 合成數據的生成\n- 數據增強",
"## 概述\n\ndatabricks-dolly-15k 是由數千名 Databricks 員工產生的超過 15,000 筆記錄的語料庫，使大型語言模型能夠展現 ChatGPT 的神奇互動性。 Databricks 員工被邀請在八個不同的指令類別中的每一個類別中建立提示/回應對，其中包括 InstructGPT 論文中概述的七個類別，以及開放式自由格式類別。貢獻者被指示避免使用除維基百科（針對指令類別的特定子集）之外的網絡上任何來源的信息，並明確指示避免在製定指令或響應時使用生成式人工智能。提供了每種行為的範例，以激發適合每個類別的問題類型和說明。\n\n在資料生成過程的中間，貢獻者可以選擇回答其他貢獻者提出的問題。他們被要求重新表述原來的問題，並且只選擇他們可以合理地預期正確回答的問題。\n\n對於某些類別，貢獻者被要求提供從維基百科複製的參考文本。參考文本（由實際資料集中的上下文欄位指示）可能包含括號內的維基百科引用編號（例如[42]），我們建議使用者在下游應用程式中將其刪除。",
"## 範例\n\n一個樣本的範例:",
"## 資料欄位\n\n資料有幾個欄位：\n\n- 'instruction': 描述模型應該執行的任務\n- 'context': 任務內容的上下文\n- 'response': 回應",
"## 已知限制\n\n- 維基百科是一個眾包語料庫，該資料集的內容可能反映維基百科中發現的偏見、事實錯誤和主題焦點\n- 註釋者人口統計和主題可能反映 Databricks 員工的組成",
"## 論文引用",
"## 許可資訊\n\n資料集中的某些類別的資料包括來自以下來源的資料，並根據 CC BY-SA 3.0 授權：\n\n- 維基百科 - URL"
] |
[
64,
17,
118,
21,
258,
12,
44,
55,
4,
36
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Chinese #license-cc-by-sa-3.0 #arxiv-2203.02155 #region-us \n# Dataset Card for \"dolly-15k-chinese-zhtw\"## 內容\n\ndolly-15k-chinese-zhtw 是一個開源數據集，它的原始數據集 databricks-dolly-15k 包含由數千名 Databricks 員工產生的指令追蹤記錄，涉及 InstructGPT 論文中概述的幾個行為類別，包括腦力激盪、分類、封閉式QA、生成、資訊擷取、開放式QA 和總結。\n\n根據以下條款，該資料集可用於任何目的，無論是學術目的還是商業目的 Creative Commons Attribution-ShareAlike 3.0 Unported License。## 支援的任務\n\n- 訓練 LLMs\n- 合成數據的生成\n- 數據增強## 概述\n\ndatabricks-dolly-15k 是由數千名 Databricks 員工產生的超過 15,000 筆記錄的語料庫，使大型語言模型能夠展現 ChatGPT 的神奇互動性。 Databricks 員工被邀請在八個不同的指令類別中的每一個類別中建立提示/回應對，其中包括 InstructGPT 論文中概述的七個類別，以及開放式自由格式類別。貢獻者被指示避免使用除維基百科（針對指令類別的特定子集）之外的網絡上任何來源的信息，並明確指示避免在製定指令或響應時使用生成式人工智能。提供了每種行為的範例，以激發適合每個類別的問題類型和說明。\n\n在資料生成過程的中間，貢獻者可以選擇回答其他貢獻者提出的問題。他們被要求重新表述原來的問題，並且只選擇他們可以合理地預期正確回答的問題。\n\n對於某些類別，貢獻者被要求提供從維基百科複製的參考文本。參考文本（由實際資料集中的上下文欄位指示）可能包含括號內的維基百科引用編號（例如[42]），我們建議使用者在下游應用程式中將其刪除。## 範例\n\n一個樣本的範例:"
] |
ba28a08a980a2a07f1b5f68656a3d2672615a338
|
This dataset contains addresses and sentences pairs, where the sentence contains the address. For instance, `"4450 WEST 32ND STREET": "Lena walked up the path to the white colonial-style house with the blue shutters and addressed the letter to Mr. and Mrs. Morrison at 4450 West 32nd Street."` I prompted the quantized version of Llama-2 to generate the sentences.
|
piazzola/addressWithContext
|
[
"language:en",
"license:cc-by-nc-2.0",
"region:us"
] |
2023-10-13T13:14:36+00:00
|
{"language": ["en"], "license": "cc-by-nc-2.0"}
|
2023-10-13T17:18:55+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-cc-by-nc-2.0 #region-us
|
This dataset contains addresses and sentences pairs, where the sentence contains the address. For instance, '"4450 WEST 32ND STREET": "Lena walked up the path to the white colonial-style house with the blue shutters and addressed the letter to Mr. and Mrs. Morrison at 4450 West 32nd Street."' I prompted the quantized version of Llama-2 to generate the sentences.
|
[] |
[
"TAGS\n#language-English #license-cc-by-nc-2.0 #region-us \n"
] |
[
21
] |
[
"passage: TAGS\n#language-English #license-cc-by-nc-2.0 #region-us \n"
] |
0d5b69aaa70577e98baef6af6646a8e692b7f55a
|
# Dataset Card for "guanaco-llama2-200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
OSK-Creative-Tech/guanaco-llama2-200
|
[
"region:us"
] |
2023-10-13T13:21:53+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 338808, "num_examples": 200}], "download_size": 201257, "dataset_size": 338808}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T13:21:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-200"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-200\"\n\nMore Information needed"
] |
32c325da59d37092da501c2f514e81f8c8465dad
|
The following dataset has been created using camel-ai, by passing various combinations of user and assistant. The dataset was translated to Odia using OdiaGenAI English=>Indic translation app.
|
OdiaGenAI/roleplay_odia
|
[
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:or",
"code",
"art",
"finance",
"architecture",
"books",
"astronomy",
"acting",
"accounting",
"region:us"
] |
2023-10-13T13:58:35+00:00
|
{"language": ["or"], "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "conversational"], "tags": ["code", "art", "finance", "architecture", "books", "astronomy", "acting", "accounting"]}
|
2023-10-16T12:19:44+00:00
|
[] |
[
"or"
] |
TAGS
#task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-Oriya (macrolanguage) #code #art #finance #architecture #books #astronomy #acting #accounting #region-us
|
The following dataset has been created using camel-ai, by passing various combinations of user and assistant. The dataset was translated to Odia using OdiaGenAI English=>Indic translation app.
|
[] |
[
"TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-Oriya (macrolanguage) #code #art #finance #architecture #books #astronomy #acting #accounting #region-us \n"
] |
[
71
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-1K<n<10K #language-Oriya (macrolanguage) #code #art #finance #architecture #books #astronomy #acting #accounting #region-us \n"
] |
d462eef848b508573a6d23f2e35578b5f410a44c
|
This dataset pulled originally from https://www.propertypriceregister.ie/ , You can visit that website and specify all or one specific county. This version I pulled goes up to March 2022.
|
c123ian/Dublin_House_Prices_2010_2022
|
[
"region:us"
] |
2023-10-13T14:02:16+00:00
|
{}
|
2023-10-13T14:11:58+00:00
|
[] |
[] |
TAGS
#region-us
|
This dataset pulled originally from URL , You can visit that website and specify all or one specific county. This version I pulled goes up to March 2022.
!image/png
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
37bb8df4eeefc6fd1d4eebb0b8e30858e8bfc91a
|
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
duncanodhis/databaseinfor
|
[
"task_categories:question-answering",
"task_categories:summarization",
"language:en",
"license:mit",
"region:us"
] |
2023-10-13T14:10:58+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["question-answering", "summarization"]}
|
2023-10-13T14:14:39+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #task_categories-summarization #language-English #license-mit #region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#task_categories-question-answering #task_categories-summarization #language-English #license-mit #region-us \n",
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
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"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
37,
34,
4,
40,
29,
3,
4,
9,
6,
5,
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4,
7,
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5,
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46,
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7,
10,
5
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[
"passage: TAGS\n#task_categories-question-answering #task_categories-summarization #language-English #license-mit #region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
d8eb2518700caf60bf681c4038452029b2181d49
|
# Dataset Card for "icons"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Imran1/icons
|
[
"region:us"
] |
2023-10-13T14:15:07+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "a-minus-test-symbol", "1": "ab-testing", "2": "acid-test", "3": "advanced-training", "4": "aids-test", "5": "allergy-test", "6": "animal-test", "7": "animal-testing", "8": "animal-training", "9": "baby-train", "10": "blood-count-test", "11": "blood-test", "12": "brain-training", "13": "bullet-train", "14": "cargo-train", "15": "chemical-test-tube", "16": "children-train", "17": "circus-train-car", "18": "color-blindness-test", "19": "computer-test", "20": "covid-test", "21": "crash-test", "22": "crash-testing-dummy-silhouette", "23": "dev", "24": "diabetes-test", "25": "diesel-train", "26": "dna-test", "27": "dog-training", "28": "dog-training-whistle", "29": "driving-test", "30": "drug-test", "31": "dumbbell-training", "32": "electric-train", "33": "emissions-test", "34": "employment-test", "35": "evaluation", "36": "experiment-test-tube", "37": "eye-test", "38": "failure-test", "39": "fast-train", "40": "filled-test-tube-with-a-drop", "41": "final-test", "42": "flight-training", "43": "freight-train", "44": "front-of-train", "45": "front-train-on-tracks", "46": "frontal-train", "47": "frontal-train-and-rails", "48": "genbeta-dev", "49": "gmo-test", "50": "hair-test", "51": "hearing-test", "52": "hemoglobin-test-meter", "53": "high-speed-train", "54": "hospital-test-tube", "55": "image-split-testing", "56": "inkblot-test", "57": "ishihara-test", "58": "medical-test", "59": "medicine-liquid-in-a-test-tube-glass", "60": "mini-train", "61": "monitoring-test", "62": "no-animal-testing", "63": "no-test", "64": "not-valid", "65": "nutritional-test", "66": "oil-train", "67": "old-train", "68": "online-driving-test", "69": "online-test", "70": "online-training", "71": "optical-test", "72": "ovulation-test", "73": "papanicolau-test", "74": "pass-test", "75": "passenger-train", "76": "pcr-test", "77": "penetration-testing", "78": "ph-test", "79": "pregnancy-test", "80": "pregnant-test", "81": "print-test", "82": "printing-test", "83": "pulmonary-function-test", "84": "quality-test", "85": "rapid-test", "86": "rorschach-test", "87": "round-test-tube", "88": "running-test", "89": "science-experiment-hand-drawn-test-tubes-couple", "90": "science-test-tube", "91": "seo-training", "92": "serology-test", "93": "skin-prick-test", "94": "skin-test", "95": "speed-test", "96": "stool-test", "97": "stress-test", "98": "test", "99": "test-card", "100": "test-cases", "101": "test-exam", "102": "test-flight", "103": "test-pen", "104": "test-quiz", "105": "test-result-on-paper", "106": "test-results", "107": "test-tube", "108": "test-tube-and-a-drop", "109": "test-tube-and-drop", "110": "test-tube-and-flask", "111": "test-tube-brush", "112": "test-tube-half-full", "113": "test-tube-rack", "114": "test-tube-with-cap", "115": "test-tube-with-drop", "116": "test-tube-with-liquid", "117": "test-tube-with-liquid-outline", "118": "test-tubes", "119": "test-tubes-hand-drawn-science-tools", "120": "test-tubes-hand-drawn-tools", "121": "testing", "122": "testing-glasses", "123": "three-test-tube", "124": "three-test-tubes", "125": "toy-train", "126": "train", "127": "train-cargo", "128": "train-engine", "129": "train-front", "130": "train-front-and-railroad", "131": "train-front-view", "132": "train-hand-drawn-outline", "133": "train-icon", "134": "train-in-a-tunnel", "135": "train-locomotive-toy", "136": "train-logo", "137": "train-operator", "138": "train-platform", "139": "train-rails", "140": "train-ride", "141": "train-satation-location", "142": "train-sign", "143": "train-station", "144": "train-station-location", "145": "train-station-sign", "146": "train-stop", "147": "train-ticket", "148": "train-times", "149": "train-to-the-airport", "150": "train-toy", "151": "train-track", "152": "train-tracks", "153": "train-wagon", "154": "training", "155": "training-bag", "156": "training-bottle", "157": "training-course", "158": "training-gear", "159": "training-gloves", "160": "training-mat", "161": "training-pants", "162": "training-phrase", "163": "training-watch", "164": "training-whistle", "165": "turing-test", "166": "turings-test", "167": "two-test-tubes", "168": "unit-testing", "169": "urine-test", "170": "user-evaluation", "171": "valid", "172": "valid-document", "173": "validation", "174": "velocity-test", "175": "window-of-test-card", "176": "x-ray-test"}}}}], "splits": [{"name": "train", "num_bytes": 63080287.752, "num_examples": 3976}], "download_size": 67589265, "dataset_size": 63080287.752}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T14:15:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "icons"
More Information needed
|
[
"# Dataset Card for \"icons\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"icons\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"icons\"\n\nMore Information needed"
] |
444e719fc2387c9979f704caf2af27f075848fdf
|
# AutoTrain Dataset for project: jeongmi_chair
## Dataset Description
This dataset has been automatically processed by AutoTrain for project jeongmi_chair.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1000x1000 RGB PIL image>",
"target": 4
},
{
"image": "<700x700 RGB PIL image>",
"target": 6
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['classsicantique', 'frenchprovence', 'industrial', 'koreaaisa', 'lovelyromantic', 'minimalsimple', 'modern', 'natural', 'notherneurope', 'unique', 'vintatageretro'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 796 |
| valid | 204 |
|
galbitang/autotrain-data-jeongmi_chair
|
[
"task_categories:image-classification",
"region:us"
] |
2023-10-13T14:32:58+00:00
|
{"task_categories": ["image-classification"]}
|
2023-10-13T14:56:45+00:00
|
[] |
[] |
TAGS
#task_categories-image-classification #region-us
|
AutoTrain Dataset for project: jeongmi\_chair
=============================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project jeongmi\_chair.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
|
[
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
17,
27,
17,
23,
27
] |
[
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
5909987c90ba81215cbbac3e35852f6da5779254
|
# Hugging Face with Bias Data in CoNLL Format
## Introduction
This README provides guidance on how to use the Hugging Face platform with bias-tagged datasets in the CoNLL format.
Such datasets are essential for studying and mitigating bias in AI models.
This dataset is curated by **Shaina Raza**.
The methods and formatting discussed here are based on the seminal work "Nbias: A natural language processing framework for BIAS identification in text" by Raza et al. (2024) (see citation below).
## Prerequisites
- Install the Hugging Face `transformers` and `datasets` libraries:
```bash
pip install transformers datasets
```
## Data Format
Bias data in CoNLL format can be structured similarly to standard CoNLL, but with labels indicating bias instead of named entities:
```
The O
book O
written B-BIAS
by I-BIAS
egoist I-BIAS
women I-BIAS
is O
good O
. O
```
Here, `B-` prefixes indicate the beginning of a biased term,`I-` indicates inside biased terms, and `O` stands for outside any biased entity.
## Steps to Use with Hugging Face
1. **Loading Bias-tagged CoNLL Data with Hugging Face**
- If your bias-tagged dataset in CoNLL format is publicly available on the Hugging Face `datasets` hub, use:
```python
from datasets import load_dataset
dataset = load_dataset("newsmediabias/BIAS-CONLL")
```
- For custom datasets, ensure they are formatted correctly and use a local path to load them.
If the dataset is gated/private, make sure you have run huggingface-cli login
2. **Preprocessing the Data**
- Tokenization:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("YOUR_PREFERRED_MODEL_CHECKPOINT")
tokenized_input = tokenizer(dataset['train']['tokens'])
```
3. **Training a Model on Bias-tagged CoNLL Data**
- Depending on your task, you may fine-tune a model on the bias data using Hugging Face's `Trainer` class or native PyTorch/TensorFlow code.
4. **Evaluation**
- After training, evaluate the model's ability to recognize and possibly mitigate bias.
- This might involve measuring the model's precision, recall, and F1 score on recognizing bias in text.
5. **Deployment**
- Once satisfied with the model's performance, deploy it for real-world applications, always being mindful of its limitations and potential implications.
Please cite us if you use it.
**Reference to cite us**
```
@article{raza2024nbias,
title={Nbias: A natural language processing framework for BIAS identification in text},
author={Raza, Shaina and Garg, Muskan and Reji, Deepak John and Bashir, Syed Raza and Ding, Chen},
journal={Expert Systems with Applications},
volume={237},
pages={121542},
year={2024},
publisher={Elsevier}
}
```
|
newsmediabias/BIAS-CONLL
|
[
"region:us"
] |
2023-10-13T14:35:27+00:00
|
{}
|
2023-10-25T19:35:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Hugging Face with Bias Data in CoNLL Format
## Introduction
This README provides guidance on how to use the Hugging Face platform with bias-tagged datasets in the CoNLL format.
Such datasets are essential for studying and mitigating bias in AI models.
This dataset is curated by Shaina Raza.
The methods and formatting discussed here are based on the seminal work "Nbias: A natural language processing framework for BIAS identification in text" by Raza et al. (2024) (see citation below).
## Prerequisites
- Install the Hugging Face 'transformers' and 'datasets' libraries:
## Data Format
Bias data in CoNLL format can be structured similarly to standard CoNLL, but with labels indicating bias instead of named entities:
Here, 'B-' prefixes indicate the beginning of a biased term,'I-' indicates inside biased terms, and 'O' stands for outside any biased entity.
## Steps to Use with Hugging Face
1. Loading Bias-tagged CoNLL Data with Hugging Face
- If your bias-tagged dataset in CoNLL format is publicly available on the Hugging Face 'datasets' hub, use:
- For custom datasets, ensure they are formatted correctly and use a local path to load them.
If the dataset is gated/private, make sure you have run huggingface-cli login
2. Preprocessing the Data
- Tokenization:
3. Training a Model on Bias-tagged CoNLL Data
- Depending on your task, you may fine-tune a model on the bias data using Hugging Face's 'Trainer' class or native PyTorch/TensorFlow code.
4. Evaluation
- After training, evaluate the model's ability to recognize and possibly mitigate bias.
- This might involve measuring the model's precision, recall, and F1 score on recognizing bias in text.
5. Deployment
- Once satisfied with the model's performance, deploy it for real-world applications, always being mindful of its limitations and potential implications.
Please cite us if you use it.
Reference to cite us
|
[
"# Hugging Face with Bias Data in CoNLL Format",
"## Introduction\n\nThis README provides guidance on how to use the Hugging Face platform with bias-tagged datasets in the CoNLL format.\nSuch datasets are essential for studying and mitigating bias in AI models.\nThis dataset is curated by Shaina Raza. \nThe methods and formatting discussed here are based on the seminal work \"Nbias: A natural language processing framework for BIAS identification in text\" by Raza et al. (2024) (see citation below).",
"## Prerequisites\n- Install the Hugging Face 'transformers' and 'datasets' libraries:",
"## Data Format\n\nBias data in CoNLL format can be structured similarly to standard CoNLL, but with labels indicating bias instead of named entities:\n\nHere, 'B-' prefixes indicate the beginning of a biased term,'I-' indicates inside biased terms, and 'O' stands for outside any biased entity.",
"## Steps to Use with Hugging Face\n\n1. Loading Bias-tagged CoNLL Data with Hugging Face\n - If your bias-tagged dataset in CoNLL format is publicly available on the Hugging Face 'datasets' hub, use:\n \n - For custom datasets, ensure they are formatted correctly and use a local path to load them.\n If the dataset is gated/private, make sure you have run huggingface-cli login\n\n\n2. Preprocessing the Data\n - Tokenization:\n \n\n3. Training a Model on Bias-tagged CoNLL Data\n - Depending on your task, you may fine-tune a model on the bias data using Hugging Face's 'Trainer' class or native PyTorch/TensorFlow code.\n\n4. Evaluation\n - After training, evaluate the model's ability to recognize and possibly mitigate bias.\n - This might involve measuring the model's precision, recall, and F1 score on recognizing bias in text.\n\n5. Deployment\n - Once satisfied with the model's performance, deploy it for real-world applications, always being mindful of its limitations and potential implications.\n\n\n\nPlease cite us if you use it.\n\nReference to cite us"
] |
[
"TAGS\n#region-us \n",
"# Hugging Face with Bias Data in CoNLL Format",
"## Introduction\n\nThis README provides guidance on how to use the Hugging Face platform with bias-tagged datasets in the CoNLL format.\nSuch datasets are essential for studying and mitigating bias in AI models.\nThis dataset is curated by Shaina Raza. \nThe methods and formatting discussed here are based on the seminal work \"Nbias: A natural language processing framework for BIAS identification in text\" by Raza et al. (2024) (see citation below).",
"## Prerequisites\n- Install the Hugging Face 'transformers' and 'datasets' libraries:",
"## Data Format\n\nBias data in CoNLL format can be structured similarly to standard CoNLL, but with labels indicating bias instead of named entities:\n\nHere, 'B-' prefixes indicate the beginning of a biased term,'I-' indicates inside biased terms, and 'O' stands for outside any biased entity.",
"## Steps to Use with Hugging Face\n\n1. Loading Bias-tagged CoNLL Data with Hugging Face\n - If your bias-tagged dataset in CoNLL format is publicly available on the Hugging Face 'datasets' hub, use:\n \n - For custom datasets, ensure they are formatted correctly and use a local path to load them.\n If the dataset is gated/private, make sure you have run huggingface-cli login\n\n\n2. Preprocessing the Data\n - Tokenization:\n \n\n3. Training a Model on Bias-tagged CoNLL Data\n - Depending on your task, you may fine-tune a model on the bias data using Hugging Face's 'Trainer' class or native PyTorch/TensorFlow code.\n\n4. Evaluation\n - After training, evaluate the model's ability to recognize and possibly mitigate bias.\n - This might involve measuring the model's precision, recall, and F1 score on recognizing bias in text.\n\n5. Deployment\n - Once satisfied with the model's performance, deploy it for real-world applications, always being mindful of its limitations and potential implications.\n\n\n\nPlease cite us if you use it.\n\nReference to cite us"
] |
[
6,
13,
110,
24,
80,
274
] |
[
"passage: TAGS\n#region-us \n# Hugging Face with Bias Data in CoNLL Format## Introduction\n\nThis README provides guidance on how to use the Hugging Face platform with bias-tagged datasets in the CoNLL format.\nSuch datasets are essential for studying and mitigating bias in AI models.\nThis dataset is curated by Shaina Raza. \nThe methods and formatting discussed here are based on the seminal work \"Nbias: A natural language processing framework for BIAS identification in text\" by Raza et al. (2024) (see citation below).## Prerequisites\n- Install the Hugging Face 'transformers' and 'datasets' libraries:## Data Format\n\nBias data in CoNLL format can be structured similarly to standard CoNLL, but with labels indicating bias instead of named entities:\n\nHere, 'B-' prefixes indicate the beginning of a biased term,'I-' indicates inside biased terms, and 'O' stands for outside any biased entity.## Steps to Use with Hugging Face\n\n1. Loading Bias-tagged CoNLL Data with Hugging Face\n - If your bias-tagged dataset in CoNLL format is publicly available on the Hugging Face 'datasets' hub, use:\n \n - For custom datasets, ensure they are formatted correctly and use a local path to load them.\n If the dataset is gated/private, make sure you have run huggingface-cli login\n\n\n2. Preprocessing the Data\n - Tokenization:\n \n\n3. Training a Model on Bias-tagged CoNLL Data\n - Depending on your task, you may fine-tune a model on the bias data using Hugging Face's 'Trainer' class or native PyTorch/TensorFlow code.\n\n4. Evaluation\n - After training, evaluate the model's ability to recognize and possibly mitigate bias.\n - This might involve measuring the model's precision, recall, and F1 score on recognizing bias in text.\n\n5. Deployment\n - Once satisfied with the model's performance, deploy it for real-world applications, always being mindful of its limitations and potential implications.\n\n\n\nPlease cite us if you use it.\n\nReference to cite us"
] |
0283a81e8191543e0cf3218b88cdf7e6bd79ff86
|
# AutoTrain Dataset for project: jin0_sofa
## Dataset Description
This dataset has been automatically processed by AutoTrain for project jin0_sofa.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<850x850 RGB PIL image>",
"target": 4
},
{
"image": "<960x960 RGB PIL image>",
"target": 6
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['classicantique', 'frenchprovence', 'industrial', 'koreaaisa', 'lovelyromantic', 'minimalsimple', 'modern', 'natural', 'notherneurope', 'unique', 'vintageretro'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 795 |
| valid | 205 |
|
galbitang/autotrain-data-jin0_sofa
|
[
"task_categories:image-classification",
"region:us"
] |
2023-10-13T14:41:44+00:00
|
{"task_categories": ["image-classification"]}
|
2023-10-13T14:49:09+00:00
|
[] |
[] |
TAGS
#task_categories-image-classification #region-us
|
AutoTrain Dataset for project: jin0\_sofa
=========================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project jin0\_sofa.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
|
[
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
[
17,
27,
17,
23,
27
] |
[
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
] |
15d8d1ddda9b19a1c10187b629c503ec601f5225
|
## Usage
1. Download from Huggingface
2. Run combine.sh to combined the piece into single dataset
The dataset is stored in the same format as ImageNet-1K.
|
JianhaoDYDY/Real-Fake
|
[
"task_categories:image-classification",
"language:en",
"license:mit",
"region:us"
] |
2023-10-13T14:42:28+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["image-classification"]}
|
2024-01-23T19:12:47+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-classification #language-English #license-mit #region-us
|
## Usage
1. Download from Huggingface
2. Run URL to combined the piece into single dataset
The dataset is stored in the same format as ImageNet-1K.
|
[
"## Usage\n 1. Download from Huggingface\n 2. Run URL to combined the piece into single dataset\n\nThe dataset is stored in the same format as ImageNet-1K."
] |
[
"TAGS\n#task_categories-image-classification #language-English #license-mit #region-us \n",
"## Usage\n 1. Download from Huggingface\n 2. Run URL to combined the piece into single dataset\n\nThe dataset is stored in the same format as ImageNet-1K."
] |
[
26,
37
] |
[
"passage: TAGS\n#task_categories-image-classification #language-English #license-mit #region-us \n## Usage\n 1. Download from Huggingface\n 2. Run URL to combined the piece into single dataset\n\nThe dataset is stored in the same format as ImageNet-1K."
] |
a9724bec7d58af08faf659183b5ce43671b0adcb
|
# Dataset Card for "xlmr_hard_curr_uda_ep3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/xlmr_hard_curr_uda_ep3
|
[
"region:us"
] |
2023-10-13T14:43:03+00:00
|
{"dataset_info": {"features": [{"name": "domain_label", "dtype": "int64"}, {"name": "pass_label", "dtype": "int64"}, {"name": "input", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 774087578, "num_examples": 519240}], "download_size": 233619604, "dataset_size": 774087578}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T14:43:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xlmr_hard_curr_uda_ep3"
More Information needed
|
[
"# Dataset Card for \"xlmr_hard_curr_uda_ep3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xlmr_hard_curr_uda_ep3\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xlmr_hard_curr_uda_ep3\"\n\nMore Information needed"
] |
7f180dae8a4e1e1f52eb22ac5b1205ac935e01a2
|
# Dataset Card for "rbrt_hard_curr_uda_ep3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/rbrt_hard_curr_uda_ep3
|
[
"region:us"
] |
2023-10-13T14:48:20+00:00
|
{"dataset_info": {"features": [{"name": "domain_label", "dtype": "int64"}, {"name": "pass_label", "dtype": "int64"}, {"name": "input", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 744404183, "num_examples": 519240}], "download_size": 242101139, "dataset_size": 744404183}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T14:48:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rbrt_hard_curr_uda_ep3"
More Information needed
|
[
"# Dataset Card for \"rbrt_hard_curr_uda_ep3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rbrt_hard_curr_uda_ep3\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rbrt_hard_curr_uda_ep3\"\n\nMore Information needed"
] |
6811589db32583c3e71141fbd04b476614915467
|
# Dataset Card for "Soldering-Data-pix2pix-1013"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ouvic215/Soldering-Data-pix2pix-1013
|
[
"region:us"
] |
2023-10-13T15:04:47+00:00
|
{"dataset_info": {"features": [{"name": "mask_image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2061392089.0, "num_examples": 24528}], "download_size": 946456098, "dataset_size": 2061392089.0}}
|
2023-10-13T15:23:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Soldering-Data-pix2pix-1013"
More Information needed
|
[
"# Dataset Card for \"Soldering-Data-pix2pix-1013\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Soldering-Data-pix2pix-1013\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Soldering-Data-pix2pix-1013\"\n\nMore Information needed"
] |
3ebfc17809744754150a9ddda8f97044b133afd4
|
# Dataset Card for "rev16"
Configs:
* `full`: the entire 30 podcast files
* `whisper_subset`: the subset of 16 podcast files used in the Whisper paper for long-form evaluation. The remaining 14 files have mis-matches between the audio and labels, and are thus filtered from the test set.
|
distil-whisper/rev16
|
[
"region:us"
] |
2023-10-13T15:09:08+00:00
|
{"dataset_info": [{"config_name": "full", "features": [{"name": "audio", "dtype": "audio"}, {"name": "file_number", "dtype": "string"}, {"name": "show_title", "dtype": "string"}, {"name": "episode_title", "dtype": "string"}, {"name": "itunes_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1509910660.0, "num_examples": 30}], "download_size": 1445493754, "dataset_size": 1509910660.0}, {"config_name": "whisper_subset", "features": [{"name": "audio", "dtype": "audio"}, {"name": "file_number", "dtype": "string"}, {"name": "show_title", "dtype": "string"}, {"name": "episode_title", "dtype": "string"}, {"name": "itunes_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 921693242.0, "num_examples": 16}], "download_size": 881542397, "dataset_size": 921693242.0}], "configs": [{"config_name": "full", "data_files": [{"split": "test", "path": "full/test-*"}]}, {"config_name": "whisper_subset", "data_files": [{"split": "test", "path": "whisper_subset/test-*"}]}]}
|
2023-10-17T16:15:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rev16"
Configs:
* 'full': the entire 30 podcast files
* 'whisper_subset': the subset of 16 podcast files used in the Whisper paper for long-form evaluation. The remaining 14 files have mis-matches between the audio and labels, and are thus filtered from the test set.
|
[
"# Dataset Card for \"rev16\"\n\nConfigs:\n* 'full': the entire 30 podcast files\n* 'whisper_subset': the subset of 16 podcast files used in the Whisper paper for long-form evaluation. The remaining 14 files have mis-matches between the audio and labels, and are thus filtered from the test set."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rev16\"\n\nConfigs:\n* 'full': the entire 30 podcast files\n* 'whisper_subset': the subset of 16 podcast files used in the Whisper paper for long-form evaluation. The remaining 14 files have mis-matches between the audio and labels, and are thus filtered from the test set."
] |
[
6,
80
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rev16\"\n\nConfigs:\n* 'full': the entire 30 podcast files\n* 'whisper_subset': the subset of 16 podcast files used in the Whisper paper for long-form evaluation. The remaining 14 files have mis-matches between the audio and labels, and are thus filtered from the test set."
] |
fc7e84c9e268a5dabfe5452b0fa858f56fc3077c
|
# Dataset Card for "rbrt_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/rbrt_eval
|
[
"region:us"
] |
2023-10-13T15:10:18+00:00
|
{"dataset_info": {"features": [{"name": "domain_label", "dtype": "int64"}, {"name": "pass_label", "dtype": "int64"}, {"name": "input", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 18920775, "num_examples": 11590}], "download_size": 6002960, "dataset_size": 18920775}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T15:10:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rbrt_eval"
More Information needed
|
[
"# Dataset Card for \"rbrt_eval\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rbrt_eval\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rbrt_eval\"\n\nMore Information needed"
] |
3a53c3807b6f7b32cf9e2d1ad4f124c2f5ef79ca
|
# Computed Tomography (CT) of the Brain
The dataset consists of CT brain scans with **cancer, tumor, and aneurysm**. Each scan represents a detailed image of a patient's brain taken using **CT (Computed Tomography)**. The data are presented in 2 different formats: **.jpg and .dcm**.
The dataset of CT brain scans is valuable for research in **neurology, radiology, and oncology**. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for **automated detection, diagnosis, and classification** of these conditions.
### Types of brain diseases in the dataset:
- **cancer**
- **tumor**
- **aneurysm**
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ct-of-the-brain) to discuss your requirements, learn about the price and buy the dataset.
# Content
### The folder "files" includes 3 folders:
- corresponding to name of the brain disease and including ct scans of people with this disease (**cancer, tumor or aneurysm**)
- including brain scans in 2 different formats: **.jpg and .dcm**.
### File with the extension .csv includes the following information for each media file:
- **dcm**: link to access the .dcm file,
- **jpg**: link to access the .jpg file,
- **type**: name of the brain disease on the ct
# Medical data might be collected in accordance with your requirements.
## **[TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ct-of-the-brain)** provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
TrainingDataPro/computed-tomography-ct-of-the-brain
|
[
"task_categories:image-to-image",
"task_categories:image-segmentation",
"task_categories:image-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"biology",
"code",
"medical",
"region:us"
] |
2023-10-13T15:29:23+00:00
|
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-image", "image-segmentation", "image-classification"], "tags": ["biology", "code", "medical"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21304259.0, "num_examples": 150}], "download_size": 21303579, "dataset_size": 21304259.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-10T08:52:49+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-to-image #task_categories-image-segmentation #task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #biology #code #medical #region-us
|
# Computed Tomography (CT) of the Brain
The dataset consists of CT brain scans with cancer, tumor, and aneurysm. Each scan represents a detailed image of a patient's brain taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm.
The dataset of CT brain scans is valuable for research in neurology, radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.
![](URL
### Types of brain diseases in the dataset:
- cancer
- tumor
- aneurysm
# Get the dataset
### This is just an example of the data
Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.
# Content
### The folder "files" includes 3 folders:
- corresponding to name of the brain disease and including ct scans of people with this disease (cancer, tumor or aneurysm)
- including brain scans in 2 different formats: .jpg and .dcm.
### File with the extension .csv includes the following information for each media file:
- dcm: link to access the .dcm file,
- jpg: link to access the .jpg file,
- type: name of the brain disease on the ct
# Medical data might be collected in accordance with your requirements.
## TrainingData provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: URL
TrainingData's GitHub: URL
|
[
"# Computed Tomography (CT) of the Brain\n\nThe dataset consists of CT brain scans with cancer, tumor, and aneurysm. Each scan represents a detailed image of a patient's brain taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm. \n\nThe dataset of CT brain scans is valuable for research in neurology, radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.\n\n![](URL",
"### Types of brain diseases in the dataset:\n- cancer\n- tumor\n- aneurysm",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Content",
"### The folder \"files\" includes 3 folders:\n- corresponding to name of the brain disease and including ct scans of people with this disease (cancer, tumor or aneurysm)\n- including brain scans in 2 different formats: .jpg and .dcm.",
"### File with the extension .csv includes the following information for each media file:\n\n- dcm: link to access the .dcm file,\n- jpg: link to access the .jpg file, \n- type: name of the brain disease on the ct",
"# Medical data might be collected in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
"TAGS\n#task_categories-image-to-image #task_categories-image-segmentation #task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #biology #code #medical #region-us \n",
"# Computed Tomography (CT) of the Brain\n\nThe dataset consists of CT brain scans with cancer, tumor, and aneurysm. Each scan represents a detailed image of a patient's brain taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm. \n\nThe dataset of CT brain scans is valuable for research in neurology, radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.\n\n![](URL",
"### Types of brain diseases in the dataset:\n- cancer\n- tumor\n- aneurysm",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Content",
"### The folder \"files\" includes 3 folders:\n- corresponding to name of the brain disease and including ct scans of people with this disease (cancer, tumor or aneurysm)\n- including brain scans in 2 different formats: .jpg and .dcm.",
"### File with the extension .csv includes the following information for each media file:\n\n- dcm: link to access the .dcm file,\n- jpg: link to access the .jpg file, \n- type: name of the brain disease on the ct",
"# Medical data might be collected in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
66,
144,
22,
5,
30,
2,
62,
57,
13,
39
] |
[
"passage: TAGS\n#task_categories-image-to-image #task_categories-image-segmentation #task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #biology #code #medical #region-us \n# Computed Tomography (CT) of the Brain\n\nThe dataset consists of CT brain scans with cancer, tumor, and aneurysm. Each scan represents a detailed image of a patient's brain taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm. \n\nThe dataset of CT brain scans is valuable for research in neurology, radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.\n\n![](URL### Types of brain diseases in the dataset:\n- cancer\n- tumor\n- aneurysm# Get the dataset### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.# Content### The folder \"files\" includes 3 folders:\n- corresponding to name of the brain disease and including ct scans of people with this disease (cancer, tumor or aneurysm)\n- including brain scans in 2 different formats: .jpg and .dcm.### File with the extension .csv includes the following information for each media file:\n\n- dcm: link to access the .dcm file,\n- jpg: link to access the .jpg file, \n- type: name of the brain disease on the ct# Medical data might be collected in accordance with your requirements.## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
47355895fc82f9f825d5d05504ab8f0bc682acb7
|
# Dataset Card for "c_x86_exebench_json_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zhangshuoming/c_x86_exebench_json_cleaned
|
[
"region:us"
] |
2023-10-13T15:37:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 749238025.3045925, "num_examples": 701744}], "download_size": 209658460, "dataset_size": 749238025.3045925}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T15:57:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "c_x86_exebench_json_cleaned"
More Information needed
|
[
"# Dataset Card for \"c_x86_exebench_json_cleaned\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"c_x86_exebench_json_cleaned\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"c_x86_exebench_json_cleaned\"\n\nMore Information needed"
] |
81f20578347d6c8d4882b9cd5bdad5a7771dc85a
|
----
# Overview
----
"SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.
# Key Features
- Removal of RLHF instances.
- Deduplication using minhash and Jaccard similarity techniques.
# Demo Models
Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.
* https://huggingface.co/openaccess-ai-collective/jackalope-7b
* https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca
----
# Dataset format
----
**Basic Structure**
This dataset uses basic sharegpt formatting. Example and explanation of the schema is below:
```json
{
"conversations": [
{"from": "system", "value": "You are an AI assistant..."},
{"from": "human", "value": "Write an article based on this..."},
{"from": "gpt", "value": "Title: Tragedy Strikes in Sydney..."}
]
}
```
**Message Formatting**
- **"from"**: A string indicating the sender of the message. Possible senders are "system", "human", and "gpt".
- **"value"**: A string containing the message or instruction from the sender.
**Message roles**
- ** System: ** The system provides instructions or guidelines for the task to the large language model (LLM).
- ** Human: ** The human provides prompts or queries for the AI model to respond to.
- ** GPT: ** The language model, generates responses or content based on the prompts or queries provided by the human. messages from this role only ever follow messages from the human role.
----
# Citation
----
```bibtex
@misc{SlimOrcaDedup,
title = {SlimOrca Dedup: A Deduplicated Subset of SlimOrca},
author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium" and Nathan Hoos},
year = {2023},
publisher = {HuggingFace},
url = {https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup/}
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
|
Open-Orca/SlimOrca-Dedup
|
[
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"license:mit",
"code",
"art",
"music",
"legal",
"finance",
"biology",
"chemistry",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] |
2023-10-13T15:45:49+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "question-answering", "text-generation"], "pretty_name": "SlimOrca Dedup", "tags": ["code", "art", "music", "legal", "finance", "biology", "chemistry"]}
|
2023-12-08T03:38:07+00:00
|
[
"2306.02707",
"2301.13688"
] |
[] |
TAGS
#task_categories-text-classification #task_categories-question-answering #task_categories-text-generation #size_categories-100K<n<1M #license-mit #code #art #music #legal #finance #biology #chemistry #arxiv-2306.02707 #arxiv-2301.13688 #region-us
|
----
# Overview
----
"SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.
# Key Features
- Removal of RLHF instances.
- Deduplication using minhash and Jaccard similarity techniques.
# Demo Models
Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.
* URL
* URL
----
# Dataset format
----
Basic Structure
This dataset uses basic sharegpt formatting. Example and explanation of the schema is below:
Message Formatting
- "from": A string indicating the sender of the message. Possible senders are "system", "human", and "gpt".
- "value": A string containing the message or instruction from the sender.
Message roles
- System: The system provides instructions or guidelines for the task to the large language model (LLM).
- Human: The human provides prompts or queries for the AI model to respond to.
- GPT: The language model, generates responses or content based on the prompts or queries provided by the human. messages from this role only ever follow messages from the human role.
----
----
|
[
"# Overview\n\n----\n\n\"SlimOrca Dedup\" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.",
"# Key Features\n\n- Removal of RLHF instances.\n- Deduplication using minhash and Jaccard similarity techniques.",
"# Demo Models\n\nNote: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.\n* URL\n* URL\n \n----",
"# Dataset format\n\n----\n\n Basic Structure\n\n This dataset uses basic sharegpt formatting. Example and explanation of the schema is below:\n \n \n \n Message Formatting\n \n - \"from\": A string indicating the sender of the message. Possible senders are \"system\", \"human\", and \"gpt\".\n - \"value\": A string containing the message or instruction from the sender.\n \n Message roles\n \n - System: The system provides instructions or guidelines for the task to the large language model (LLM). \n - Human: The human provides prompts or queries for the AI model to respond to.\n - GPT: The language model, generates responses or content based on the prompts or queries provided by the human. messages from this role only ever follow messages from the human role.\n\n\n\n----\n\n----"
] |
[
"TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-text-generation #size_categories-100K<n<1M #license-mit #code #art #music #legal #finance #biology #chemistry #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n",
"# Overview\n\n----\n\n\"SlimOrca Dedup\" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.",
"# Key Features\n\n- Removal of RLHF instances.\n- Deduplication using minhash and Jaccard similarity techniques.",
"# Demo Models\n\nNote: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.\n* URL\n* URL\n \n----",
"# Dataset format\n\n----\n\n Basic Structure\n\n This dataset uses basic sharegpt formatting. Example and explanation of the schema is below:\n \n \n \n Message Formatting\n \n - \"from\": A string indicating the sender of the message. Possible senders are \"system\", \"human\", and \"gpt\".\n - \"value\": A string containing the message or instruction from the sender.\n \n Message roles\n \n - System: The system provides instructions or guidelines for the task to the large language model (LLM). \n - Human: The human provides prompts or queries for the AI model to respond to.\n - GPT: The language model, generates responses or content based on the prompts or queries provided by the human. messages from this role only ever follow messages from the human role.\n\n\n\n----\n\n----"
] |
[
92,
51,
29,
37,
171
] |
[
"passage: TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-text-generation #size_categories-100K<n<1M #license-mit #code #art #music #legal #finance #biology #chemistry #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n# Overview\n\n----\n\n\"SlimOrca Dedup\" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.# Key Features\n\n- Removal of RLHF instances.\n- Deduplication using minhash and Jaccard similarity techniques.# Demo Models\n\nNote: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.\n* URL\n* URL\n \n----# Dataset format\n\n----\n\n Basic Structure\n\n This dataset uses basic sharegpt formatting. Example and explanation of the schema is below:\n \n \n \n Message Formatting\n \n - \"from\": A string indicating the sender of the message. Possible senders are \"system\", \"human\", and \"gpt\".\n - \"value\": A string containing the message or instruction from the sender.\n \n Message roles\n \n - System: The system provides instructions or guidelines for the task to the large language model (LLM). \n - Human: The human provides prompts or queries for the AI model to respond to.\n - GPT: The language model, generates responses or content based on the prompts or queries provided by the human. messages from this role only ever follow messages from the human role.\n\n\n\n----\n\n----"
] |
e69cc11f7dc22177f3769df859f8d22a7f5230ad
|
---
license: cc-by-nc-4.0
task_categories:
- object-detection
language:
- la
tags:
- object detection
- critical edition
- yolo
size_categories:
- n<1K
---
# MGH Layout Detection Dataset
## Dataset Description
### General Description
This dataset consists of scans from the MGH critical edition of Alcuin's letters, which were first edited by Ernestus Duemmler in 1895. The digital scans were sourced from the DMGH's repository, which can be accessed [here](https://www.dmgh.de/mgh_epp_4). The scans were annotated using CVAT, marking out two classes: the title of a letter and the body of the letter.
### Why was this dataset created?
The primary motivation behind the creation of this dataset was to enhance the downstream task of OCR. OCR often returns errors due to interferences like marginalia and footnotes present in the scanned pages. By having accurate annotations for the title and body of the letters, users can efficiently isolate the main content of the letters and possibly achieve better OCR results.
Future plans for this dataset include expanding the annotations to encompass footnotes and marginalia, thus further refining the demarcation between the main content and supplementary notes.
### Classes
Currently, the dataset has two annotated classes:
- Title of the letter
- Body of the letter
Planned future additions include:
- Footnotes
- Marginalia
## Sample Annotation
## Biographical Information
### About Alcuin
Alcuin of York (c. 735 – 804 AD) was an English scholar, clergyman, poet, and teacher. He was born in York and became a leading figure in the so-called "Carolingian renaissance." Alcuin made significant contributions to the educational and religious reforms initiated by Charlemagne, emphasizing the importance of classical studies.
### About Alcuin's Letters
Alcuin's letters provide a crucial insight into the Carolingian world, highlighting the intellectual and religious discourse of the time. They serve as invaluable resources for understanding the interactions between some of the important figures of Charlemagne's court, the challenges they faced, and the solutions they proposed. The letters also offer a window into Alcuin's own thoughts, his relationships with peers and, most importantly, his students, and his role as an advisor to Charlemagne.
## Dataset and Annotation Details
### Annotation Process
The scans of Alcuin's letters were annotated manually using the CVAT tool. The primary focus was to delineate the titles and bodies of the letters. This clear demarcation aids in improving the precision of OCR tools by allowing them to target specific regions in the scanned pages.
### Dataset Limitations
As the dataset currently focuses only on titles and bodies of the letters, it may not fully address the challenges posed by marginalia and footnotes in OCR tasks. However, the planned expansion to include these classes will provide a more comprehensive solution.
### Usage
Given the non-commercial restriction associated with the source scans, users of this dataset should be mindful of the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) license under which it is distributed.
## Additional Information
For more details on the dataset and to access the digital scans, visit the DMGH repository link provided above.
|
medieval-data/mgh-critical-edition-layout
|
[
"license:cc-by-nc-4.0",
"doi:10.57967/hf/1210",
"region:us"
] |
2023-10-13T15:48:13+00:00
|
{"license": "cc-by-nc-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "image_id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}, {"name": "id", "sequence": "null"}]}], "splits": [{"name": "train", "num_bytes": 19639133.0, "num_examples": 79}, {"name": "val", "num_bytes": 4967295.0, "num_examples": 21}], "download_size": 24112875, "dataset_size": 24606428.0}}
|
2023-10-13T15:53:55+00:00
|
[] |
[] |
TAGS
#license-cc-by-nc-4.0 #doi-10.57967/hf/1210 #region-us
|
---
license: cc-by-nc-4.0
task_categories:
- object-detection
language:
- la
tags:
- object detection
- critical edition
- yolo
size_categories:
- n<1K
---
# MGH Layout Detection Dataset
## Dataset Description
### General Description
This dataset consists of scans from the MGH critical edition of Alcuin's letters, which were first edited by Ernestus Duemmler in 1895. The digital scans were sourced from the DMGH's repository, which can be accessed here. The scans were annotated using CVAT, marking out two classes: the title of a letter and the body of the letter.
### Why was this dataset created?
The primary motivation behind the creation of this dataset was to enhance the downstream task of OCR. OCR often returns errors due to interferences like marginalia and footnotes present in the scanned pages. By having accurate annotations for the title and body of the letters, users can efficiently isolate the main content of the letters and possibly achieve better OCR results.
Future plans for this dataset include expanding the annotations to encompass footnotes and marginalia, thus further refining the demarcation between the main content and supplementary notes.
### Classes
Currently, the dataset has two annotated classes:
- Title of the letter
- Body of the letter
Planned future additions include:
- Footnotes
- Marginalia
## Sample Annotation
!sample_annotation
## Biographical Information
### About Alcuin
Alcuin of York (c. 735 – 804 AD) was an English scholar, clergyman, poet, and teacher. He was born in York and became a leading figure in the so-called "Carolingian renaissance." Alcuin made significant contributions to the educational and religious reforms initiated by Charlemagne, emphasizing the importance of classical studies.
### About Alcuin's Letters
Alcuin's letters provide a crucial insight into the Carolingian world, highlighting the intellectual and religious discourse of the time. They serve as invaluable resources for understanding the interactions between some of the important figures of Charlemagne's court, the challenges they faced, and the solutions they proposed. The letters also offer a window into Alcuin's own thoughts, his relationships with peers and, most importantly, his students, and his role as an advisor to Charlemagne.
## Dataset and Annotation Details
### Annotation Process
The scans of Alcuin's letters were annotated manually using the CVAT tool. The primary focus was to delineate the titles and bodies of the letters. This clear demarcation aids in improving the precision of OCR tools by allowing them to target specific regions in the scanned pages.
### Dataset Limitations
As the dataset currently focuses only on titles and bodies of the letters, it may not fully address the challenges posed by marginalia and footnotes in OCR tasks. However, the planned expansion to include these classes will provide a more comprehensive solution.
### Usage
Given the non-commercial restriction associated with the source scans, users of this dataset should be mindful of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license under which it is distributed.
## Additional Information
For more details on the dataset and to access the digital scans, visit the DMGH repository link provided above.
|
[
"# MGH Layout Detection Dataset",
"## Dataset Description",
"### General Description\n\nThis dataset consists of scans from the MGH critical edition of Alcuin's letters, which were first edited by Ernestus Duemmler in 1895. The digital scans were sourced from the DMGH's repository, which can be accessed here. The scans were annotated using CVAT, marking out two classes: the title of a letter and the body of the letter.",
"### Why was this dataset created?\n\nThe primary motivation behind the creation of this dataset was to enhance the downstream task of OCR. OCR often returns errors due to interferences like marginalia and footnotes present in the scanned pages. By having accurate annotations for the title and body of the letters, users can efficiently isolate the main content of the letters and possibly achieve better OCR results.\n\nFuture plans for this dataset include expanding the annotations to encompass footnotes and marginalia, thus further refining the demarcation between the main content and supplementary notes.",
"### Classes\n\nCurrently, the dataset has two annotated classes:\n- Title of the letter\n- Body of the letter\n\nPlanned future additions include:\n- Footnotes\n- Marginalia",
"## Sample Annotation\n\n!sample_annotation",
"## Biographical Information",
"### About Alcuin\n\nAlcuin of York (c. 735 – 804 AD) was an English scholar, clergyman, poet, and teacher. He was born in York and became a leading figure in the so-called \"Carolingian renaissance.\" Alcuin made significant contributions to the educational and religious reforms initiated by Charlemagne, emphasizing the importance of classical studies.",
"### About Alcuin's Letters\n\nAlcuin's letters provide a crucial insight into the Carolingian world, highlighting the intellectual and religious discourse of the time. They serve as invaluable resources for understanding the interactions between some of the important figures of Charlemagne's court, the challenges they faced, and the solutions they proposed. The letters also offer a window into Alcuin's own thoughts, his relationships with peers and, most importantly, his students, and his role as an advisor to Charlemagne.",
"## Dataset and Annotation Details",
"### Annotation Process\n\nThe scans of Alcuin's letters were annotated manually using the CVAT tool. The primary focus was to delineate the titles and bodies of the letters. This clear demarcation aids in improving the precision of OCR tools by allowing them to target specific regions in the scanned pages.",
"### Dataset Limitations\n\nAs the dataset currently focuses only on titles and bodies of the letters, it may not fully address the challenges posed by marginalia and footnotes in OCR tasks. However, the planned expansion to include these classes will provide a more comprehensive solution.",
"### Usage\n\nGiven the non-commercial restriction associated with the source scans, users of this dataset should be mindful of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license under which it is distributed.",
"## Additional Information\n\nFor more details on the dataset and to access the digital scans, visit the DMGH repository link provided above."
] |
[
"TAGS\n#license-cc-by-nc-4.0 #doi-10.57967/hf/1210 #region-us \n",
"# MGH Layout Detection Dataset",
"## Dataset Description",
"### General Description\n\nThis dataset consists of scans from the MGH critical edition of Alcuin's letters, which were first edited by Ernestus Duemmler in 1895. The digital scans were sourced from the DMGH's repository, which can be accessed here. The scans were annotated using CVAT, marking out two classes: the title of a letter and the body of the letter.",
"### Why was this dataset created?\n\nThe primary motivation behind the creation of this dataset was to enhance the downstream task of OCR. OCR often returns errors due to interferences like marginalia and footnotes present in the scanned pages. By having accurate annotations for the title and body of the letters, users can efficiently isolate the main content of the letters and possibly achieve better OCR results.\n\nFuture plans for this dataset include expanding the annotations to encompass footnotes and marginalia, thus further refining the demarcation between the main content and supplementary notes.",
"### Classes\n\nCurrently, the dataset has two annotated classes:\n- Title of the letter\n- Body of the letter\n\nPlanned future additions include:\n- Footnotes\n- Marginalia",
"## Sample Annotation\n\n!sample_annotation",
"## Biographical Information",
"### About Alcuin\n\nAlcuin of York (c. 735 – 804 AD) was an English scholar, clergyman, poet, and teacher. He was born in York and became a leading figure in the so-called \"Carolingian renaissance.\" Alcuin made significant contributions to the educational and religious reforms initiated by Charlemagne, emphasizing the importance of classical studies.",
"### About Alcuin's Letters\n\nAlcuin's letters provide a crucial insight into the Carolingian world, highlighting the intellectual and religious discourse of the time. They serve as invaluable resources for understanding the interactions between some of the important figures of Charlemagne's court, the challenges they faced, and the solutions they proposed. The letters also offer a window into Alcuin's own thoughts, his relationships with peers and, most importantly, his students, and his role as an advisor to Charlemagne.",
"## Dataset and Annotation Details",
"### Annotation Process\n\nThe scans of Alcuin's letters were annotated manually using the CVAT tool. The primary focus was to delineate the titles and bodies of the letters. This clear demarcation aids in improving the precision of OCR tools by allowing them to target specific regions in the scanned pages.",
"### Dataset Limitations\n\nAs the dataset currently focuses only on titles and bodies of the letters, it may not fully address the challenges posed by marginalia and footnotes in OCR tasks. However, the planned expansion to include these classes will provide a more comprehensive solution.",
"### Usage\n\nGiven the non-commercial restriction associated with the source scans, users of this dataset should be mindful of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license under which it is distributed.",
"## Additional Information\n\nFor more details on the dataset and to access the digital scans, visit the DMGH repository link provided above."
] |
[
29,
8,
4,
94,
133,
42,
11,
5,
91,
121,
7,
76,
63,
60,
31
] |
[
"passage: TAGS\n#license-cc-by-nc-4.0 #doi-10.57967/hf/1210 #region-us \n# MGH Layout Detection Dataset## Dataset Description### General Description\n\nThis dataset consists of scans from the MGH critical edition of Alcuin's letters, which were first edited by Ernestus Duemmler in 1895. The digital scans were sourced from the DMGH's repository, which can be accessed here. The scans were annotated using CVAT, marking out two classes: the title of a letter and the body of the letter.### Why was this dataset created?\n\nThe primary motivation behind the creation of this dataset was to enhance the downstream task of OCR. OCR often returns errors due to interferences like marginalia and footnotes present in the scanned pages. By having accurate annotations for the title and body of the letters, users can efficiently isolate the main content of the letters and possibly achieve better OCR results.\n\nFuture plans for this dataset include expanding the annotations to encompass footnotes and marginalia, thus further refining the demarcation between the main content and supplementary notes.### Classes\n\nCurrently, the dataset has two annotated classes:\n- Title of the letter\n- Body of the letter\n\nPlanned future additions include:\n- Footnotes\n- Marginalia## Sample Annotation\n\n!sample_annotation## Biographical Information### About Alcuin\n\nAlcuin of York (c. 735 – 804 AD) was an English scholar, clergyman, poet, and teacher. He was born in York and became a leading figure in the so-called \"Carolingian renaissance.\" Alcuin made significant contributions to the educational and religious reforms initiated by Charlemagne, emphasizing the importance of classical studies."
] |
0aef4a4e0b86e43d85f14495d8627f5a7573cc9e
|
This contains a set of schemas obtained via the [JSON Schema Store catalog](https://github.com/SchemaStore/schemastore/blob/master/src/api/json/catalog.json).
|
dataunitylab/json-schema-store
|
[
"size_categories:n<1K",
"language:en",
"json",
"region:us"
] |
2023-10-13T15:51:04+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "pretty_name": "JSON Schema Store", "tags": ["json"]}
|
2023-10-20T16:16:58+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-n<1K #language-English #json #region-us
|
This contains a set of schemas obtained via the JSON Schema Store catalog.
|
[] |
[
"TAGS\n#size_categories-n<1K #language-English #json #region-us \n"
] |
[
23
] |
[
"passage: TAGS\n#size_categories-n<1K #language-English #json #region-us \n"
] |
c3cc28af2873f535442db05fcd0eed0145a04bd5
|
# Dataset Card for "pokemon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Konthee/pokemon
|
[
"region:us"
] |
2023-10-13T16:06:50+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "th-input_ids", "sequence": "int64"}, {"name": "th-attention_mask", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 496836, "num_examples": 666}, {"name": "val", "num_bytes": 124582, "num_examples": 167}], "download_size": 32687, "dataset_size": 621418}}
|
2023-10-14T03:42:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pokemon"
More Information needed
|
[
"# Dataset Card for \"pokemon\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pokemon\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pokemon\"\n\nMore Information needed"
] |
58d779b65826f9e1e2357904c29878d841083e19
|
# Dataset Card for "samsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
saturncloud/samsum
|
[
"region:us"
] |
2023-10-13T16:15:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "eval", "path": "data/eval-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "contexts", "sequence": "null"}], "splits": [{"name": "train", "num_bytes": 9360301, "num_examples": 14732}, {"name": "eval", "num_bytes": 509831, "num_examples": 818}], "download_size": 6284066, "dataset_size": 9870132}}
|
2023-10-17T15:02:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "samsum"
More Information needed
|
[
"# Dataset Card for \"samsum\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"samsum\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"samsum\"\n\nMore Information needed"
] |
4e5e169753f8cb4a21a34d0e50c303528fd6ff2a
|
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
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).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
nk2201/English-to-Hinglish
|
[
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"translation",
"region:us"
] |
2023-10-13T16:23:54+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "json", "tags": ["translation"]}
|
2023-10-13T17:38:15+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-1K<n<10K #language-English #license-mit #translation #region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#size_categories-1K<n<10K #language-English #license-mit #translation #region-us \n",
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Data Collection and Processing",
"#### Who are the source data producers?",
"### Annotations [optional]",
"#### Annotation process",
"#### Who are the annotators?",
"#### Personal and Sensitive Information",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
30,
34,
4,
40,
29,
3,
4,
9,
6,
5,
7,
4,
7,
10,
9,
5,
9,
8,
10,
46,
8,
7,
10,
5
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #language-English #license-mit #translation #region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
6a7ed7e91a5594c62815d3d4fb3dd886d8344c9d
|
# Dataset Card for "es-1310-no-demoji-m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gg-ai/es-1310-no-demoji-m
|
[
"region:us"
] |
2023-10-13T17:58:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "clean_text", "dtype": "string"}, {"name": "sent", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 12631455, "num_examples": 32431}, {"name": "test", "num_bytes": 2492249, "num_examples": 6486}, {"name": "val", "num_bytes": 609294, "num_examples": 1622}], "download_size": 10520538, "dataset_size": 15732998}}
|
2023-10-13T17:58:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "es-1310-no-demoji-m"
More Information needed
|
[
"# Dataset Card for \"es-1310-no-demoji-m\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"es-1310-no-demoji-m\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"es-1310-no-demoji-m\"\n\nMore Information needed"
] |
776d329be6954215c3f1ac956f213b3e0e71bd80
|
# Dataset Card for "ExperimentalFourthBrainMailingDS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TiagoB23/ExperimentalFourthBrainMailingDS
|
[
"region:us"
] |
2023-10-13T18:12:11+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18730, "num_examples": 10}], "download_size": 23195, "dataset_size": 18730}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T18:12:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ExperimentalFourthBrainMailingDS"
More Information needed
|
[
"# Dataset Card for \"ExperimentalFourthBrainMailingDS\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ExperimentalFourthBrainMailingDS\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ExperimentalFourthBrainMailingDS\"\n\nMore Information needed"
] |
3fa71e25fff79fc9c37e833fae00570a477dc35d
|
# Dataset Card for "medical_nonmedical"
This dataset is a combination of [`20newsgroups`](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) and the [`Medical Abstracts TC`](https://github.com/sebischair/Medical-Abstracts-TC-Corpus.git) datasets.
|
ai-maker-space/medical_nonmedical
|
[
"region:us"
] |
2023-10-13T18:12:17+00:00
|
{"dataset_info": {"features": [{"name": "is_medical", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 25910847, "num_examples": 14202}], "download_size": 0, "dataset_size": 25910847}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2024-02-06T23:07:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "medical_nonmedical"
This dataset is a combination of '20newsgroups' and the 'Medical Abstracts TC' datasets.
|
[
"# Dataset Card for \"medical_nonmedical\"\n\nThis dataset is a combination of '20newsgroups' and the 'Medical Abstracts TC' datasets."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"medical_nonmedical\"\n\nThis dataset is a combination of '20newsgroups' and the 'Medical Abstracts TC' datasets."
] |
[
6,
39
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"medical_nonmedical\"\n\nThis dataset is a combination of '20newsgroups' and the 'Medical Abstracts TC' datasets."
] |
04f42d693acd370db0c4d6337f37aad3054764b6
|
# Dataset Card for "MexLotMin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hrangel/MexLotMin
|
[
"region:us"
] |
2023-10-13T18:19:05+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": 415097.0, "num_examples": 10}], "download_size": 337823, "dataset_size": 415097.0}}
|
2023-10-13T18:19:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MexLotMin"
More Information needed
|
[
"# Dataset Card for \"MexLotMin\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MexLotMin\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MexLotMin\"\n\nMore Information needed"
] |
15b72c0905db375fbbd81c5c14745ae4020b9562
|
# Hacker News posts and comments
This is a dataset of all HN posts and comments, current as of November 1, 2023.
|
OpenPipe/hacker-news
|
[
"region:us"
] |
2023-10-13T18:44:25+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "type", "dtype": "string"}, {"name": "by", "dtype": "string"}, {"name": "time", "dtype": "timestamp[us]"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "parent", "dtype": "float64"}, {"name": "top_level_parent", "dtype": "int64"}, {"name": "descendants", "dtype": "float64"}, {"name": "kids", "sequence": "int64"}, {"name": "deleted", "dtype": "bool"}, {"name": "dead", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 16886975696, "num_examples": 38109500}], "download_size": 9948795138, "dataset_size": 16886975696}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-02T13:41:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Hacker News posts and comments
This is a dataset of all HN posts and comments, current as of November 1, 2023.
|
[
"# Hacker News posts and comments\n\nThis is a dataset of all HN posts and comments, current as of November 1, 2023."
] |
[
"TAGS\n#region-us \n",
"# Hacker News posts and comments\n\nThis is a dataset of all HN posts and comments, current as of November 1, 2023."
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Hacker News posts and comments\n\nThis is a dataset of all HN posts and comments, current as of November 1, 2023."
] |
ef96673b926e67818c5153c2cf6263efa6f40d6d
|
# HiFi-16KH
This dataset contains 16,000 hours of hifi music (44.1k) collected from multiple music platforms.
-----
## WARNING
You are not allowed to distribute or mix this dataset.
This dataset is for ACADEMIC USE ONLY.
WE ARE NOT RESPONSIBLE FOR ANY LEGAL ISSUE / DMCA REQUESTS.
TAKE YOUR OWN RISK.
|
fish-audio-private/hifi-16kh
|
[
"region:us"
] |
2023-10-13T18:52:36+00:00
|
{}
|
2024-01-26T16:01:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# HiFi-16KH
This dataset contains 16,000 hours of hifi music (44.1k) collected from multiple music platforms.
-----
## WARNING
You are not allowed to distribute or mix this dataset.
This dataset is for ACADEMIC USE ONLY.
WE ARE NOT RESPONSIBLE FOR ANY LEGAL ISSUE / DMCA REQUESTS.
TAKE YOUR OWN RISK.
|
[
"# HiFi-16KH\n\nThis dataset contains 16,000 hours of hifi music (44.1k) collected from multiple music platforms.\n\n-----",
"## WARNING\n\nYou are not allowed to distribute or mix this dataset. \nThis dataset is for ACADEMIC USE ONLY. \nWE ARE NOT RESPONSIBLE FOR ANY LEGAL ISSUE / DMCA REQUESTS.\n\nTAKE YOUR OWN RISK."
] |
[
"TAGS\n#region-us \n",
"# HiFi-16KH\n\nThis dataset contains 16,000 hours of hifi music (44.1k) collected from multiple music platforms.\n\n-----",
"## WARNING\n\nYou are not allowed to distribute or mix this dataset. \nThis dataset is for ACADEMIC USE ONLY. \nWE ARE NOT RESPONSIBLE FOR ANY LEGAL ISSUE / DMCA REQUESTS.\n\nTAKE YOUR OWN RISK."
] |
[
6,
31,
62
] |
[
"passage: TAGS\n#region-us \n# HiFi-16KH\n\nThis dataset contains 16,000 hours of hifi music (44.1k) collected from multiple music platforms.\n\n-----## WARNING\n\nYou are not allowed to distribute or mix this dataset. \nThis dataset is for ACADEMIC USE ONLY. \nWE ARE NOT RESPONSIBLE FOR ANY LEGAL ISSUE / DMCA REQUESTS.\n\nTAKE YOUR OWN RISK."
] |
a06bce4a9d4c7413ef388c8fced5e95c71087fd6
|
# Dataset Card for "tok-corpus-shuffled"
This is the dataset used to fit custom tokenizers. Goal is to have a ytokenizer that is good for French, Englsih and Code.
The dataset uploaded is shuffled to facilitate subsampling it for tokenizer training.
```
French
Dataset({
features: ['id', 'text', 'dataset_id'],
num_rows: 16881941
})
Code
Dataset({
features: ['id', 'text', 'dataset_id'],
num_rows: 6338566
})
English
Dataset({
features: ['text', 'id', 'dataset_id'],
num_rows: 8440970
})
Size of Concatenated: 124.0 GB
Size of French: 58.0 GB, ratio of 0.4705131689639972
Size of Code: 28.0 GB, ratio of 0.23046591420706297
Size of English: 37.0 GB, ratio of 0.29902091682893983
```
|
manu/tok-corpus-shuffled
|
[
"region:us"
] |
2023-10-13T19:00:26+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "dataset_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 124374684398.0, "num_examples": 31661477}], "download_size": 67451668212, "dataset_size": 124374684398.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T21:42:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tok-corpus-shuffled"
This is the dataset used to fit custom tokenizers. Goal is to have a ytokenizer that is good for French, Englsih and Code.
The dataset uploaded is shuffled to facilitate subsampling it for tokenizer training.
|
[
"# Dataset Card for \"tok-corpus-shuffled\"\n\nThis is the dataset used to fit custom tokenizers. Goal is to have a ytokenizer that is good for French, Englsih and Code.\nThe dataset uploaded is shuffled to facilitate subsampling it for tokenizer training."
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tok-corpus-shuffled\"\n\nThis is the dataset used to fit custom tokenizers. Goal is to have a ytokenizer that is good for French, Englsih and Code.\nThe dataset uploaded is shuffled to facilitate subsampling it for tokenizer training."
] |
[
6,
73
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tok-corpus-shuffled\"\n\nThis is the dataset used to fit custom tokenizers. Goal is to have a ytokenizer that is good for French, Englsih and Code.\nThe dataset uploaded is shuffled to facilitate subsampling it for tokenizer training."
] |
d3d202ad65ca6566f2134d2aebb3c10c83124096
|
# Dataset Card for "TIR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChristopherS27/TIR
|
[
"region:us"
] |
2023-10-13T19:08:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "mask", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 990057666.0, "num_examples": 950}, {"name": "test", "num_bytes": 52080614.0, "num_examples": 50}, {"name": "val", "num_bytes": 52096133.0, "num_examples": 50}], "download_size": 524005256, "dataset_size": 1094234413.0}}
|
2023-10-13T19:25:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TIR"
More Information needed
|
[
"# Dataset Card for \"TIR\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TIR\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"TIR\"\n\nMore Information needed"
] |
b4f6f1e627089a65a21c3e5212a8699bc4741c47
|
# Dataset Card for "korean-child-command-voice_train-0-10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
haseong8012/child-10k_sr-48k
|
[
"region:us"
] |
2023-10-13T19:20:45+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "audio", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 6230798096, "num_examples": 10000}], "download_size": 1789308102, "dataset_size": 6230798096}}
|
2023-10-13T20:29:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "korean-child-command-voice_train-0-10000"
More Information needed
|
[
"# Dataset Card for \"korean-child-command-voice_train-0-10000\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"korean-child-command-voice_train-0-10000\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"korean-child-command-voice_train-0-10000\"\n\nMore Information needed"
] |
e0deec7ab433fa2d834cea7447760382c157fbae
|
# Quotes [JSON dataset]
A dataset comprising artificially generated **quotes** derived from a diverse array of Large Language Models (LLMs) such as GPT-3.5, GPT-4, Claude, Bard, Alpaca, LLaMA, LLaMA-2, Vicuna, PaLM-2 and Mistral-7B.
## Dataset Contents
The dataset comprises artificially generated quotes, with each quote offering a unique perspective on various topics, accompanied by a title, description, and a designated topic. These quotes are entirely generated by AI and are not to be considered as statements of real-world wisdom or knowledge.
## Prompt
The prompt used:
```json
Generate a JSON-formatted list of synthetically generated quotes on various topics, ensuring that each entry follows the specified structure:
'''json
[
{
"title": "...",
"description": "...",
"topic": "..."
},
]
'''
```
## Disclaimer
Please note that while I strive to maintain data quality, I cannot guarantee the accuracy or quality of all entries in this dataset. Use it responsibly and exercise caution when relying on the data for any critical applications. Your feedback and contributions are greatly appreciated for improving the dataset's overall quality.
|
Tanvir1337/quotes
|
[
"size_categories:1K<n<10K",
"language:en",
"license:cdla-sharing-1.0",
"GPT-3.5",
"GPT-4",
"Claude",
"Bard",
"Alpaca",
"LLaMA",
"LLaMA-2",
"Vicuna",
"PaLM-2",
"Mistral-7B",
"region:us"
] |
2023-10-13T19:23:15+00:00
|
{"language": ["en"], "license": "cdla-sharing-1.0", "size_categories": ["1K<n<10K"], "pretty_name": "Quotes", "tags": ["GPT-3.5", "GPT-4", "Claude", "Bard", "Alpaca", "LLaMA", "LLaMA-2", "Vicuna", "PaLM-2", "Mistral-7B"]}
|
2023-10-14T15:31:50+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-1K<n<10K #language-English #license-cdla-sharing-1.0 #GPT-3.5 #GPT-4 #Claude #Bard #Alpaca #LLaMA #LLaMA-2 #Vicuna #PaLM-2 #Mistral-7B #region-us
|
# Quotes [JSON dataset]
A dataset comprising artificially generated quotes derived from a diverse array of Large Language Models (LLMs) such as GPT-3.5, GPT-4, Claude, Bard, Alpaca, LLaMA, LLaMA-2, Vicuna, PaLM-2 and Mistral-7B.
## Dataset Contents
The dataset comprises artificially generated quotes, with each quote offering a unique perspective on various topics, accompanied by a title, description, and a designated topic. These quotes are entirely generated by AI and are not to be considered as statements of real-world wisdom or knowledge.
## Prompt
The prompt used:
## Disclaimer
Please note that while I strive to maintain data quality, I cannot guarantee the accuracy or quality of all entries in this dataset. Use it responsibly and exercise caution when relying on the data for any critical applications. Your feedback and contributions are greatly appreciated for improving the dataset's overall quality.
|
[
"# Quotes [JSON dataset]\n\nA dataset comprising artificially generated quotes derived from a diverse array of Large Language Models (LLMs) such as GPT-3.5, GPT-4, Claude, Bard, Alpaca, LLaMA, LLaMA-2, Vicuna, PaLM-2 and Mistral-7B.",
"## Dataset Contents\n\nThe dataset comprises artificially generated quotes, with each quote offering a unique perspective on various topics, accompanied by a title, description, and a designated topic. These quotes are entirely generated by AI and are not to be considered as statements of real-world wisdom or knowledge.",
"## Prompt\n\nThe prompt used:",
"## Disclaimer\n\nPlease note that while I strive to maintain data quality, I cannot guarantee the accuracy or quality of all entries in this dataset. Use it responsibly and exercise caution when relying on the data for any critical applications. Your feedback and contributions are greatly appreciated for improving the dataset's overall quality."
] |
[
"TAGS\n#size_categories-1K<n<10K #language-English #license-cdla-sharing-1.0 #GPT-3.5 #GPT-4 #Claude #Bard #Alpaca #LLaMA #LLaMA-2 #Vicuna #PaLM-2 #Mistral-7B #region-us \n",
"# Quotes [JSON dataset]\n\nA dataset comprising artificially generated quotes derived from a diverse array of Large Language Models (LLMs) such as GPT-3.5, GPT-4, Claude, Bard, Alpaca, LLaMA, LLaMA-2, Vicuna, PaLM-2 and Mistral-7B.",
"## Dataset Contents\n\nThe dataset comprises artificially generated quotes, with each quote offering a unique perspective on various topics, accompanied by a title, description, and a designated topic. These quotes are entirely generated by AI and are not to be considered as statements of real-world wisdom or knowledge.",
"## Prompt\n\nThe prompt used:",
"## Disclaimer\n\nPlease note that while I strive to maintain data quality, I cannot guarantee the accuracy or quality of all entries in this dataset. Use it responsibly and exercise caution when relying on the data for any critical applications. Your feedback and contributions are greatly appreciated for improving the dataset's overall quality."
] |
[
73,
78,
71,
8,
73
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #language-English #license-cdla-sharing-1.0 #GPT-3.5 #GPT-4 #Claude #Bard #Alpaca #LLaMA #LLaMA-2 #Vicuna #PaLM-2 #Mistral-7B #region-us \n# Quotes [JSON dataset]\n\nA dataset comprising artificially generated quotes derived from a diverse array of Large Language Models (LLMs) such as GPT-3.5, GPT-4, Claude, Bard, Alpaca, LLaMA, LLaMA-2, Vicuna, PaLM-2 and Mistral-7B.## Dataset Contents\n\nThe dataset comprises artificially generated quotes, with each quote offering a unique perspective on various topics, accompanied by a title, description, and a designated topic. These quotes are entirely generated by AI and are not to be considered as statements of real-world wisdom or knowledge.## Prompt\n\nThe prompt used:## Disclaimer\n\nPlease note that while I strive to maintain data quality, I cannot guarantee the accuracy or quality of all entries in this dataset. Use it responsibly and exercise caution when relying on the data for any critical applications. Your feedback and contributions are greatly appreciated for improving the dataset's overall quality."
] |
1fdebaa66ef58a9517c87e73f813f5923490b5c2
|
# Dataset Card for "zhwikisource-zhtw"
**維基文庫**（英文：Wikisource）, 又稱 "自由的圖書館", 是一個由志願者在線收集自由內容文本的站點。 它屬維基媒體計劃項目，由維基媒體基金會負責運營。
作品類型:
- 典籍 | 史書 | 小說 | 詩歌 | 散文 | 演講 | 歌詞 | 經書 | 更多……
主題：
- 條約 | 憲法 | 法律 | 教育 | 政治 | 歷史 | 宗教 | 更多……
精選：
- 文章： 道德經 | 脂硯齋重評石頭記
- 文集： 紅樓夢 | 三國演義 | 西遊記 | 詩經 | 夢溪筆談 | 三十六計 | 古文觀止
- 歷史： 史記 | 資治通鑑 | 續資治通鑑 | 金史 | 漢書 | 後漢書 | 三國志
- 判例： 中國大理院解釋 | 中華民國最高法院解釋 | 中華民國司法院解釋 | 中華民國司法院大法官解釋
- 分類： 中華民國法律 | 中華人民共和國法律 | 中華人民共和國國務院政府工作報告 | 十三經 | 正史
這個數據集是根據 Wikipedia dumps (https://dumps.wikimedia.org/) 裡頭 `zhwikisource` 的中文下載檔案來建構的。每個範例都包含一篇完整的維基新聞文章的內容，並經過清理以去除不需要的部分。
- **Homepage:** [https://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **zhwikisource 下載點:** [https://dumps.wikimedia.org/zhwikisource](https://dumps.wikimedia.org/zhwikisource/)
## 數據 Dump 版本
由於維基百科數據集定期會進行網站數據拋轉，在 `2023/10/10` 的時間點去查看時會有下列的數據可供下載:
|數據 Dump 目錄|拋轉時間點|
|-------------|--------|
|`20230520/`|01-Jul-2023 09:25|
|`20230601/`|20-Jul-2023 09:28|
|`20230620/`|01-Aug-2023 09:27|
|`20230701/`|20-Aug-2023 09:30|
|`20230720/`|01-Sep-2023 09:27|
|`20230801/`|20-Sep-2023 09:29|
|`20230820/`|01-Oct-2023 09:28|
|`20230901/`|02-Sep-2023 21:44|
|`20230920/`|21-Sep-2023 17:25|
|`20231001/`|14-Oct-2023 05:20|
|`latest/`|14-Oct-2023 05:20|
本數據集會定期去取得最近有明確的日期來進行下載與清理，便於驗證與使用。
## 數據下載清理
1. 下載 zhwiki 的 data dump 檔案
2. 使用 [WikiExtractor](https://github.com/attardi/wikiextractor) 套件來進行文件內容萃取
3. 使用 [hanzidentifier](https://github.com/tsroten/hanzidentifier) 來判斷內容是中文簡體或繁體 (用文章的 `title`)
4. 進行數據清理并轉換成 jsonl 格式檔案
5. 使用 Huggingface [Datasets](https://pypi.org/project/datasets/) 套件來載入 jsonl 并上傳至 Huggingface Hub
## 資料集結構
範例如下：
```
{'id': '7183',
'url': 'https://zh.wikisource.org/wiki?curid=7183',
'title': '相見歡 (李煜)',
'lang': 1,
'text': '無言獨上西樓，月如鉤。寂寞梧桐深院鎖清秋。剪不斷，理還亂，是離愁。別是一般滋味在心頭。'
}
```
## 資料欄位
所有配置中的資料欄位都是相同的：
- `id (str)`: 文章的 ID。
- `url (str)`: 文章的 URL。
- `title (str)`: 文章的標題。
- `lang (int)`: 判斷內容是中文簡體或繁體 (用文章的 `title`)。
- 0: UNKNOWN
- 1: TRADITIONAL (中文繁體)
- 2: SIMPLIFIED (中文簡體)
- 3: BOTH
- 4: MIXED
- `text (str)`: 文章的文字內容。
## 使用
```python
from datasets import load_dataset
# 請在第二個參數去指定要使用的數據 dump 的日期
load_dataset("erhwenkuo/zhwikisource-zhtw", "20231001")
```
## 許可資訊
維基百科的大部分文章內容及其許多圖像均根據 `Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA)` 和 `GNU Free Documentation License (GFDL)` 共同授權。
## Citation
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
|
erhwenkuo/zhwikisource-zhtw
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:cc-by-sa-3.0",
"region:us"
] |
2023-10-13T21:43:13+00:00
|
{"language": ["zh"], "license": "cc-by-sa-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "dataset_info": {"config_name": "20231001", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "lang", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4441187554, "num_examples": 311698}], "download_size": 2980564378, "dataset_size": 4441187554}, "configs": [{"config_name": "20231001", "data_files": [{"split": "train", "path": "20231001/train-*"}]}]}
|
2023-10-14T04:45:51+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-cc-by-sa-3.0 #region-us
|
Dataset Card for "zhwikisource-zhtw"
====================================
維基文庫（英文：Wikisource）, 又稱 "自由的圖書館", 是一個由志願者在線收集自由內容文本的站點。 它屬維基媒體計劃項目，由維基媒體基金會負責運營。
作品類型:
* 典籍 | 史書 | 小說 | 詩歌 | 散文 | 演講 | 歌詞 | 經書 | 更多……
主題：
* 條約 | 憲法 | 法律 | 教育 | 政治 | 歷史 | 宗教 | 更多……
精選：
* 文章： 道德經 | 脂硯齋重評石頭記
* 文集： 紅樓夢 | 三國演義 | 西遊記 | 詩經 | 夢溪筆談 | 三十六計 | 古文觀止
* 歷史： 史記 | 資治通鑑 | 續資治通鑑 | 金史 | 漢書 | 後漢書 | 三國志
* 判例： 中國大理院解釋 | 中華民國最高法院解釋 | 中華民國司法院解釋 | 中華民國司法院大法官解釋
* 分類： 中華民國法律 | 中華人民共和國法律 | 中華人民共和國國務院政府工作報告 | 十三經 | 正史
這個數據集是根據 Wikipedia dumps (URL 裡頭 'zhwikisource' 的中文下載檔案來建構的。每個範例都包含一篇完整的維基新聞文章的內容，並經過清理以去除不需要的部分。
* Homepage: URL
* zhwikisource 下載點: URL
數據 Dump 版本
----------
由於維基百科數據集定期會進行網站數據拋轉，在 '2023/10/10' 的時間點去查看時會有下列的數據可供下載:
本數據集會定期去取得最近有明確的日期來進行下載與清理，便於驗證與使用。
數據下載清理
------
1. 下載 zhwiki 的 data dump 檔案
2. 使用 WikiExtractor 套件來進行文件內容萃取
3. 使用 hanzidentifier 來判斷內容是中文簡體或繁體 (用文章的 'title')
4. 進行數據清理并轉換成 jsonl 格式檔案
5. 使用 Huggingface Datasets 套件來載入 jsonl 并上傳至 Huggingface Hub
資料集結構
-----
範例如下：
資料欄位
----
所有配置中的資料欄位都是相同的：
* 'id (str)': 文章的 ID。
* 'url (str)': 文章的 URL。
* 'title (str)': 文章的標題。
* 'lang (int)': 判斷內容是中文簡體或繁體 (用文章的 'title')。
+ 0: UNKNOWN
+ 1: TRADITIONAL (中文繁體)
+ 2: SIMPLIFIED (中文簡體)
+ 3: BOTH
+ 4: MIXED
* 'text (str)': 文章的文字內容。
使用
--
許可資訊
----
維基百科的大部分文章內容及其許多圖像均根據 'Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA)' 和 'GNU Free Documentation License (GFDL)' 共同授權。
|
[] |
[
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-cc-by-sa-3.0 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #license-cc-by-sa-3.0 #region-us \n"
] |
0c3aeeadace0ca6ac8845c6d1e8f8f01ccfb2c14
|
# Synthetic Multimodal Video Benchmark (SMVB)
A dataset consisting of synthetic images from distinct synthetic scenes, annotated with object/instance/semantic segmentation masks, depth data, surface normal information and optical for testing and benchmarking model performance for multi-task/multi-objective learning.
### Supported Tasks and Leaderboards
The dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, depth, surface normal, and optical flow estimation, and video object segmentation.
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
### Citation Information
```bibtex
@INPROCEEDINGS{karoly2024synthetic,
author={Károly, Artúr I. and Nádas, Imre and Galambos, Péter},
booktitle={2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)},
title={Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation},
year={2024},
volume={},
number={},
pages={},
doi={}}
```
|
ABC-iRobotics/SMVB
|
[
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:depth-estimation",
"task_categories:video-classification",
"task_categories:other",
"task_ids:instance-segmentation",
"task_ids:semantic-segmentation",
"annotations_creators:machine-generated",
"size_categories:1K<n<10K",
"language:en",
"license:gpl-3.0",
"vision",
"image-segmentation",
"instance-segmentation",
"object-detection",
"optical-flow",
"depth",
"synthetic",
"sim-to-real",
"region:us"
] |
2023-10-13T22:34:25+00:00
|
{"annotations_creators": ["machine-generated"], "language": ["en"], "license": "gpl-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["object-detection", "image-segmentation", "depth-estimation", "video-classification", "other"], "task_ids": ["instance-segmentation", "semantic-segmentation"], "pretty_name": "SMVB Dataset", "tags": ["vision", "image-segmentation", "instance-segmentation", "object-detection", "optical-flow", "depth", "synthetic", "sim-to-real"]}
|
2023-12-04T15:10:15+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-object-detection #task_categories-image-segmentation #task_categories-depth-estimation #task_categories-video-classification #task_categories-other #task_ids-instance-segmentation #task_ids-semantic-segmentation #annotations_creators-machine-generated #size_categories-1K<n<10K #language-English #license-gpl-3.0 #vision #image-segmentation #instance-segmentation #object-detection #optical-flow #depth #synthetic #sim-to-real #region-us
|
# Synthetic Multimodal Video Benchmark (SMVB)
A dataset consisting of synthetic images from distinct synthetic scenes, annotated with object/instance/semantic segmentation masks, depth data, surface normal information and optical for testing and benchmarking model performance for multi-task/multi-objective learning.
### Supported Tasks and Leaderboards
The dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, depth, surface normal, and optical flow estimation, and video object segmentation.
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
|
[
"# Synthetic Multimodal Video Benchmark (SMVB)\n\nA dataset consisting of synthetic images from distinct synthetic scenes, annotated with object/instance/semantic segmentation masks, depth data, surface normal information and optical for testing and benchmarking model performance for multi-task/multi-objective learning.",
"### Supported Tasks and Leaderboards\n\nThe dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, depth, surface normal, and optical flow estimation, and video object segmentation.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data"
] |
[
"TAGS\n#task_categories-object-detection #task_categories-image-segmentation #task_categories-depth-estimation #task_categories-video-classification #task_categories-other #task_ids-instance-segmentation #task_ids-semantic-segmentation #annotations_creators-machine-generated #size_categories-1K<n<10K #language-English #license-gpl-3.0 #vision #image-segmentation #instance-segmentation #object-detection #optical-flow #depth #synthetic #sim-to-real #region-us \n",
"# Synthetic Multimodal Video Benchmark (SMVB)\n\nA dataset consisting of synthetic images from distinct synthetic scenes, annotated with object/instance/semantic segmentation masks, depth data, surface normal information and optical for testing and benchmarking model performance for multi-task/multi-objective learning.",
"### Supported Tasks and Leaderboards\n\nThe dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, depth, surface normal, and optical flow estimation, and video object segmentation.",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data"
] |
[
157,
78,
55,
6,
6,
5,
5,
5,
7,
4
] |
[
"passage: TAGS\n#task_categories-object-detection #task_categories-image-segmentation #task_categories-depth-estimation #task_categories-video-classification #task_categories-other #task_ids-instance-segmentation #task_ids-semantic-segmentation #annotations_creators-machine-generated #size_categories-1K<n<10K #language-English #license-gpl-3.0 #vision #image-segmentation #instance-segmentation #object-detection #optical-flow #depth #synthetic #sim-to-real #region-us \n# Synthetic Multimodal Video Benchmark (SMVB)\n\nA dataset consisting of synthetic images from distinct synthetic scenes, annotated with object/instance/semantic segmentation masks, depth data, surface normal information and optical for testing and benchmarking model performance for multi-task/multi-objective learning.### Supported Tasks and Leaderboards\n\nThe dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, depth, surface normal, and optical flow estimation, and video object segmentation.## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data"
] |
cb610a60e815436c80584873db63c5153c36b45f
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ebinum](https://huggingface.co/ebinum) for evaluating this model.
|
autoevaluate/autoeval-eval-acronym_identification-default-d87697-95015146250
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-13T22:35:51+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["acronym_identification"], "eval_info": {"task": "entity_extraction", "model": "lewtun/autotrain-acronym-identification-7324788", "metrics": ["code_eval", "lvwerra/ai4code"], "dataset_name": "acronym_identification", "dataset_config": "default", "dataset_split": "train", "col_mapping": {"tokens": "tokens", "tags": "labels"}}}
|
2023-10-13T22:39:17+00:00
|
[] |
[] |
TAGS
#autotrain #evaluation #region-us
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @ebinum for evaluating this model.
|
[
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @ebinum for evaluating this model."
] |
[
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @ebinum for evaluating this model."
] |
[
13,
96,
15
] |
[
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: train\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @ebinum for evaluating this model."
] |
a16143c298aa5606f7311ae68145969cce94281d
|
# Dataset Card for "dataya-nolja-llama2-finetuning"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ken-sungmin/dataya-nolja-llama2-finetuning
|
[
"region:us"
] |
2023-10-13T23:27:33+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 85851, "num_examples": 100}], "download_size": 0, "dataset_size": 85851}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-14T05:18:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dataya-nolja-llama2-finetuning"
More Information needed
|
[
"# Dataset Card for \"dataya-nolja-llama2-finetuning\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dataya-nolja-llama2-finetuning\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dataya-nolja-llama2-finetuning\"\n\nMore Information needed"
] |
01af4bc5922f6e4ebc48d8c1027a666bc05e74d0
|
# Dataset Card for "xlmr_hard_curr_uda_ep3_corr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/xlmr_hard_curr_uda_ep3_corr
|
[
"region:us"
] |
2023-10-13T23:42:32+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "pass_label", "dtype": "int64"}, {"name": "domain_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 774087578, "num_examples": 519240}], "download_size": 231183382, "dataset_size": 774087578}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T23:43:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xlmr_hard_curr_uda_ep3_corr"
More Information needed
|
[
"# Dataset Card for \"xlmr_hard_curr_uda_ep3_corr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xlmr_hard_curr_uda_ep3_corr\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xlmr_hard_curr_uda_ep3_corr\"\n\nMore Information needed"
] |
23ca3174280382f13e12ba88fea9df603dc53674
|
# Dataset Card for "rbrt_hard_curr_uda_ep3_corr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/rbrt_hard_curr_uda_ep3_corr
|
[
"region:us"
] |
2023-10-13T23:46:55+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "pass_label", "dtype": "int64"}, {"name": "domain_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 744404183, "num_examples": 519240}], "download_size": 239299242, "dataset_size": 744404183}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-13T23:47:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rbrt_hard_curr_uda_ep3_corr"
More Information needed
|
[
"# Dataset Card for \"rbrt_hard_curr_uda_ep3_corr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rbrt_hard_curr_uda_ep3_corr\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rbrt_hard_curr_uda_ep3_corr\"\n\nMore Information needed"
] |
83e97c67a66ead491e3ad735358ab4639feb815f
|
# Dataset Card for "AdventureTimeCaptions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
varun4/AdventureTimeCaptions
|
[
"region:us"
] |
2023-10-13T23:55:44+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": 62319.0, "num_examples": 3}], "download_size": 58529, "dataset_size": 62319.0}}
|
2023-10-14T20:14:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "AdventureTimeCaptions"
More Information needed
|
[
"# Dataset Card for \"AdventureTimeCaptions\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"AdventureTimeCaptions\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"AdventureTimeCaptions\"\n\nMore Information needed"
] |
72cac67e35c6a37ea986f983b27dec9b1e52ffd2
|
## max_context_length: 128
## max_documents_per_subject: 100
|
sordonia/id-maxD100
|
[
"region:us"
] |
2023-10-14T00:00:16+00:00
|
{}
|
2023-10-14T00:00:30+00:00
|
[] |
[] |
TAGS
#region-us
|
## max_context_length: 128
## max_documents_per_subject: 100
|
[
"## max_context_length: 128",
"## max_documents_per_subject: 100"
] |
[
"TAGS\n#region-us \n",
"## max_context_length: 128",
"## max_documents_per_subject: 100"
] |
[
6,
10,
12
] |
[
"passage: TAGS\n#region-us \n## max_context_length: 128## max_documents_per_subject: 100"
] |
54bad13d3d9d568bc41f038d0d85cc5555e79ec9
|
---
license: apache-2.0
---
FFDM Lyric by Zhijiao for OSS LZU
|
Zhijiao/FDDM_Lyric
|
[
"region:us"
] |
2023-10-14T02:52:15+00:00
|
{}
|
2023-10-14T02:53:47+00:00
|
[] |
[] |
TAGS
#region-us
|
---
license: apache-2.0
---
FFDM Lyric by Zhijiao for OSS LZU
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
12dfde3be855dac89568974ffb7e9b4e1e87ba8d
|
# Introduction
Second-by-second price updates from the last 60 seconds of the monthly license plate auction in Shanghai from 2014 to 2020, and a few months of 2021. The seconds data is given as a differential compared to the startprice. I managed to correctly predict and score a license plate on all three years that I worked on the project during 2018-2020. But it's not easy as there are lots of other factors affecting success on top of prediction accuracy.
# Read More
To learn the details about the auction process and why it is so darn hard, please read my [article series](https://www.linkedin.com/pulse/part-1-applied-ml-timeline-prediction-shanghai-license-tianyi-pan) on LinkedIn.
|
tipani/Shanghai-License-Plate-Auction
|
[
"task_categories:tabular-regression",
"task_categories:time-series-forecasting",
"language:en",
"license:mit",
"License Plate",
"Auction",
"Timeline",
"region:us"
] |
2023-10-14T03:31:22+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["tabular-regression", "time-series-forecasting"], "pretty_name": "Shanghai License Plate Auction 2014-2021", "tags": ["License Plate", "Auction", "Timeline"]}
|
2023-10-14T03:43:48+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-tabular-regression #task_categories-time-series-forecasting #language-English #license-mit #License Plate #Auction #Timeline #region-us
|
# Introduction
Second-by-second price updates from the last 60 seconds of the monthly license plate auction in Shanghai from 2014 to 2020, and a few months of 2021. The seconds data is given as a differential compared to the startprice. I managed to correctly predict and score a license plate on all three years that I worked on the project during 2018-2020. But it's not easy as there are lots of other factors affecting success on top of prediction accuracy.
# Read More
To learn the details about the auction process and why it is so darn hard, please read my article series on LinkedIn.
|
[
"# Introduction\nSecond-by-second price updates from the last 60 seconds of the monthly license plate auction in Shanghai from 2014 to 2020, and a few months of 2021. The seconds data is given as a differential compared to the startprice. I managed to correctly predict and score a license plate on all three years that I worked on the project during 2018-2020. But it's not easy as there are lots of other factors affecting success on top of prediction accuracy.",
"# Read More\nTo learn the details about the auction process and why it is so darn hard, please read my article series on LinkedIn."
] |
[
"TAGS\n#task_categories-tabular-regression #task_categories-time-series-forecasting #language-English #license-mit #License Plate #Auction #Timeline #region-us \n",
"# Introduction\nSecond-by-second price updates from the last 60 seconds of the monthly license plate auction in Shanghai from 2014 to 2020, and a few months of 2021. The seconds data is given as a differential compared to the startprice. I managed to correctly predict and score a license plate on all three years that I worked on the project during 2018-2020. But it's not easy as there are lots of other factors affecting success on top of prediction accuracy.",
"# Read More\nTo learn the details about the auction process and why it is so darn hard, please read my article series on LinkedIn."
] |
[
53,
102,
29
] |
[
"passage: TAGS\n#task_categories-tabular-regression #task_categories-time-series-forecasting #language-English #license-mit #License Plate #Auction #Timeline #region-us \n# Introduction\nSecond-by-second price updates from the last 60 seconds of the monthly license plate auction in Shanghai from 2014 to 2020, and a few months of 2021. The seconds data is given as a differential compared to the startprice. I managed to correctly predict and score a license plate on all three years that I worked on the project during 2018-2020. But it's not easy as there are lots of other factors affecting success on top of prediction accuracy.# Read More\nTo learn the details about the auction process and why it is so darn hard, please read my article series on LinkedIn."
] |
09c51d659784dc928814424ca9c1e014370d0b4f
|
# Dataset Card for "vt_multiapi_v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hmao/vt_multiapi_v0
|
[
"region:us"
] |
2023-10-14T03:51:56+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "fncall", "sequence": "string"}, {"name": "generated_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25028, "num_examples": 70}], "download_size": 12622, "dataset_size": 25028}}
|
2023-10-19T15:52:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "vt_multiapi_v0"
More Information needed
|
[
"# Dataset Card for \"vt_multiapi_v0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"vt_multiapi_v0\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"vt_multiapi_v0\"\n\nMore Information needed"
] |
624f06878a533d5533f2af683136b25738d14392
|
# Dataset Card for "vietnamese_general_data_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tinhpx2911/vietnamese_general_data_processed
|
[
"region:us"
] |
2023-10-14T04:12:02+00:00
|
{"dataset_info": [{"config_name": "train_1", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13070931261, "num_examples": 32434667}], "download_size": 6902902017, "dataset_size": 13070931261}, {"config_name": "train_2", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13079301675, "num_examples": 32444361}], "download_size": 6907570478, "dataset_size": 13079301675}, {"config_name": "train_3", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13083262611, "num_examples": 32455485}], "download_size": 6908687251, "dataset_size": 13083262611}, {"config_name": "train_4", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13083227441, "num_examples": 32440768}], "download_size": 6909612652, "dataset_size": 13083227441}, {"config_name": "train_5", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10862029760, "num_examples": 26942980}], "download_size": 5736766203, "dataset_size": 10862029760}], "configs": [{"config_name": "train_1", "data_files": [{"split": "train", "path": "train_1/train-*"}]}, {"config_name": "train_2", "data_files": [{"split": "train", "path": "train_2/train-*"}]}, {"config_name": "train_3", "data_files": [{"split": "train", "path": "train_3/train-*"}]}, {"config_name": "train_4", "data_files": [{"split": "train", "path": "train_4/train-*"}]}, {"config_name": "train_5", "data_files": [{"split": "train", "path": "train_5/train-*"}]}]}
|
2023-10-14T07:15:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "vietnamese_general_data_processed"
More Information needed
|
[
"# Dataset Card for \"vietnamese_general_data_processed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"vietnamese_general_data_processed\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"vietnamese_general_data_processed\"\n\nMore Information needed"
] |
ceb729b2e40444f5831b0360b608d18627a12f3c
|
# Dataset Card for "new-image-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yusuf802/new-image-dataset
|
[
"region:us"
] |
2023-10-14T04:22:09+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Apple_Black_rot", "1": "Apple_Cedar_apple_rust", "2": "Apple_Powdery_mildew", "3": "Apple_healthy", "4": "Apple_scab", "5": "Cherry_(including_sour)_Powdery_mildew", "6": "Cherry_(including_sour)_healthy", "7": "Corn_(maize)_Cercospora_leaf_spot Gray_leaf_spot", "8": "Corn_(maize)_Common_rust", "9": "Corn_(maize)_Northern_Leaf_Blight", "10": "Corn_(maize)_healthy", "11": "Cotton_leaf_diseased", "12": "Cotton_leaf_fresh", "13": "Grape_Black_rot", "14": "Grape___Esca_(Black_Measles)", "15": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "16": "Grape___healthy", "17": "Orange_Haunglongbing_(Citrus_greening)", "18": "Orange__Black_Rot", "19": "Orange__Canker", "20": "Orange__Healthy", "21": "Peach_Bacterial_spot", "22": "Peach_healthy", "23": "Pepper,_bell_Bacterial_spot", "24": "Pepper,_bell_healthy", "25": "Potato_Early_blight", "26": "Potato_Late_blight", "27": "Potato_healthy", "28": "Squash_Powdery_mildew", "29": "Strawberry_Leaf_scorch", "30": "Strawberry_healthy", "31": "Tomato_Bacterial_spot", "32": "Tomato_Early_blight", "33": "Tomato_Late_blight", "34": "Tomato_Leaf_Mold", "35": "Tomato_Septoria_leaf_spot", "36": "Tomato_Spider_mites_Two_spotted_spider_mite", "37": "Tomato_Target_Spot", "38": "Tomato_Tomato_Yellow_Leaf_Curl_Virus", "39": "Tomato_Tomato_mosaic_virus", "40": "Tomato_healthy", "41": "Wheat_healthy", "42": "Wheat_leaf_rust", "43": "Wheat_nitrogen_deficiency"}}}}], "splits": [{"name": "train", "num_bytes": 5580252809.260068, "num_examples": 56842}, {"name": "test", "num_bytes": 960697024.6779323, "num_examples": 10032}], "download_size": 6476692260, "dataset_size": 6540949833.938}}
|
2023-10-14T08:09:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "new-image-dataset"
More Information needed
|
[
"# Dataset Card for \"new-image-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"new-image-dataset\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"new-image-dataset\"\n\nMore Information needed"
] |
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