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8a27a7988a20341352b85bf0e65e88a6c1337f1c | # Dataset Card for "fMoW"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | danielz01/fMoW | [
"region:us"
]
| 2023-11-20T08:29:33+00:00 | {"configs": [{"config_name": "WILDS", "data_files": [{"split": "test", "path": "WILDS/test-*"}, {"split": "id_test", "path": "WILDS/id_test-*"}, {"split": "val", "path": "WILDS/val-*"}, {"split": "id_val", "path": "WILDS/id_val-*"}, {"split": "train", "path": "WILDS/train-*"}]}], "dataset_info": {"config_name": "WILDS", "features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airport", "1": "airport_hangar", "2": "airport_terminal", "3": "amusement_park", "4": "aquaculture", "5": "archaeological_site", "6": "barn", "7": "border_checkpoint", "8": "burial_site", "9": "car_dealership", "10": "construction_site", "11": "crop_field", "12": "dam", "13": "debris_or_rubble", "14": "educational_institution", "15": "electric_substation", "16": "factory_or_powerplant", "17": "fire_station", "18": "flooded_road", "19": "fountain", "20": "gas_station", "21": "golf_course", "22": "ground_transportation_station", "23": "helipad", "24": "hospital", "25": "impoverished_settlement", "26": "interchange", "27": "lake_or_pond", "28": "lighthouse", "29": "military_facility", "30": "multi-unit_residential", "31": "nuclear_powerplant", "32": "office_building", "33": "oil_or_gas_facility", "34": "park", "35": "parking_lot_or_garage", "36": "place_of_worship", "37": "police_station", "38": "port", "39": "prison", "40": "race_track", "41": "railway_bridge", "42": "recreational_facility", "43": "road_bridge", "44": "runway", "45": "shipyard", "46": "shopping_mall", "47": "single-unit_residential", "48": "smokestack", "49": "solar_farm", "50": "space_facility", "51": "stadium", "52": "storage_tank", "53": "surface_mine", "54": "swimming_pool", "55": "toll_booth", "56": "tower", "57": "tunnel_opening", "58": "waste_disposal", "59": "water_treatment_facility", "60": "wind_farm", "61": "zoo"}}}}, {"name": "domain_labels", "sequence": "int64"}, {"name": "domain_labels_readable", "struct": [{"name": "from_source_domain", "dtype": "bool"}, {"name": "region", "dtype": "string"}, {"name": "y", "dtype": "null"}, {"name": "year", "dtype": "int64"}]}, {"name": "split", "dtype": "string"}, {"name": "img_filename", "dtype": "string"}, {"name": "img_path", "dtype": "string"}, {"name": "spatial_reference", "dtype": "string"}, {"name": "epsg", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "visible", "dtype": "bool"}, {"name": "img_width", "dtype": "int64"}, {"name": "img_height", "dtype": "int64"}, {"name": "country_code", "dtype": "string"}, {"name": "cloud_cover", "dtype": "int64"}, {"name": "timestamp", "dtype": "string"}, {"name": "lat", "dtype": "float64"}, {"name": "lon", "dtype": "float64"}, {"name": "region", "dtype": "int64"}, {"name": "y", "dtype": "int64"}, {"name": "year", "dtype": "float64"}], "splits": [{"name": "test", "num_bytes": 2283079843.392, "num_examples": 22108}, {"name": "id_test", "num_bytes": 1168174637.125, "num_examples": 11327}, {"name": "val", "num_bytes": 2052331276.625, "num_examples": 19915}, {"name": "id_val", "num_bytes": 1191085782.625, "num_examples": 11483}, {"name": "train", "num_bytes": 7946709118.125, "num_examples": 76863}], "download_size": 14612709837, "dataset_size": 14641380657.892}} | 2023-11-20T10:36:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fMoW"
More Information needed | [
"# Dataset Card for \"fMoW\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fMoW\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fMoW\"\n\nMore Information needed"
]
|
0ee94e4fa6782d549d9fe95654748e81eea827e8 | # Dataset Card for "AutomaticSpeechRecognition_LibriSpeech-TestOther"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DynamicSuperb/AutomaticSpeechRecognition_LibriSpeech-TestOther | [
"region:us"
]
| 2023-11-20T08:30:26+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 352426584.188, "num_examples": 2939}], "download_size": 332888539, "dataset_size": 352426584.188}} | 2023-11-20T08:33:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AutomaticSpeechRecognition_LibriSpeech-TestOther"
More Information needed | [
"# Dataset Card for \"AutomaticSpeechRecognition_LibriSpeech-TestOther\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AutomaticSpeechRecognition_LibriSpeech-TestOther\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AutomaticSpeechRecognition_LibriSpeech-TestOther\"\n\nMore Information needed"
]
|
9945e8ff9eec06bb9807ab2276108e0e39a731f7 |
# Dataset Card for Evaluation run of maywell/koOpenChat-sft
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/koOpenChat-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 [maywell/koOpenChat-sft](https://huggingface.co/maywell/koOpenChat-sft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_maywell__koOpenChat-sft_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-20T08:36:25.253046](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__koOpenChat-sft_public/blob/main/results_2023-11-20T08-36-25.253046.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
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"mc2_stderr": 0.014984310875510325,
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"em_stderr": 0.0007322104102794216,
"f1": 0.07822776845637572,
"f1_stderr": 0.0016538004844235878
},
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"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.03061111655743253,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.03061111655743253
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3378212974296206,
"mc1_stderr": 0.01655716732251688,
"mc2": 0.5124049209846685,
"mc2_stderr": 0.014984310875510325
},
"harness|winogrande|5": {
"acc": 0.7640094711917916,
"acc_stderr": 0.011933828850275626
},
"harness|drop|3": {
"em": 0.005138422818791947,
"em_stderr": 0.0007322104102794216,
"f1": 0.07822776845637572,
"f1_stderr": 0.0016538004844235878
},
"harness|gsm8k|5": {
"acc": 0.24184988627748294,
"acc_stderr": 0.011794861371318695
}
}
```
### 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_maywell__koOpenChat-sft | [
"region:us"
]
| 2023-11-20T08:39:23+00:00 | {"pretty_name": "Evaluation run of maywell/koOpenChat-sft", "dataset_summary": "Dataset automatically created during the evaluation run of model [maywell/koOpenChat-sft](https://huggingface.co/maywell/koOpenChat-sft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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_maywell__koOpenChat-sft_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-20T08:36:25.253046](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__koOpenChat-sft_public/blob/main/results_2023-11-20T08-36-25.253046.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\": 0.6084632908836825,\n \"acc_stderr\": 0.03295483776577676,\n \"acc_norm\": 0.6158685044863811,\n \"acc_norm_stderr\": 0.03365334045258809,\n \"mc1\": 0.3378212974296206,\n \"mc1_stderr\": 0.01655716732251688,\n \"mc2\": 0.5124049209846685,\n \"mc2_stderr\": 0.014984310875510325,\n \"em\": 0.005138422818791947,\n \"em_stderr\": 0.0007322104102794216,\n \"f1\": 0.07822776845637572,\n \"f1_stderr\": 0.0016538004844235878\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.568259385665529,\n \"acc_stderr\": 0.014474591427196202,\n \"acc_norm\": 0.5981228668941979,\n \"acc_norm_stderr\": 0.014327268614578273\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5913164708225453,\n \"acc_stderr\": 0.004905859114942294,\n \"acc_norm\": 0.7872933678550089,\n \"acc_norm_stderr\": 0.004083855139469325\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5481481481481482,\n \"acc_stderr\": 0.042992689054808644,\n \"acc_norm\": 0.5481481481481482,\n \"acc_norm_stderr\": 0.042992689054808644\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.038607315993160904,\n \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.038607315993160904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.03656343653353159,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.03656343653353159\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137285,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137285\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7451612903225806,\n \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.7451612903225806,\n \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124484,\n \"acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124484\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121434,\n \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121434\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.0245375915728305,\n \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.0245375915728305\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8238532110091743,\n \"acc_stderr\": 0.016332882393431374,\n \"acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.016332882393431374\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082395,\n \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082395\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n \"acc_norm_stderr\": 0.02250903393707781\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n \"acc_stderr\": 0.014419123980931899,\n \"acc_norm\": 0.7956577266922095,\n \"acc_norm_stderr\": 0.014419123980931899\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917205,\n \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917205\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4134078212290503,\n \"acc_stderr\": 0.01646981492840617,\n \"acc_norm\": 0.4134078212290503,\n \"acc_norm_stderr\": 0.01646981492840617\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6503267973856209,\n \"acc_stderr\": 0.027305308076274695,\n \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.027305308076274695\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6481481481481481,\n \"acc_stderr\": 0.026571483480719967,\n \"acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.026571483480719967\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4511082138200782,\n \"acc_stderr\": 0.012709037347346233,\n \"acc_norm\": 0.4511082138200782,\n \"acc_norm_stderr\": 0.012709037347346233\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.030161911930767112,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.030161911930767112\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.619281045751634,\n \"acc_stderr\": 0.019643801557924803,\n \"acc_norm\": 0.619281045751634,\n \"acc_norm_stderr\": 0.019643801557924803\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.030555316755573637,\n \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.030555316755573637\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7910447761194029,\n \"acc_stderr\": 0.028748298931728655,\n \"acc_norm\": 0.7910447761194029,\n \"acc_norm_stderr\": 0.028748298931728655\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3378212974296206,\n \"mc1_stderr\": 0.01655716732251688,\n \"mc2\": 0.5124049209846685,\n \"mc2_stderr\": 0.014984310875510325\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7640094711917916,\n \"acc_stderr\": 0.011933828850275626\n },\n \"harness|drop|3\": {\n \"em\": 0.005138422818791947,\n \"em_stderr\": 0.0007322104102794216,\n \"f1\": 0.07822776845637572,\n \"f1_stderr\": 0.0016538004844235878\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.24184988627748294,\n \"acc_stderr\": 0.011794861371318695\n }\n}\n```", "repo_url": "https://huggingface.co/maywell/koOpenChat-sft", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_20T08_36_25.253046", "path": ["**/details_harness|arc:challenge|25_2023-11-20T08-36-25.253046.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-20T08-36-25.253046.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_20T08_36_25.253046", "path": ["**/details_harness|drop|3_2023-11-20T08-36-25.253046.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-20T08-36-25.253046.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_20T08_36_25.253046", "path": ["**/details_harness|gsm8k|5_2023-11-20T08-36-25.253046.parquet"]}, 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of maywell/koOpenChat-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 maywell/koOpenChat-sft on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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 2023-11-20T08:36:25.253046(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 maywell/koOpenChat-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 maywell/koOpenChat-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-11-20T08:36:25.253046(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 maywell/koOpenChat-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 maywell/koOpenChat-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-11-20T08:36:25.253046(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 maywell/koOpenChat-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 maywell/koOpenChat-sft on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-11-20T08:36:25.253046(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"
]
|
f4de2b4c8026eb7152565e6b9be62c778bdffd09 | # Dataset Card for "random25_find_passage_train1000_eval100_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random25_find_passage_train1000_eval100_rare | [
"region:us"
]
| 2023-11-20T08:39:32+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": 210960, "num_examples": 2100}, {"name": "validation", "num_bytes": 11496, "num_examples": 100}], "download_size": 84635, "dataset_size": 222456}} | 2023-11-20T08:39:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random25_find_passage_train1000_eval100_rare"
More Information needed | [
"# Dataset Card for \"random25_find_passage_train1000_eval100_rare\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"random25_find_passage_train1000_eval100_rare\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"random25_find_passage_train1000_eval100_rare\"\n\nMore Information needed"
]
|
bd7e2997c23b62525bd212043bac86df613d124b | # Dataset Card for "random25_find_passage_train10000_eval100_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random25_find_passage_train10000_eval100_rare | [
"region:us"
]
| 2023-11-20T08:40:11+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": 2031126, "num_examples": 20100}, {"name": "validation", "num_bytes": 11496, "num_examples": 100}], "download_size": 704761, "dataset_size": 2042622}} | 2023-11-20T08:40:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random25_find_passage_train10000_eval100_rare"
More Information needed | [
"# Dataset Card for \"random25_find_passage_train10000_eval100_rare\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"random25_find_passage_train10000_eval100_rare\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"random25_find_passage_train10000_eval100_rare\"\n\nMore Information needed"
]
|
ef3376e8152aea3844da0cf438cc97f73efa74c6 | # Dataset Card for "AutomaticSpeechRecognition_LJSpeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DynamicSuperb/AutomaticSpeechRecognition_LJSpeech | [
"region:us"
]
| 2023-11-20T08:42:22+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 3800884574.0, "num_examples": 13100}], "download_size": 3785131725, "dataset_size": 3800884574.0}} | 2023-11-20T08:45:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AutomaticSpeechRecognition_LJSpeech"
More Information needed | [
"# Dataset Card for \"AutomaticSpeechRecognition_LJSpeech\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AutomaticSpeechRecognition_LJSpeech\"\n\nMore Information needed"
]
| [
6,
23
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AutomaticSpeechRecognition_LJSpeech\"\n\nMore Information needed"
]
|
5c2eccb5cfb586a5ac9176c3685ab581545bd230 | ## Introduction
[ArcMMLU](https://github.com/stzhang-patrick/ArcMMLU) is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science. Please refer to our paper for more information [ArcMMLU: A Library and Information Science Benchmark for Large Language Models](https://arxiv.org/abs/2311.18658)
It is important to note that the name ArcMMLU is derived from our previous large language model research project—[ArcGPT](https://arxiv.org/abs/2307.14852), which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline.
For the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the [CMMLU---Chinese Multi-Task Language Understanding Evaluation](https://github.com/haonan-li/CMMLU) project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs.
| patrickshitou/ArcMMLU | [
"license:cc-by-nc-sa-4.0",
"arxiv:2311.18658",
"arxiv:2307.14852",
"region:us"
]
| 2023-11-20T08:44:58+00:00 | {"license": "cc-by-nc-sa-4.0"} | 2023-12-01T07:43:50+00:00 | [
"2311.18658",
"2307.14852"
]
| []
| TAGS
#license-cc-by-nc-sa-4.0 #arxiv-2311.18658 #arxiv-2307.14852 #region-us
| ## Introduction
ArcMMLU is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science. Please refer to our paper for more information ArcMMLU: A Library and Information Science Benchmark for Large Language Models
It is important to note that the name ArcMMLU is derived from our previous large language model research project—ArcGPT, which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline.
For the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the CMMLU---Chinese Multi-Task Language Understanding Evaluation project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs.
| [
"## Introduction\n\nArcMMLU is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science. Please refer to our paper for more information ArcMMLU: A Library and Information Science Benchmark for Large Language Models\n\nIt is important to note that the name ArcMMLU is derived from our previous large language model research project—ArcGPT, which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline.\n\nFor the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the CMMLU---Chinese Multi-Task Language Understanding Evaluation project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs."
]
| [
"TAGS\n#license-cc-by-nc-sa-4.0 #arxiv-2311.18658 #arxiv-2307.14852 #region-us \n",
"## Introduction\n\nArcMMLU is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science. Please refer to our paper for more information ArcMMLU: A Library and Information Science Benchmark for Large Language Models\n\nIt is important to note that the name ArcMMLU is derived from our previous large language model research project—ArcGPT, which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline.\n\nFor the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the CMMLU---Chinese Multi-Task Language Understanding Evaluation project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs."
]
| [
36,
327
]
| [
"passage: TAGS\n#license-cc-by-nc-sa-4.0 #arxiv-2311.18658 #arxiv-2307.14852 #region-us \n## Introduction\n\nArcMMLU is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science. Please refer to our paper for more information ArcMMLU: A Library and Information Science Benchmark for Large Language Models\n\nIt is important to note that the name ArcMMLU is derived from our previous large language model research project—ArcGPT, which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline.\n\nFor the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the CMMLU---Chinese Multi-Task Language Understanding Evaluation project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs."
]
|
d036e1ebc35a5b91c312ce08cd1b96f1fcbb329f |
# Dataset Card for Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [l3utterfly/mistral-7b-v0.1-layla-v2](https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_l3utterfly__mistral-7b-v0.1-layla-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T17:43:48.335083](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__mistral-7b-v0.1-layla-v2/blob/main/results_2023-12-16T17-43-48.335083.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": 0.5109909692126704,
"acc_stderr": 0.034097544063625904,
"acc_norm": 0.5139816809039905,
"acc_norm_stderr": 0.03481633806243026,
"mc1": 0.3561811505507956,
"mc1_stderr": 0.01676379072844634,
"mc2": 0.5156921467880321,
"mc2_stderr": 0.015196148225186935
},
"harness|arc:challenge|25": {
"acc": 0.5332764505119454,
"acc_stderr": 0.014578995859605813,
"acc_norm": 0.5631399317406144,
"acc_norm_stderr": 0.014494421584256515
},
"harness|hellaswag|10": {
"acc": 0.6014738099980084,
"acc_stderr": 0.004885942040894562,
"acc_norm": 0.7976498705437164,
"acc_norm_stderr": 0.004009307895677153
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.25,
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},
"harness|hendrycksTest-anatomy|5": {
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},
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"acc_norm_stderr": 0.04051646342874143
},
"harness|hendrycksTest-business_ethics|5": {
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},
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"harness|hendrycksTest-college_computer_science|5": {
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},
"harness|hendrycksTest-college_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm_stderr": 0.03847021420456023
},
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"harness|hendrycksTest-management|5": {
"acc": 0.6699029126213593,
"acc_stderr": 0.0465614711001235,
"acc_norm": 0.6699029126213593,
"acc_norm_stderr": 0.0465614711001235
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7692307692307693,
"acc_stderr": 0.02760192138141762,
"acc_norm": 0.7692307692307693,
"acc_norm_stderr": 0.02760192138141762
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7241379310344828,
"acc_stderr": 0.01598281477469563,
"acc_norm": 0.7241379310344828,
"acc_norm_stderr": 0.01598281477469563
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5173410404624278,
"acc_stderr": 0.02690290045866664,
"acc_norm": 0.5173410404624278,
"acc_norm_stderr": 0.02690290045866664
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.33743016759776534,
"acc_stderr": 0.01581390128391305,
"acc_norm": 0.33743016759776534,
"acc_norm_stderr": 0.01581390128391305
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5130718954248366,
"acc_stderr": 0.028620130800700246,
"acc_norm": 0.5130718954248366,
"acc_norm_stderr": 0.028620130800700246
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5562700964630225,
"acc_stderr": 0.02821768355665231,
"acc_norm": 0.5562700964630225,
"acc_norm_stderr": 0.02821768355665231
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5493827160493827,
"acc_stderr": 0.027684721415656203,
"acc_norm": 0.5493827160493827,
"acc_norm_stderr": 0.027684721415656203
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3191489361702128,
"acc_stderr": 0.027807990141320196,
"acc_norm": 0.3191489361702128,
"acc_norm_stderr": 0.027807990141320196
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3650586701434159,
"acc_stderr": 0.012296373743443478,
"acc_norm": 0.3650586701434159,
"acc_norm_stderr": 0.012296373743443478
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"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4742647058823529,
"acc_stderr": 0.03033257809455504,
"acc_norm": 0.4742647058823529,
"acc_norm_stderr": 0.03033257809455504
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.020212274976302957,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.020212274976302957
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6181818181818182,
"acc_stderr": 0.046534298079135075,
"acc_norm": 0.6181818181818182,
"acc_norm_stderr": 0.046534298079135075
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5510204081632653,
"acc_stderr": 0.03184213866687579,
"acc_norm": 0.5510204081632653,
"acc_norm_stderr": 0.03184213866687579
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7661691542288557,
"acc_stderr": 0.029929415408348387,
"acc_norm": 0.7661691542288557,
"acc_norm_stderr": 0.029929415408348387
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.73,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.73,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
"acc_stderr": 0.03891364495835817,
"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835817
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7485380116959064,
"acc_stderr": 0.033275044238468436,
"acc_norm": 0.7485380116959064,
"acc_norm_stderr": 0.033275044238468436
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3561811505507956,
"mc1_stderr": 0.01676379072844634,
"mc2": 0.5156921467880321,
"mc2_stderr": 0.015196148225186935
},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.012042352526174789
},
"harness|gsm8k|5": {
"acc": 0.31387414708112205,
"acc_stderr": 0.01278268125105321
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### 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. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_l3utterfly__mistral-7b-v0.1-layla-v2 | [
"region:us"
]
| 2023-11-20T09:07:04+00:00 | {"pretty_name": "Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [l3utterfly/mistral-7b-v0.1-layla-v2](https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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_l3utterfly__mistral-7b-v0.1-layla-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-16T17:43:48.335083](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__mistral-7b-v0.1-layla-v2/blob/main/results_2023-12-16T17-43-48.335083.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\": 0.5109909692126704,\n \"acc_stderr\": 0.034097544063625904,\n \"acc_norm\": 0.5139816809039905,\n \"acc_norm_stderr\": 0.03481633806243026,\n \"mc1\": 0.3561811505507956,\n \"mc1_stderr\": 0.01676379072844634,\n \"mc2\": 0.5156921467880321,\n \"mc2_stderr\": 0.015196148225186935\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5332764505119454,\n \"acc_stderr\": 0.014578995859605813,\n \"acc_norm\": 0.5631399317406144,\n \"acc_norm_stderr\": 0.014494421584256515\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6014738099980084,\n \"acc_stderr\": 0.004885942040894562,\n \"acc_norm\": 0.7976498705437164,\n \"acc_norm_stderr\": 0.004009307895677153\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5460526315789473,\n \"acc_stderr\": 0.04051646342874143,\n \"acc_norm\": 0.5460526315789473,\n \"acc_norm_stderr\": 0.04051646342874143\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.5509433962264151,\n \"acc_stderr\": 0.030612730713641095,\n \"acc_norm\": 0.5509433962264151,\n \"acc_norm_stderr\": 0.030612730713641095\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4913294797687861,\n \"acc_stderr\": 0.038118909889404126,\n \"acc_norm\": 0.4913294797687861,\n \"acc_norm_stderr\": 0.038118909889404126\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4765957446808511,\n \"acc_stderr\": 0.032650194750335815,\n \"acc_norm\": 0.4765957446808511,\n \"acc_norm_stderr\": 0.032650194750335815\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3508771929824561,\n \"acc_stderr\": 0.044895393502706986,\n \"acc_norm\": 0.3508771929824561,\n \"acc_norm_stderr\": 0.044895393502706986\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02391998416404772,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02391998416404772\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.603225806451613,\n \"acc_stderr\": 0.027831231605767944,\n \"acc_norm\": 0.603225806451613,\n \"acc_norm_stderr\": 0.027831231605767944\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.37438423645320196,\n \"acc_stderr\": 0.03405155380561952,\n \"acc_norm\": 0.37438423645320196,\n \"acc_norm_stderr\": 0.03405155380561952\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6303030303030303,\n \"acc_stderr\": 0.03769430314512568,\n \"acc_norm\": 0.6303030303030303,\n \"acc_norm_stderr\": 0.03769430314512568\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.0347327959083696,\n \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.0347327959083696\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.7253886010362695,\n \"acc_stderr\": 0.03221024508041153,\n \"acc_norm\": 0.7253886010362695,\n \"acc_norm_stderr\": 0.03221024508041153\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4512820512820513,\n \"acc_stderr\": 0.025230381238934837,\n \"acc_norm\": 0.4512820512820513,\n \"acc_norm_stderr\": 0.025230381238934837\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.46638655462184875,\n \"acc_stderr\": 0.03240501447690071,\n \"acc_norm\": 0.46638655462184875,\n \"acc_norm_stderr\": 0.03240501447690071\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.26490066225165565,\n \"acc_stderr\": 0.03603038545360384,\n \"acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.03603038545360384\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6807339449541284,\n \"acc_stderr\": 0.019987829069750027,\n \"acc_norm\": 0.6807339449541284,\n \"acc_norm_stderr\": 0.019987829069750027\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.28703703703703703,\n \"acc_stderr\": 0.030851992993257013,\n \"acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.030851992993257013\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6568627450980392,\n \"acc_stderr\": 0.033321399446680854,\n \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.033321399446680854\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6455696202531646,\n \"acc_stderr\": 0.0311373042971858,\n \"acc_norm\": 0.6455696202531646,\n \"acc_norm_stderr\": 0.0311373042971858\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5725190839694656,\n \"acc_stderr\": 0.04338920305792401,\n \"acc_norm\": 0.5725190839694656,\n \"acc_norm_stderr\": 0.04338920305792401\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6859504132231405,\n \"acc_stderr\": 0.042369647530410184,\n \"acc_norm\": 0.6859504132231405,\n \"acc_norm_stderr\": 0.042369647530410184\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6012269938650306,\n \"acc_stderr\": 0.03847021420456023,\n \"acc_norm\": 0.6012269938650306,\n \"acc_norm_stderr\": 0.03847021420456023\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n \"acc_stderr\": 0.046695106638751906,\n \"acc_norm\": 0.4107142857142857,\n \"acc_norm_stderr\": 0.046695106638751906\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7692307692307693,\n \"acc_stderr\": 0.02760192138141762,\n \"acc_norm\": 0.7692307692307693,\n \"acc_norm_stderr\": 0.02760192138141762\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 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| []
| TAGS
#region-us
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# Dataset Card for Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2
Dataset automatically created during the evaluation run of model l3utterfly/mistral-7b-v0.1-layla-v2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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 2023-12-16T17:43:48.335083(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):
## 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.
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BibTeX:
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## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
| [
"# Dataset Card for Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2\n\n\n\nDataset automatically created during the evaluation run of model l3utterfly/mistral-7b-v0.1-layla-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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 2023-12-16T17:43:48.335083(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):",
"## Dataset Details",
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"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"# Dataset Card for Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2\n\n\n\nDataset automatically created during the evaluation run of model l3utterfly/mistral-7b-v0.1-layla-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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 2023-12-16T17:43:48.335083(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):",
"## 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:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2\n\n\n\nDataset automatically created during the evaluation run of model l3utterfly/mistral-7b-v0.1-layla-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the 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 2023-12-16T17:43:48.335083(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):## 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]"
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|
d449d64b70b3ae77a17072d48938eca67c7af1aa | # Dataset Card for "math_train_zl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | laitrongduc/math_train_zl | [
"region:us"
]
| 2023-11-20T09:28:21+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "choices", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 301700, "num_examples": 1200}], "download_size": 149569, "dataset_size": 301700}} | 2023-11-20T09:28:36+00:00 | []
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7565cc5e78c225bf6738a4bc1535352d5b135640 | # Dataset Card for "oct-object-detection-v3-merge"
Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels.
[Source datataset](https://doi.org/10.1101/2023.03.29.534704)
| joseluhf11/oct-object-detection-v3-merge | [
"region:us"
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| 2023-11-20T09:28:44+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "objects", "struct": [{"name": "bbox", "sequence": {"sequence": "int64"}}, {"name": "categories", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 154014595.25, "num_examples": 1246}], "download_size": 71638878, "dataset_size": 154014595.25}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-22T08:49:02+00:00 | []
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| TAGS
#region-us
| # Dataset Card for "oct-object-detection-v3-merge"
Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels.
Source datataset
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"# Dataset Card for \"oct-object-detection-v3-merge\"\nDataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease. \nThe unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels.\nSource datataset"
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|
43d123000f04dbb9e892bda5bf5db6e49c273655 |
# ShareGPT4V 1.2M Dataset Card
## Dataset details
**Dataset type:**
ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data.
It is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities.
* sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V).
* share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT).
* sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage.
**Dataset date:**
ShareGPT4V Captions 1.2M was collected in 11.07 2023.
**Paper or resources for more information:**
[[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)]
**License:**
Attribution-NonCommercial 4.0 International
It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use
## Intended use
**Primary intended uses:**
The primary use of ShareGPT4V Captions 1.2M is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
| Lin-Chen/ShareGPT4V | [
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| 2023-11-20T09:29:12+00:00 | {"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["1M<n"], "task_categories": ["visual-question-answering", "question-answering", "conversational"], "pretty_name": "ShareGPT4V Captions 1.2M Dataset Card", "configs": [{"config_name": "ShareGPT4V", "data_files": "sharegpt4v_instruct_gpt4-vision_cap100k.json"}, {"config_name": "ShareGPT4V-PT", "data_files": "share-captioner_coco_lcs_sam_1246k_1107.json"}]} | 2023-11-22T15:48:45+00:00 | [
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|
# ShareGPT4V 1.2M Dataset Card
## Dataset details
Dataset type:
ShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data.
It is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities.
* sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V).
* share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT).
* sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage.
Dataset date:
ShareGPT4V Captions 1.2M was collected in 11.07 2023.
Paper or resources for more information:
[Project] [Paper] [Code]
License:
Attribution-NonCommercial 4.0 International
It should abide by the policy of OpenAI: URL
## Intended use
Primary intended uses:
The primary use of ShareGPT4V Captions 1.2M is research on large multimodal models and chatbots.
Primary intended users:
The primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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"## Dataset details\n\nDataset type:\nShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data.\n\nIt is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities.\n\n* sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V).\n* share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT).\n* sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage.\n\nDataset date:\nShareGPT4V Captions 1.2M was collected in 11.07 2023.\n\nPaper or resources for more information:\n[Project] [Paper] [Code]\n\nLicense:\nAttribution-NonCommercial 4.0 International\nIt should abide by the policy of OpenAI: URL",
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"## Dataset details\n\nDataset type:\nShareGPT4V Captions 1.2M is a set of GPT4-Vision-powered multi-modal captions data.\n\nIt is constructed to enhance modality alignment and fine-grained visual concept perception in Large Multi-Modal Models (LMMs) during both the pre-training and supervised fine-tuning stages. This advancement aims to bring LMMs towards GPT4-Vision capabilities.\n\n* sharegpt4v_instruct_gpt4-vision_cap100k.json is generated by GPT4-Vision (ShareGPT4V).\n* share-captioner_coco_lcs_sam_1246k_1107.json is generated by our Share-Captioner trained on GPT4-Vision-generated data (ShareGPT4V-PT).\n* sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json is curated from sharegpt4v_instruct_gpt4-vision_cap100k.json for the supervised fine-tuning stage.\n\nDataset date:\nShareGPT4V Captions 1.2M was collected in 11.07 2023.\n\nPaper or resources for more information:\n[Project] [Paper] [Code]\n\nLicense:\nAttribution-NonCommercial 4.0 International\nIt should abide by the policy of OpenAI: URL",
"## Intended use\nPrimary intended uses:\nThe primary use of ShareGPT4V Captions 1.2M is research on large multimodal models and chatbots.\n\nPrimary intended users:\nThe primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence."
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58e1f291b978de257580c24ad8be97eb1696f7fa | # Dataset Card for "oct-object-detection-v3-average"
Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels.
[Source datataset](https://doi.org/10.1101/2023.03.29.534704)
| joseluhf11/oct-object-detection-v3-average | [
"region:us"
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| 2023-11-20T09:29:49+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "objects", "struct": [{"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "categories", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 154014595.25, "num_examples": 1246}], "download_size": 71641492, "dataset_size": 154014595.25}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-22T08:48:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "oct-object-detection-v3-average"
Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels.
Source datataset
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b05eacd8325aa991a7010719656e1fc89a4ca58c | # Dataset Card for "watches-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rjaiswal/watches-dataset | [
"region:us"
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| 2023-11-20T09:43:06+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16329292.0, "num_examples": 186}], "download_size": 0, "dataset_size": 16329292.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-23T10:57:36+00:00 | []
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More Information needed | [
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334ad7d4d008421be5a9b098dd256951eba1a5c5 |
# Dataset Card for PKU-SafeRLHF
<span style="color: red;">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members. </span>
## Dataset Summary
The preference dataset consists of 30k+ expert comparison data. Each entry in this dataset includes two responses to a question, along with safety meta-labels and preferences for both responses, taking into consideration their helpfulness and harmlessness.
The annotation pipeline for this dataset is depicted in the following image:
## Human-Preference on Harmlessness and Helpfulness
### Harmlessness of a QA Pair
The harmlessness of a QA pair is evaluated based on ethical considerations and safety implications inherent in the interaction.
A QA pair is deemed harmless when it is classified as risk-neutral across all 14 harm categories outlined in our paper.
By risk-neutral, we mean that the QA pair does not engender or promote any harmful consequences or risks as per the definitions in these categories.
Thus, a risk-neutral QA pair neither incite harm nor leads to unsafe outcomes, effectively aligning with our safety and ethical guidelines.
### Helpfulness of a Response
The helpfulness of a response pertains to how effectively it addresses a given prompt. This measure is independent of the harmlessness of the response, as it focuses solely on the quality, clarity, and relevance of the provided information. Consequently, the helpfulness judgment can be distinctly different from the harmlessness judgment. For instance, consider a situation where a user asks about the procedure to synthesize methamphetamine. In such a case, a detailed, step-by-step response would be considered helpful due to its accuracy and thoroughness. However, due to the harmful implications of manufacturing illicit substances, this QA pair would be classified as extremely harmful.
### Ranking of Responses
Once the helpfulness and harmlessness of responses are evaluated, they are ranked accordingly. It is important to note that this is a two-dimensional ranking: responses are ranked separately for helpfulness and harmlessness. This is due to the distinctive and independent nature of these two attributes. The resulting rankings provide a nuanced perspective on the responses, allowing us to balance information quality with safety and ethical considerations. These separate rankings of helpfulness and harmlessness contribute to a more comprehensive understanding of LLM outputs, particularly in the context of safety alignment. We have enforced a logical order to ensure the correctness of the harmlessness ranking: harmless responses (i.e. all 14 harm categories risk-neutral) are always ranked higher than harmful ones (i.e., at least 1 category risky).
## Usage
To load our dataset, use the `load_dataset()` function as follows:
```python
from datasets import load_dataset
dataset = load_dataset("PKU-Alignment/PKU-SafeRLHF-30K")
```
## Paper
You can find more information in our paper
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
## Contact
The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails.
| PKU-Alignment/PKU-SafeRLHF-30K | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"llm",
"lm",
"human-feedback",
"rlhf",
"safe-rlhf",
"arxiv:2307.04657",
"region:us"
]
| 2023-11-20T10:20:10+00:00 | {"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "tags": ["safe", "safety", "ai-safety", "llm", "lm", "human-feedback", "rlhf", "safe-rlhf"]} | 2023-11-20T10:23:37+00:00 | [
"2307.04657"
]
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #safe #safety #ai-safety #llm #lm #human-feedback #rlhf #safe-rlhf #arxiv-2307.04657 #region-us
|
# Dataset Card for PKU-SafeRLHF
<span style="color: red;">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members. </span>
## Dataset Summary
The preference dataset consists of 30k+ expert comparison data. Each entry in this dataset includes two responses to a question, along with safety meta-labels and preferences for both responses, taking into consideration their helpfulness and harmlessness.
The annotation pipeline for this dataset is depicted in the following image:
!Annotation Pipeline
## Human-Preference on Harmlessness and Helpfulness
### Harmlessness of a QA Pair
The harmlessness of a QA pair is evaluated based on ethical considerations and safety implications inherent in the interaction.
A QA pair is deemed harmless when it is classified as risk-neutral across all 14 harm categories outlined in our paper.
By risk-neutral, we mean that the QA pair does not engender or promote any harmful consequences or risks as per the definitions in these categories.
Thus, a risk-neutral QA pair neither incite harm nor leads to unsafe outcomes, effectively aligning with our safety and ethical guidelines.
### Helpfulness of a Response
The helpfulness of a response pertains to how effectively it addresses a given prompt. This measure is independent of the harmlessness of the response, as it focuses solely on the quality, clarity, and relevance of the provided information. Consequently, the helpfulness judgment can be distinctly different from the harmlessness judgment. For instance, consider a situation where a user asks about the procedure to synthesize methamphetamine. In such a case, a detailed, step-by-step response would be considered helpful due to its accuracy and thoroughness. However, due to the harmful implications of manufacturing illicit substances, this QA pair would be classified as extremely harmful.
### Ranking of Responses
Once the helpfulness and harmlessness of responses are evaluated, they are ranked accordingly. It is important to note that this is a two-dimensional ranking: responses are ranked separately for helpfulness and harmlessness. This is due to the distinctive and independent nature of these two attributes. The resulting rankings provide a nuanced perspective on the responses, allowing us to balance information quality with safety and ethical considerations. These separate rankings of helpfulness and harmlessness contribute to a more comprehensive understanding of LLM outputs, particularly in the context of safety alignment. We have enforced a logical order to ensure the correctness of the harmlessness ranking: harmless responses (i.e. all 14 harm categories risk-neutral) are always ranked higher than harmful ones (i.e., at least 1 category risky).
## Usage
To load our dataset, use the 'load_dataset()' function as follows:
## Paper
You can find more information in our paper
- Dataset Paper: <URL
## Contact
The original authors host this dataset on GitHub here: URL
| [
"# Dataset Card for PKU-SafeRLHF\n\n<span style=\"color: red;\">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members. </span>",
"## Dataset Summary\n\nThe preference dataset consists of 30k+ expert comparison data. Each entry in this dataset includes two responses to a question, along with safety meta-labels and preferences for both responses, taking into consideration their helpfulness and harmlessness.\n\nThe annotation pipeline for this dataset is depicted in the following image:\n\n!Annotation Pipeline",
"## Human-Preference on Harmlessness and Helpfulness",
"### Harmlessness of a QA Pair\n\nThe harmlessness of a QA pair is evaluated based on ethical considerations and safety implications inherent in the interaction.\nA QA pair is deemed harmless when it is classified as risk-neutral across all 14 harm categories outlined in our paper.\nBy risk-neutral, we mean that the QA pair does not engender or promote any harmful consequences or risks as per the definitions in these categories.\nThus, a risk-neutral QA pair neither incite harm nor leads to unsafe outcomes, effectively aligning with our safety and ethical guidelines.",
"### Helpfulness of a Response\n\nThe helpfulness of a response pertains to how effectively it addresses a given prompt. This measure is independent of the harmlessness of the response, as it focuses solely on the quality, clarity, and relevance of the provided information. Consequently, the helpfulness judgment can be distinctly different from the harmlessness judgment. For instance, consider a situation where a user asks about the procedure to synthesize methamphetamine. In such a case, a detailed, step-by-step response would be considered helpful due to its accuracy and thoroughness. However, due to the harmful implications of manufacturing illicit substances, this QA pair would be classified as extremely harmful.",
"### Ranking of Responses\n\nOnce the helpfulness and harmlessness of responses are evaluated, they are ranked accordingly. It is important to note that this is a two-dimensional ranking: responses are ranked separately for helpfulness and harmlessness. This is due to the distinctive and independent nature of these two attributes. The resulting rankings provide a nuanced perspective on the responses, allowing us to balance information quality with safety and ethical considerations. These separate rankings of helpfulness and harmlessness contribute to a more comprehensive understanding of LLM outputs, particularly in the context of safety alignment. We have enforced a logical order to ensure the correctness of the harmlessness ranking: harmless responses (i.e. all 14 harm categories risk-neutral) are always ranked higher than harmful ones (i.e., at least 1 category risky).",
"## Usage\n\nTo load our dataset, use the 'load_dataset()' function as follows:",
"## Paper\n\nYou can find more information in our paper\n\n- Dataset Paper: <URL",
"## Contact\n\nThe original authors host this dataset on GitHub here: URL"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #safe #safety #ai-safety #llm #lm #human-feedback #rlhf #safe-rlhf #arxiv-2307.04657 #region-us \n",
"# Dataset Card for PKU-SafeRLHF\n\n<span style=\"color: red;\">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members. </span>",
"## Dataset Summary\n\nThe preference dataset consists of 30k+ expert comparison data. Each entry in this dataset includes two responses to a question, along with safety meta-labels and preferences for both responses, taking into consideration their helpfulness and harmlessness.\n\nThe annotation pipeline for this dataset is depicted in the following image:\n\n!Annotation Pipeline",
"## Human-Preference on Harmlessness and Helpfulness",
"### Harmlessness of a QA Pair\n\nThe harmlessness of a QA pair is evaluated based on ethical considerations and safety implications inherent in the interaction.\nA QA pair is deemed harmless when it is classified as risk-neutral across all 14 harm categories outlined in our paper.\nBy risk-neutral, we mean that the QA pair does not engender or promote any harmful consequences or risks as per the definitions in these categories.\nThus, a risk-neutral QA pair neither incite harm nor leads to unsafe outcomes, effectively aligning with our safety and ethical guidelines.",
"### Helpfulness of a Response\n\nThe helpfulness of a response pertains to how effectively it addresses a given prompt. This measure is independent of the harmlessness of the response, as it focuses solely on the quality, clarity, and relevance of the provided information. Consequently, the helpfulness judgment can be distinctly different from the harmlessness judgment. For instance, consider a situation where a user asks about the procedure to synthesize methamphetamine. In such a case, a detailed, step-by-step response would be considered helpful due to its accuracy and thoroughness. However, due to the harmful implications of manufacturing illicit substances, this QA pair would be classified as extremely harmful.",
"### Ranking of Responses\n\nOnce the helpfulness and harmlessness of responses are evaluated, they are ranked accordingly. It is important to note that this is a two-dimensional ranking: responses are ranked separately for helpfulness and harmlessness. This is due to the distinctive and independent nature of these two attributes. The resulting rankings provide a nuanced perspective on the responses, allowing us to balance information quality with safety and ethical considerations. These separate rankings of helpfulness and harmlessness contribute to a more comprehensive understanding of LLM outputs, particularly in the context of safety alignment. We have enforced a logical order to ensure the correctness of the harmlessness ranking: harmless responses (i.e. all 14 harm categories risk-neutral) are always ranked higher than harmful ones (i.e., at least 1 category risky).",
"## Usage\n\nTo load our dataset, use the 'load_dataset()' function as follows:",
"## Paper\n\nYou can find more information in our paper\n\n- Dataset Paper: <URL",
"## Contact\n\nThe original authors host this dataset on GitHub here: URL"
]
| [
82,
91,
84,
14,
148,
167,
206,
24,
17,
17
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #safe #safety #ai-safety #llm #lm #human-feedback #rlhf #safe-rlhf #arxiv-2307.04657 #region-us \n# Dataset Card for PKU-SafeRLHF\n\n<span style=\"color: red;\">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members. </span>## Dataset Summary\n\nThe preference dataset consists of 30k+ expert comparison data. Each entry in this dataset includes two responses to a question, along with safety meta-labels and preferences for both responses, taking into consideration their helpfulness and harmlessness.\n\nThe annotation pipeline for this dataset is depicted in the following image:\n\n!Annotation Pipeline## Human-Preference on Harmlessness and Helpfulness### Harmlessness of a QA Pair\n\nThe harmlessness of a QA pair is evaluated based on ethical considerations and safety implications inherent in the interaction.\nA QA pair is deemed harmless when it is classified as risk-neutral across all 14 harm categories outlined in our paper.\nBy risk-neutral, we mean that the QA pair does not engender or promote any harmful consequences or risks as per the definitions in these categories.\nThus, a risk-neutral QA pair neither incite harm nor leads to unsafe outcomes, effectively aligning with our safety and ethical guidelines."
]
|
c6f8761f4e5dfc70cf00b331f051bec356ab3fa1 | # CPJKU/openmic
The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset.
This dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded.
The mp3 bytes can be decoded quickly using for [example](https://github.com/kkoutini/PaSST/blob/4519e4605989b8c2e62dccb5b928af9bf7bf8602/audioset/dataset.py#L55) or [minimp3](https://github.com/f0k/minimp3py).
Take a look at the original dataset for more information.
The original dataset contains the following:
10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key)
VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key)
MD5 checksums for each OGG and JSON file
Anonymized individual responses, in 'openmic-2018-individual-responses.csv'
Aggregated labels, in 'openmic-2018-aggregated-labels.csv'
Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv'
A Python-friendly NPZ file of features and labels, 'openmic-2018.npz'
Sample partitions for train and test, in 'partitions/*.txt'
## Homepage
https://zenodo.org/records/1432913
## Citation
```
Humphrey, Eric J., Durand, Simon, and McFee, Brian. "OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition." in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018.
```
## License
CC BY 4.0
| CPJKU/openmic | [
"region:us"
]
| 2023-11-20T10:42:04+00:00 | {"dataset_info": {"features": [{"name": "filename", "dtype": "string"}, {"name": "true", "sequence": "float32", "length": 20}, {"name": "mask", "sequence": "int32", "length": 20}, {"name": "mp3_bytes", "dtype": "binary"}], "splits": [{"name": "train", "num_bytes": 1790991884, "num_examples": 14915}, {"name": "test", "num_bytes": 611455142, "num_examples": 5085}], "download_size": 0, "dataset_size": 2402447026}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/shard_train_*"}, {"split": "test", "path": "data/shard_test_*"}]}]} | 2023-11-20T10:43:58+00:00 | []
| []
| TAGS
#region-us
| # CPJKU/openmic
The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset.
This dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded.
The mp3 bytes can be decoded quickly using for example or minimp3.
Take a look at the original dataset for more information.
The original dataset contains the following:
10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key)
VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key)
MD5 checksums for each OGG and JSON file
Anonymized individual responses, in 'URL'
Aggregated labels, in 'URL'
Track metadata, with licenses for each audio recording, in 'URL'
A Python-friendly NPZ file of features and labels, 'URL'
Sample partitions for train and test, in 'partitions/*.txt'
## Homepage
URL
## License
CC BY 4.0
| [
"# CPJKU/openmic\nThe dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset.\n\nThis dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded.\nThe mp3 bytes can be decoded quickly using for example or minimp3.\n\nTake a look at the original dataset for more information.\nThe original dataset contains the following:\n\n10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key)\nVGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key)\nMD5 checksums for each OGG and JSON file\nAnonymized individual responses, in 'URL'\nAggregated labels, in 'URL'\nTrack metadata, with licenses for each audio recording, in 'URL'\nA Python-friendly NPZ file of features and labels, 'URL'\nSample partitions for train and test, in 'partitions/*.txt'",
"## Homepage\n URL",
"## License\n CC BY 4.0"
]
| [
"TAGS\n#region-us \n",
"# CPJKU/openmic\nThe dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset.\n\nThis dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded.\nThe mp3 bytes can be decoded quickly using for example or minimp3.\n\nTake a look at the original dataset for more information.\nThe original dataset contains the following:\n\n10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key)\nVGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key)\nMD5 checksums for each OGG and JSON file\nAnonymized individual responses, in 'URL'\nAggregated labels, in 'URL'\nTrack metadata, with licenses for each audio recording, in 'URL'\nA Python-friendly NPZ file of features and labels, 'URL'\nSample partitions for train and test, in 'partitions/*.txt'",
"## Homepage\n URL",
"## License\n CC BY 4.0"
]
| [
6,
282,
3,
5
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"passage: TAGS\n#region-us \n# CPJKU/openmic\nThe dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset.\n\nThis dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded.\nThe mp3 bytes can be decoded quickly using for example or minimp3.\n\nTake a look at the original dataset for more information.\nThe original dataset contains the following:\n\n10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key)\nVGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key)\nMD5 checksums for each OGG and JSON file\nAnonymized individual responses, in 'URL'\nAggregated labels, in 'URL'\nTrack metadata, with licenses for each audio recording, in 'URL'\nA Python-friendly NPZ file of features and labels, 'URL'\nSample partitions for train and test, in 'partitions/*.txt'## Homepage\n URL## License\n CC BY 4.0"
]
|
7a35c7ca945bc075ed1903eded95c17b4fc411da | The PolyU-COMP-Information is a dataset about the department of computing in PolyU, which contains 370 rows question and answering data. | Tsinggu/PolyU-COMP-Information | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"region:us"
]
| 2023-11-20T10:56:01+00:00 | {"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["question-answering"]} | 2023-11-20T11:25:22+00:00 | []
| [
"en"
]
| TAGS
#task_categories-question-answering #size_categories-n<1K #language-English #region-us
| The PolyU-COMP-Information is a dataset about the department of computing in PolyU, which contains 370 rows question and answering data. | []
| [
"TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #region-us \n"
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|
0c7d06c9bd88bdb4be832b9f66ffc0dc3f5ca565 |
# Dataset Description
The provided dataset includes **11430** URLs with **87** extracted features.
The dataset are designed to be used as a benchmark for machine learning based **phishing detection** systems.
The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs.
Features are from three different classes:
- **56** extracted from the structure and syntax of URLs
- **24** extracted from the content of their correspondent pages
- **7** are extracetd by querying external services.
The dataset was partitioned randomly into training and testing sets, with a ratio of **two-thirds for training** and **one-third for testing**.
## Details
- **Funded by:** Abdelhakim Hannousse, Salima Yahiouche
- **Shared by:** [pirocheto](https://github.com/pirocheto)
- **License:** [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
- **Paper:** [https://arxiv.org/abs/2010.12847](https://arxiv.org/abs/2010.12847)
## Source Data
The diagram below illustrates the procedure for creating the corpus.
For details, please refer to the paper.
<div align="center">
<img src="images/source_data.png" alt="Diagram source data">
</div>
<p align="center">
<em>Source: Extract form the <a href="https://arxiv.org/abs/2010.12847">paper</a></em>
</p>
## Load Dataset
- With **datasets**:
```python
from datasets import load_dataset
dataset = load_dataset("pirocheto/phishing-url")
```
- With **pandas** and **huggingface_hub**:
```python
import pandas as pd
from huggingface_hub import hf_hub_download
REPO_ID = "pirocheto/phishing-url"
FILENAME = "data/train.parquet"
df = pd.read_parquet(
hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset")
)
```
- With **pandas** only:
```python
import pandas as pd
url = "https://huggingface.co/datasets/pirocheto/phishing-url/resolve/main/data/train.parquet"
df = pd.read_parquet(url)
```
## Citation
To give credit to the creators of this dataset, please use the following citation in your work:
- BibTeX format
```
@article{Hannousse_2021,
title={Towards benchmark datasets for machine learning based website phishing detection: An experimental study},
volume={104},
ISSN={0952-1976},
url={http://dx.doi.org/10.1016/j.engappai.2021.104347},
DOI={10.1016/j.engappai.2021.104347},
journal={Engineering Applications of Artificial Intelligence},
publisher={Elsevier BV},
author={Hannousse, Abdelhakim and Yahiouche, Salima},
year={2021},
month=sep, pages={104347} }
```
- APA format
```
Hannousse, A., & Yahiouche, S. (2021).
Towards benchmark datasets for machine learning based website phishing detection: An experimental study.
Engineering Applications of Artificial Intelligence, 104, 104347.
```
| pirocheto/phishing-url | [
"task_categories:text-classification",
"task_categories:tabular-classification",
"annotations_creators:found",
"size_categories:n<1K",
"license:cc-by-4.0",
"phishing",
"url",
"security",
"arxiv:2010.12847",
"region:us"
]
| 2023-11-20T11:30:52+00:00 | {"annotations_creators": ["found"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "task_categories": ["text-classification", "tabular-classification"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train.parquet"}, {"split": "test", "path": "data/test.parquet"}]}], "tags": ["phishing", "url", "security"]} | 2023-11-24T20:00:01+00:00 | [
"2010.12847"
]
| []
| TAGS
#task_categories-text-classification #task_categories-tabular-classification #annotations_creators-found #size_categories-n<1K #license-cc-by-4.0 #phishing #url #security #arxiv-2010.12847 #region-us
|
# Dataset Description
The provided dataset includes 11430 URLs with 87 extracted features.
The dataset are designed to be used as a benchmark for machine learning based phishing detection systems.
The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs.
Features are from three different classes:
- 56 extracted from the structure and syntax of URLs
- 24 extracted from the content of their correspondent pages
- 7 are extracetd by querying external services.
The dataset was partitioned randomly into training and testing sets, with a ratio of two-thirds for training and one-third for testing.
## Details
- Funded by: Abdelhakim Hannousse, Salima Yahiouche
- Shared by: pirocheto
- License: CC-BY-4.0
- Paper: URL
## Source Data
The diagram below illustrates the procedure for creating the corpus.
For details, please refer to the paper.
<div align="center">
<img src="images/source_data.png" alt="Diagram source data">
</div>
<p align="center">
<em>Source: Extract form the <a href="URL
</p>
## Load Dataset
- With datasets:
- With pandas and huggingface_hub:
- With pandas only:
To give credit to the creators of this dataset, please use the following citation in your work:
- BibTeX format
- APA format
| [
"# Dataset Description\n\nThe provided dataset includes 11430 URLs with 87 extracted features. \nThe dataset are designed to be used as a benchmark for machine learning based phishing detection systems. \nThe datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. \n\nFeatures are from three different classes:\n- 56 extracted from the structure and syntax of URLs\n- 24 extracted from the content of their correspondent pages\n- 7 are extracetd by querying external services.\n\n\nThe dataset was partitioned randomly into training and testing sets, with a ratio of two-thirds for training and one-third for testing.",
"## Details\n\n- Funded by: Abdelhakim Hannousse, Salima Yahiouche\n- Shared by: pirocheto\n- License: CC-BY-4.0\n- Paper: URL",
"## Source Data\n\nThe diagram below illustrates the procedure for creating the corpus. \nFor details, please refer to the paper.\n\n<div align=\"center\">\n <img src=\"images/source_data.png\" alt=\"Diagram source data\">\n</div>\n\n<p align=\"center\">\n <em>Source: Extract form the <a href=\"URL\n</p>",
"## Load Dataset\n\n- With datasets:\n\n\n- With pandas and huggingface_hub:\n\n\n- With pandas only:\n\n\nTo give credit to the creators of this dataset, please use the following citation in your work:\n\n- BibTeX format\n\n\n\n- APA format"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-tabular-classification #annotations_creators-found #size_categories-n<1K #license-cc-by-4.0 #phishing #url #security #arxiv-2010.12847 #region-us \n",
"# Dataset Description\n\nThe provided dataset includes 11430 URLs with 87 extracted features. \nThe dataset are designed to be used as a benchmark for machine learning based phishing detection systems. \nThe datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. \n\nFeatures are from three different classes:\n- 56 extracted from the structure and syntax of URLs\n- 24 extracted from the content of their correspondent pages\n- 7 are extracetd by querying external services.\n\n\nThe dataset was partitioned randomly into training and testing sets, with a ratio of two-thirds for training and one-third for testing.",
"## Details\n\n- Funded by: Abdelhakim Hannousse, Salima Yahiouche\n- Shared by: pirocheto\n- License: CC-BY-4.0\n- Paper: URL",
"## Source Data\n\nThe diagram below illustrates the procedure for creating the corpus. \nFor details, please refer to the paper.\n\n<div align=\"center\">\n <img src=\"images/source_data.png\" alt=\"Diagram source data\">\n</div>\n\n<p align=\"center\">\n <em>Source: Extract form the <a href=\"URL\n</p>",
"## Load Dataset\n\n- With datasets:\n\n\n- With pandas and huggingface_hub:\n\n\n- With pandas only:\n\n\nTo give credit to the creators of this dataset, please use the following citation in your work:\n\n- BibTeX format\n\n\n\n- APA format"
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"passage: TAGS\n#task_categories-text-classification #task_categories-tabular-classification #annotations_creators-found #size_categories-n<1K #license-cc-by-4.0 #phishing #url #security #arxiv-2010.12847 #region-us \n# Dataset Description\n\nThe provided dataset includes 11430 URLs with 87 extracted features. \nThe dataset are designed to be used as a benchmark for machine learning based phishing detection systems. \nThe datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs. \n\nFeatures are from three different classes:\n- 56 extracted from the structure and syntax of URLs\n- 24 extracted from the content of their correspondent pages\n- 7 are extracetd by querying external services.\n\n\nThe dataset was partitioned randomly into training and testing sets, with a ratio of two-thirds for training and one-third for testing.## Details\n\n- Funded by: Abdelhakim Hannousse, Salima Yahiouche\n- Shared by: pirocheto\n- License: CC-BY-4.0\n- Paper: URL## Source Data\n\nThe diagram below illustrates the procedure for creating the corpus. \nFor details, please refer to the paper.\n\n<div align=\"center\">\n <img src=\"images/source_data.png\" alt=\"Diagram source data\">\n</div>\n\n<p align=\"center\">\n <em>Source: Extract form the <a href=\"URL\n</p>## Load Dataset\n\n- With datasets:\n\n\n- With pandas and huggingface_hub:\n\n\n- With pandas only:\n\n\nTo give credit to the creators of this dataset, please use the following citation in your work:\n\n- BibTeX format\n\n\n\n- APA format"
]
|
422fcc2cefacd8ad7e82c733a321edd1d01000f9 | # Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
**Paper or resources for more information:**
[[Paper](https://huggingface.co/papers/2311.08046)] [[Code](https://github.com/PKU-YuanGroup/Chat-UniVi)] | Chat-UniVi/Chat-UniVi-Instruct | [
"license:apache-2.0",
"arxiv:2311.08046",
"region:us"
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| 2023-11-20T11:43:22+00:00 | {"license": "apache-2.0"} | 2023-11-23T02:17:47+00:00 | [
"2311.08046"
]
| []
| TAGS
#license-apache-2.0 #arxiv-2311.08046 #region-us
| # Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Paper or resources for more information:
[Paper] [Code] | [
"# Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding\n\nPaper or resources for more information:\n[Paper] [Code]"
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]
|
cafa8b3e628f50981df485acee4801fe601264d8 |
# Dataset Card for Black-box Prompt Optimization (BPO)
## Dataset Description
- **Repository:** https://github.com/thu-coai/BPO
- **Paper:** https://arxiv.org/abs/2311.04155
- **Model:**
### Data Summary
To advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's `gpt-3.5-turbo` engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.
Please refer to our [paper](https://arxiv.org/abs/2311.04155) for further details.
### Supported Tasks
The BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.
### Language
The data in BPO are in English.
## Data Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"prompt": "Minimize the following data set\n[3, 4, 6, 7, 8, 10]",
"optimized_prompt": "Provide a step-by-step solution to minimize the following data set: [3, 4, 6, 7, 8, 10].",
"good_res": "To minimize the values in the given data set, which is a list of numbers, you could sort them in ascending order as follows: [3, 4, 6, 7, 8, 10]. The minimized (least) value in this data set would be 3.",
"bad_res": "[3, 4, 6, 7, 8, 10]"
}
```
### Data Fields
The data fields are as follows:
* `prompt`: the original user input
* `optimized_prompt`: the optimized prompt based on feedback
* `good_res`: human-chosen (preferred) response
* `bad_res`: human-rejected response
### Data Splits
| | train | valid | test |
|---------------|------:|------:|------:|
| BPO | 13895 | 300 | 200 |
## Additional Information
### Source Data
Our feedback data come from the following open-sourced datasets:
- [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Alpaca-GPT4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/blob/main/data/comparison_data_v2.json)
- [Chatbot Arena Conversation](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
### Other Known Limitations
- Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.
- Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.
- Optimized Prompts: The optimized prompts are auto-generated by `gpt-3.5-turbo` based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct.
### Citation Information
```
@article{cheng2023black,
title={Black-Box Prompt Optimization: Aligning Large Language Models without Model Training},
author={Cheng, Jiale and Liu, Xiao and Zheng, Kehan and Ke, Pei and Wang, Hongning and Dong, Yuxiao and Tang, Jie and Huang, Minlie},
journal={arXiv preprint arXiv:2311.04155},
year={2023}
}
``` | THUDM/BPO | [
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| 2023-11-20T11:46:58+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "BPO", "tags": ["human_feedback"]} | 2023-11-20T11:49:55+00:00 | [
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]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #human_feedback #arxiv-2311.04155 #region-us
| Dataset Card for Black-box Prompt Optimization (BPO)
====================================================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Model:
### Data Summary
To advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's 'gpt-3.5-turbo' engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.
Please refer to our paper for further details.
### Supported Tasks
The BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.
### Language
The data in BPO are in English.
Data Structure
--------------
### Data Instances
An example of "train" looks as follows:
### Data Fields
The data fields are as follows:
* 'prompt': the original user input
* 'optimized\_prompt': the optimized prompt based on feedback
* 'good\_res': human-chosen (preferred) response
* 'bad\_res': human-rejected response
### Data Splits
Additional Information
----------------------
### Source Data
Our feedback data come from the following open-sourced datasets:
* OASST1
* hh-rlhf
* Alpaca-GPT4
* Chatbot Arena Conversation
### Other Known Limitations
* Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.
* Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.
* Optimized Prompts: The optimized prompts are auto-generated by 'gpt-3.5-turbo' based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct.
| [
"### Data Summary\n\n\nTo advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's 'gpt-3.5-turbo' engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.\n\n\nPlease refer to our paper for further details.",
"### Supported Tasks\n\n\nThe BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.",
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"### Data Splits\n\n\n\nAdditional Information\n----------------------",
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"### Supported Tasks\n\n\nThe BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.",
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"### Other Known Limitations\n\n\n* Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.\n* Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.\n* Optimized Prompts: The optimized prompts are auto-generated by 'gpt-3.5-turbo' based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct."
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"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #human_feedback #arxiv-2311.04155 #region-us \n### Data Summary\n\n\nTo advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's 'gpt-3.5-turbo' engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.\n\n\nPlease refer to our paper for further details.### Supported Tasks\n\n\nThe BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.### Language\n\n\nThe data in BPO are in English.\n\n\nData Structure\n--------------### Data Instances\n\n\nAn example of \"train\" looks as follows:### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'prompt': the original user input\n* 'optimized\\_prompt': the optimized prompt based on feedback\n* 'good\\_res': human-chosen (preferred) response\n* 'bad\\_res': human-rejected response### Data Splits\n\n\n\nAdditional Information\n----------------------### Source Data\n\n\nOur feedback data come from the following open-sourced datasets:\n\n\n* OASST1\n* hh-rlhf\n* Alpaca-GPT4\n* Chatbot Arena Conversation"
]
|
2b0ba914da9f66034e546e24e0c69f3dd3e1ad1f | # Dataset Card for "find_marker_before_sent_train_100_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_before_sent_train_100_eval_40 | [
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| 2023-11-20T11:48:18+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"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 740120, "num_examples": 642}, {"name": "validation", "num_bytes": 206534, "num_examples": 201}], "download_size": 0, "dataset_size": 946654}} | 2023-11-20T11:58:22+00:00 | []
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| # Dataset Card for "find_marker_before_sent_train_100_eval_40"
More Information needed | [
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52f7ae23a717362a0e9eac3b595d00cfaa2585f9 | # Dataset Card for "find_marker_before_sent_train_200_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_before_sent_train_200_eval_40 | [
"region:us"
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| 2023-11-20T11:48:59+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"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1450283, "num_examples": 1260}, {"name": "validation", "num_bytes": 218272, "num_examples": 203}], "download_size": 0, "dataset_size": 1668555}} | 2023-11-20T11:59:10+00:00 | []
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More Information needed | [
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b1143b13c2666c92ed876ce213554182b3e91f29 | # Dataset Card for "find_marker_after_sent_train_100_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_after_sent_train_100_eval_40 | [
"region:us"
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| 2023-11-20T11:51:10+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"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 739816, "num_examples": 641}, {"name": "validation", "num_bytes": 204879, "num_examples": 199}], "download_size": 218325, "dataset_size": 944695}} | 2023-11-20T11:58:49+00:00 | []
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5a6f457156816a2a021d7660d64c9c091e9f3d82 | # Dataset Card for "find_marker_after_sent_train_200_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_after_sent_train_200_eval_40 | [
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| 2023-11-20T11:51:58+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"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1445507, "num_examples": 1254}, {"name": "validation", "num_bytes": 214957, "num_examples": 198}], "download_size": 351050, "dataset_size": 1660464}} | 2023-11-20T11:59:41+00:00 | []
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More Information needed | [
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59b255b9fada8fe1f9ce1c8c8fc9934b163ee149 | # Dataset Card for "find_marker_before_sent_train_400_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_before_sent_train_400_eval_40 | [
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| 2023-11-20T11:52:23+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"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2782863, "num_examples": 2428}, {"name": "validation", "num_bytes": 215266, "num_examples": 200}], "download_size": 0, "dataset_size": 2998129}} | 2023-11-20T12:00:02+00:00 | []
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More Information needed | [
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52c2fa0386eb5fa806b4c5b985cd1b78471ecaf9 | # Dataset Card for "find_marker_after_sent_train_400_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/find_marker_after_sent_train_400_eval_40 | [
"region:us"
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| 2023-11-20T11:52:49+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"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2771239, "num_examples": 2412}, {"name": "validation", "num_bytes": 213176, "num_examples": 197}], "download_size": 591587, "dataset_size": 2984415}} | 2023-11-20T12:00:32+00:00 | []
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8b3ee744ebd991803befee3a193a104b0927586b | # Dataset Card for "exams_van"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vlsp-2023-vllm/exams_van | [
"region:us"
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| 2023-11-20T12:06:13+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "grade", "dtype": "string"}, {"name": "subject", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1481304.5274812179, "num_examples": 3550}], "download_size": 719931, "dataset_size": 1481304.5274812179}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2023-11-20T12:06:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "exams_van"
More Information needed | [
"# Dataset Card for \"exams_van\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
950d60ada319e52a396e4eef8de22ed1a5b0d95c |
# Dataset Card for RuIzardEmotions
## 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 Summary
The RuIzardEmotions dataset is a high-quality translation of the [go-emotions](https://huggingface.co/datasets/go_emotions) dataset and the other [emotion-detection](https://www.kaggle.com/datasets/ishantjuyal/emotions-in-text/data) dataset. It contains 30k Reddit comments labeled for 10 emotion categories (__joy__, __sadness__, __anger__, __enthusiasm__, __surprise__, __disgust__, __fear__, __guilt__, __shame__ and __neutral__).
The datasets were translated using the accurate translator [DeepL](https://www.deepl.com/translator) and additional processing. The idea for the dataset was inspired by the [Izard's model](https://en.wikipedia.org/wiki/Differential_Emotions_Scale) of human emotions.
The dataset already with predefined train/val/test splits.
### Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
### Languages
The data is in Russian.
## Dataset Structure
### Data Instances
Each instance is a reddit comment with one or more emotion annotations (or neutral).
### Data Splits
The simplified data includes a set of train/val/test splits with 24k, 3k, and 3k examples respectively.
## Considerations for Using the Data
### Social Impact of Dataset
Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer
interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases
to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance
pricing, and student attentiveness
## Additional Information
### Licensing Information
The GitHub repository which houses this dataset has an
[Apache License 2.0](https://github.com/Djacon/russian-emotion-detection/blob/main/LICENSE).
### Citation Information
```
@inproceedings{Djacon,
author={Djacon},
title={RuIzardEmotions: A Dataset of Fine-Grained Emotions},
year={2023}
}
``` | Djacon/ru-izard-emotions | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"multilinguality:russian",
"size_categories:10K<n<100K",
"language:ru",
"license:mit",
"emotion",
"region:us"
]
| 2023-11-20T12:08:50+00:00 | {"language": ["ru"], "license": ["mit"], "multilinguality": ["russian"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification", "multi-class-classification", "multi-label-classification"], "pretty_name": "RuIzardEmotions", "tags": ["emotion"]} | 2023-11-23T19:17:45+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #multilinguality-russian #size_categories-10K<n<100K #language-Russian #license-mit #emotion #region-us
|
# Dataset Card for RuIzardEmotions
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
### Dataset Summary
The RuIzardEmotions dataset is a high-quality translation of the go-emotions dataset and the other emotion-detection dataset. It contains 30k Reddit comments labeled for 10 emotion categories (__joy__, __sadness__, __anger__, __enthusiasm__, __surprise__, __disgust__, __fear__, __guilt__, __shame__ and __neutral__).
The datasets were translated using the accurate translator DeepL and additional processing. The idea for the dataset was inspired by the Izard's model of human emotions.
The dataset already with predefined train/val/test splits.
### Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
### Languages
The data is in Russian.
## Dataset Structure
### Data Instances
Each instance is a reddit comment with one or more emotion annotations (or neutral).
### Data Splits
The simplified data includes a set of train/val/test splits with 24k, 3k, and 3k examples respectively.
## Considerations for Using the Data
### Social Impact of Dataset
Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer
interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases
to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance
pricing, and student attentiveness
## Additional Information
### Licensing Information
The GitHub repository which houses this dataset has an
Apache License 2.0.
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]
|
ef0759e834a78b9bac100dc7c773731dd733fb25 | ## Dataset Description
- **Repository:** [GitHub Repository](https://kgxqr.github.io/) | FudanSELab/SO_KGXQR_DUPLICATE | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
]
| 2023-11-20T12:30:06+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "duplicate_csharp", "features": [{"name": "query", "dtype": "string"}, {"name": "relevant", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 91485, "num_examples": 1200}], "download_size": 61619, "dataset_size": 91485}, {"config_name": "duplicate_java", "features": [{"name": "query", "dtype": "string"}, {"name": "relevant", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 102838, "num_examples": 1200}], "download_size": 69239, "dataset_size": 102838}, {"config_name": "duplicate_javascript", "features": [{"name": "query", "dtype": "string"}, {"name": "relevant", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 107321, "num_examples": 1200}], "download_size": 69456, "dataset_size": 107321}, {"config_name": "duplicate_python", "features": [{"name": "query", "dtype": "string"}, {"name": "relevant", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 109709, "num_examples": 1200}], "download_size": 73833, "dataset_size": 109709}], "configs": [{"config_name": "duplicate_csharp", "data_files": [{"split": "test", "path": "duplicate_csharp/test-*"}]}, {"config_name": "duplicate_java", "data_files": [{"split": "test", "path": "duplicate_java/test-*"}]}, {"config_name": "duplicate_javascript", "data_files": [{"split": "test", "path": "duplicate_javascript/test-*"}]}, {"config_name": "duplicate_python", "data_files": [{"split": "test", "path": "duplicate_python/test-*"}]}]} | 2023-11-20T12:36:44+00:00 | []
| [
"en"
]
| TAGS
#size_categories-1K<n<10K #language-English #license-mit #region-us
| ## Dataset Description
- Repository: GitHub Repository | [
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|
c1a8eaea6987e24aa68fa8cb9820b44fed69f18b | # Dataset Card for "translated_math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | PaulTran/translated_math | [
"region:us"
]
| 2023-11-20T12:43:30+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "explanation", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7908449, "num_examples": 28386}], "download_size": 1068029, "dataset_size": 7908449}} | 2023-11-21T01:06:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "translated_math"
More Information needed | [
"# Dataset Card for \"translated_math\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"translated_math\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"translated_math\"\n\nMore Information needed"
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|
3a63b83aae8f92704a39aaef3ad9260ae7f1477d | # Dataset Card for "all_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | phanvancongthanh/all_data | [
"region:us"
]
| 2023-11-20T13:12:27+00:00 | {"dataset_info": {"features": [{"name": "smiles", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22169550234, "num_examples": 507079513}], "download_size": 11449897663, "dataset_size": 22169550234}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-20T13:21:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "all_data"
More Information needed | [
"# Dataset Card for \"all_data\"\n\nMore Information needed"
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|
2ac60a96358db6b2241a6f9233bb56c55e615b37 | # Dataset Card for "Music_Gen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HgThinker/Music_Gen | [
"region:us"
]
| 2023-11-20T13:23:54+00:00 | {"dataset_info": {"features": [{"name": "ytid", "dtype": "string"}, {"name": "start_s", "dtype": "int64"}, {"name": "end_s", "dtype": "int64"}, {"name": "audioset_positive_labels", "dtype": "string"}, {"name": "aspect_list", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "author_id", "dtype": "int64"}, {"name": "is_balanced_subset", "dtype": "bool"}, {"name": "is_audioset_eval", "dtype": "bool"}, {"name": "audio", "struct": [{"name": "bytes", "dtype": "null"}, {"name": "path", "dtype": "string"}]}, {"name": "download_status", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3161607, "num_examples": 5520}], "download_size": 0, "dataset_size": 3161607}} | 2023-11-20T16:14:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Music_Gen"
More Information needed | [
"# Dataset Card for \"Music_Gen\"\n\nMore Information needed"
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|
f195077dc2fc28adbd01f70041e4ce57cff0511b |
# Tesla Model 3 ROS Data Repository
## Overview
This repository contains a collection of ROS bag files (in MCAP format) and video streams from a Tesla Model 3 vehicle. It includes data from multiple runs, featuring a variety of sensors such as VLP-16 LiDAR, CAN bus signals, GPS, a 9-axis IMU, and four camera streams.
## Dataset Description
The data is organized into multiple runs, each containing synchronized streams from the following sensors:
* VLP-16 LiDAR: 3D point cloud data capturing the vehicle's surroundings.
* CAN Bus: Vehicle's internal communication data including speed, steering angle, and more.
* GPS: Geolocation data showing the vehicle's position.
* 9-Axis IMU: Inertial data providing acceleration, orientation, and gyroscope measurements.
* Camera Streams: Four video streams capturing front, rear, and side views.
# License
This project is licensed under the MIT License - see the LICENSE file for details.
# Contact
For any queries or issues, please open an issue in the repository, or contact me directly at [email protected] | tfoldi/tesla3_av_rosbags | [
"task_categories:object-detection",
"size_categories:100M<n<1B",
"license:mit",
"rosbag",
"ros2",
"lidar",
"vehicle",
"car",
"canbus",
"autonomous_vehicles",
"region:us"
]
| 2023-11-20T13:27:20+00:00 | {"license": "mit", "size_categories": ["100M<n<1B"], "task_categories": ["object-detection"], "pretty_name": "tesla3_av_rosbags", "tags": ["rosbag", "ros2", "lidar", "vehicle", "car", "canbus", "autonomous_vehicles"]} | 2024-02-10T14:27:44+00:00 | []
| []
| TAGS
#task_categories-object-detection #size_categories-100M<n<1B #license-mit #rosbag #ros2 #lidar #vehicle #car #canbus #autonomous_vehicles #region-us
|
# Tesla Model 3 ROS Data Repository
## Overview
This repository contains a collection of ROS bag files (in MCAP format) and video streams from a Tesla Model 3 vehicle. It includes data from multiple runs, featuring a variety of sensors such as VLP-16 LiDAR, CAN bus signals, GPS, a 9-axis IMU, and four camera streams.
!image/png
## Dataset Description
The data is organized into multiple runs, each containing synchronized streams from the following sensors:
* VLP-16 LiDAR: 3D point cloud data capturing the vehicle's surroundings.
* CAN Bus: Vehicle's internal communication data including speed, steering angle, and more.
* GPS: Geolocation data showing the vehicle's position.
* 9-Axis IMU: Inertial data providing acceleration, orientation, and gyroscope measurements.
* Camera Streams: Four video streams capturing front, rear, and side views.
# License
This project is licensed under the MIT License - see the LICENSE file for details.
# Contact
For any queries or issues, please open an issue in the repository, or contact me directly at tfoldi@URL | [
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"passage: TAGS\n#task_categories-object-detection #size_categories-100M<n<1B #license-mit #rosbag #ros2 #lidar #vehicle #car #canbus #autonomous_vehicles #region-us \n# Tesla Model 3 ROS Data Repository## Overview\nThis repository contains a collection of ROS bag files (in MCAP format) and video streams from a Tesla Model 3 vehicle. It includes data from multiple runs, featuring a variety of sensors such as VLP-16 LiDAR, CAN bus signals, GPS, a 9-axis IMU, and four camera streams.\n\n\n!image/png## Dataset Description\n\nThe data is organized into multiple runs, each containing synchronized streams from the following sensors:\n\n * VLP-16 LiDAR: 3D point cloud data capturing the vehicle's surroundings.\n * CAN Bus: Vehicle's internal communication data including speed, steering angle, and more.\n * GPS: Geolocation data showing the vehicle's position.\n * 9-Axis IMU: Inertial data providing acceleration, orientation, and gyroscope measurements.\n * Camera Streams: Four video streams capturing front, rear, and side views.# License\nThis project is licensed under the MIT License - see the LICENSE file for details.# Contact\nFor any queries or issues, please open an issue in the repository, or contact me directly at tfoldi@URL"
]
|
1de5136be829f914ec496655b8309881d2b90e9f | ## Dataset Description
- **Repository:** [GitHub Repository](https://kgxqr.github.io/) | FudanSELab/SO_KGXQR_TRAIN | [
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
]
| 2023-11-20T13:27:26+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "dataset_info": [{"config_name": "duplicate", "features": [{"name": "question1_id", "dtype": "string"}, {"name": "question1", "dtype": "string"}, {"name": "question2_id", "dtype": "string"}, {"name": "question2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2428577, "num_examples": 18281}], "download_size": 1682661, "dataset_size": 2428577}, {"config_name": "history", "features": [{"name": "so_question_id", "dtype": "string"}, {"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10039163, "num_examples": 80000}], "download_size": 7239803, "dataset_size": 10039163}, {"config_name": "negative", "features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27392182, "num_examples": 248940}], "download_size": 11085232, "dataset_size": 27392182}, {"config_name": "positive", "features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11457312, "num_examples": 101172}], "download_size": 7917727, "dataset_size": 11457312}], "configs": [{"config_name": "duplicate", "data_files": [{"split": "train", "path": "duplicate/train-*"}]}, {"config_name": "history", "data_files": [{"split": "train", "path": "history/train-*"}]}, {"config_name": "negative", "data_files": [{"split": "train", "path": "negative/train-*"}]}, {"config_name": "positive", "data_files": [{"split": "train", "path": "positive/train-*"}]}]} | 2023-11-20T16:20:32+00:00 | []
| [
"en"
]
| TAGS
#size_categories-100K<n<1M #language-English #license-mit #region-us
| ## Dataset Description
- Repository: GitHub Repository | [
"## Dataset Description\n\n- Repository: GitHub Repository"
]
| [
"TAGS\n#size_categories-100K<n<1M #language-English #license-mit #region-us \n",
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"passage: TAGS\n#size_categories-100K<n<1M #language-English #license-mit #region-us \n## Dataset Description\n\n- Repository: GitHub Repository"
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|
7883bf180a6a51f8f57943450f70e20b7123639a |
# Your Storybook Dataset
Welcome to the repository for the "Your Storybook Dataset." This dataset contains a collection of stories and narratives suitable for various natural language processing tasks.
## Under Collection | DamarJati/gpt2-m-storybook | [
"task_categories:text-generation",
"language:en",
"storybook",
"fiction",
"region:us"
]
| 2023-11-20T13:27:42+00:00 | {"language": ["en"], "task_categories": ["text-generation"], "pretty_name": "Your Storybook Dataset", "tags": ["storybook", "fiction"]} | 2023-11-21T22:20:27+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #language-English #storybook #fiction #region-us
|
# Your Storybook Dataset
Welcome to the repository for the "Your Storybook Dataset." This dataset contains a collection of stories and narratives suitable for various natural language processing tasks.
## Under Collection | [
"# Your Storybook Dataset\n\nWelcome to the repository for the \"Your Storybook Dataset.\" This dataset contains a collection of stories and narratives suitable for various natural language processing tasks.",
"## Under Collection"
]
| [
"TAGS\n#task_categories-text-generation #language-English #storybook #fiction #region-us \n",
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"## Under Collection"
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27,
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| [
"passage: TAGS\n#task_categories-text-generation #language-English #storybook #fiction #region-us \n# Your Storybook Dataset\n\nWelcome to the repository for the \"Your Storybook Dataset.\" This dataset contains a collection of stories and narratives suitable for various natural language processing tasks.## Under Collection"
]
|
c615a50f0e25e037db2b112c3cc624de509786e7 | # Dataset Card for "ICPR_pipeline3_big"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nourheshamshaheen/ICPR_pipeline3_big | [
"region:us"
]
| 2023-11-20T14:38:33+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "area", "1": "heatmap", "2": "horizontal_bar", "3": "horizontal_interval", "4": "line", "5": "manhattan", "6": "map", "7": "pie", "8": "scatter", "9": "scatter-line", "10": "surface", "11": "venn", "12": "vertical_bar", "13": "vertical_box", "14": "vertical_interval"}}}}, {"name": "pipeline_label", "dtype": {"class_label": {"names": {"0": "line", "1": "other", "2": "scatter", "3": "scatter_line", "4": "vertical_bar"}}}}, {"name": "true_label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1073939947.25, "num_examples": 20630}], "download_size": 979370224, "dataset_size": 1073939947.25}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-20T14:45:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ICPR_pipeline3_big"
More Information needed | [
"# Dataset Card for \"ICPR_pipeline3_big\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ICPR_pipeline3_big\"\n\nMore Information needed"
]
| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ICPR_pipeline3_big\"\n\nMore Information needed"
]
|
d3d15d51b419e2baf4bf1cc2cc940f6b58a946f7 | # Dataset Card for "ICPR_pipeline3_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nourheshamshaheen/ICPR_pipeline3_small | [
"region:us"
]
| 2023-11-20T14:45:17+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "area", "1": "heatmap", "2": "horizontal_bar", "3": "horizontal_interval", "4": "line", "5": "manhattan", "6": "map", "7": "pie", "8": "scatter", "9": "scatter-line", "10": "surface", "11": "venn", "12": "vertical_bar", "13": "vertical_box", "14": "vertical_interval"}}}}, {"name": "true_label", "dtype": {"class_label": {"names": {"0": "area", "1": "heatmap", "2": "horizontal_bar", "3": "horizontal_interval", "4": "manhattan", "5": "map", "6": "other", "7": "pie", "8": "surface", "9": "venn", "10": "vertical_box", "11": "vertical_interval"}}}}], "splits": [{"name": "train", "num_bytes": 236277245.998, "num_examples": 4234}], "download_size": 216714311, "dataset_size": 236277245.998}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-20T14:46:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ICPR_pipeline3_small"
More Information needed | [
"# Dataset Card for \"ICPR_pipeline3_small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ICPR_pipeline3_small\"\n\nMore Information needed"
]
| [
6,
20
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ICPR_pipeline3_small\"\n\nMore Information needed"
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|
77790df32c1855a0f1fc957ac11c3413a6914971 |
Latest "cases" datadump from the Open Legal Data project, dated at 19-Oct-2022 12:11.
See source: https://static.openlegaldata.io/dumps/de/2022-10-18/ | hyperinfer/old_cases | [
"license:mit",
"region:us"
]
| 2023-11-20T14:58:10+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4860834620.419848, "num_examples": 225934}, {"name": "test", "num_bytes": 540097516.5801512, "num_examples": 25104}], "download_size": 2677291709, "dataset_size": 5400932137.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-01-17T13:51:31+00:00 | []
| []
| TAGS
#license-mit #region-us
|
Latest "cases" datadump from the Open Legal Data project, dated at 19-Oct-2022 12:11.
See source: URL | []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
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|
c2f669904aaff2796842192fa0c3f59c2373834f | # Dataset Card for "des_mesh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | FlyingFishzzz/des_mesh | [
"region:us"
]
| 2023-11-20T15:49:41+00:00 | {"dataset_info": {"features": [{"name": "target", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "mesh", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 520589341.0, "num_examples": 1588}], "download_size": 520482757, "dataset_size": 520589341.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-20T15:51:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "des_mesh"
More Information needed | [
"# Dataset Card for \"des_mesh\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"des_mesh\"\n\nMore Information needed"
]
| [
6,
14
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"des_mesh\"\n\nMore Information needed"
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|
50395a79a36eddcec8675e80c4e1b8667c053ec2 | # Dataset Card for "donut_model_data_for_german_invoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Aoschu/donut_model_data_for_german_invoice | [
"region:us"
]
| 2023-11-20T16:14:35+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": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12829172.0, "num_examples": 97}, {"name": "validation", "num_bytes": 2062396.0, "num_examples": 14}, {"name": "test", "num_bytes": 2719786.0, "num_examples": 18}], "download_size": 13266362, "dataset_size": 17611354.0}} | 2023-11-20T23:17:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "donut_model_data_for_german_invoice"
More Information needed | [
"# Dataset Card for \"donut_model_data_for_german_invoice\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"donut_model_data_for_german_invoice\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"donut_model_data_for_german_invoice\"\n\nMore Information needed"
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|
030cb61986ac5913f65a69af88f34bdce4f37b03 | ## Dataset Description
This dataset contains a collection of law documents related to Terms of
Service, general federal law, and California law. It includes documents
sourced from pile-of-law and various other legal sources.
Documents from pile-of-law:
- U.S. Code of Federal Regulations
- U.S. State Codes
- The United States Code
- Educational Casebooks released under open CC licenses
- Unannotated Terms of Service contracts
- Advisory opinions by the Federal Trade Commission
Documents from other sources:
- California's Anti-SLAPP Statute
- California Confidentiality of Medical Information Act (CMIA)
- CALIFORNIA CONSUMER PRIVACY ACT OF 2018
- THE CALIFORNIA PRIVACY RIGHTS ACT OF 2020
- California's Electronic Communications Privacy Act (CalECPA)
- California's Unfair Competition Law
- Consumer Legal Remedies Act (CLRA)
- Judicial Branch Contracting Manual
- Online Privacy Protection Act (OPPA) - Minors
- THE ELECTRONIC COMMUNICATIONS PRIVACY ACT- PROMOTING SECURITY AND PROTECTING
PRIVACY IN THE DIGITAL AGE
- Senate Bill no. 178: Privacy: electronic communications: search warrant
- 2022 California Code Financial Code - FIN DIVISION 1.4 - CALIFORNIA
FINANCIAL INFORMATION PRIVACY ACT
- 2010 California Code Financial Code Division 1.2. California Financial
Information Privacy Act
## Data Fields
- text: the document text
## Language
English
# Importing the Dataset
To use this dataset, you need to download it into your Python environment, then import it using the Hugging Face `datasets` library:
```python
from datasets import Dataset
# Replace "path/to/data.arrow" with the actual path to your downloaded dataset file
ds = Dataset.from_file("path/to/data.arrow")
```
| ibunescu/General_TOS_Law_California | [
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-11-20T16:31:36+00:00 | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "pretty_name": "a", "id": "legal_documents"} | 2023-11-20T16:58:12+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-cc-by-nc-sa-4.0 #region-us
| ## Dataset Description
This dataset contains a collection of law documents related to Terms of
Service, general federal law, and California law. It includes documents
sourced from pile-of-law and various other legal sources.
Documents from pile-of-law:
- U.S. Code of Federal Regulations
- U.S. State Codes
- The United States Code
- Educational Casebooks released under open CC licenses
- Unannotated Terms of Service contracts
- Advisory opinions by the Federal Trade Commission
Documents from other sources:
- California's Anti-SLAPP Statute
- California Confidentiality of Medical Information Act (CMIA)
- CALIFORNIA CONSUMER PRIVACY ACT OF 2018
- THE CALIFORNIA PRIVACY RIGHTS ACT OF 2020
- California's Electronic Communications Privacy Act (CalECPA)
- California's Unfair Competition Law
- Consumer Legal Remedies Act (CLRA)
- Judicial Branch Contracting Manual
- Online Privacy Protection Act (OPPA) - Minors
- THE ELECTRONIC COMMUNICATIONS PRIVACY ACT- PROMOTING SECURITY AND PROTECTING
PRIVACY IN THE DIGITAL AGE
- Senate Bill no. 178: Privacy: electronic communications: search warrant
- 2022 California Code Financial Code - FIN DIVISION 1.4 - CALIFORNIA
FINANCIAL INFORMATION PRIVACY ACT
- 2010 California Code Financial Code Division 1.2. California Financial
Information Privacy Act
## Data Fields
- text: the document text
## Language
English
# Importing the Dataset
To use this dataset, you need to download it into your Python environment, then import it using the Hugging Face 'datasets' library:
| [
"## Dataset Description\n This dataset contains a collection of law documents related to Terms of\n Service, general federal law, and California law. It includes documents\n sourced from pile-of-law and various other legal sources.\n\n\n Documents from pile-of-law:\n\n - U.S. Code of Federal Regulations\n\n - U.S. State Codes\n\n - The United States Code\n\n - Educational Casebooks released under open CC licenses\n\n - Unannotated Terms of Service contracts\n\n - Advisory opinions by the Federal Trade Commission\n\n\n Documents from other sources:\n\n - California's Anti-SLAPP Statute\n\n - California Confidentiality of Medical Information Act (CMIA)\n\n - CALIFORNIA CONSUMER PRIVACY ACT OF 2018\n\n - THE CALIFORNIA PRIVACY RIGHTS ACT OF 2020\n\n - California's Electronic Communications Privacy Act (CalECPA)\n\n - California's Unfair Competition Law\n\n - Consumer Legal Remedies Act (CLRA)\n\n - Judicial Branch Contracting Manual\n\n - Online Privacy Protection Act (OPPA) - Minors\n\n - THE ELECTRONIC COMMUNICATIONS PRIVACY ACT- PROMOTING SECURITY AND PROTECTING\n PRIVACY IN THE DIGITAL AGE\n\n - Senate Bill no. 178: Privacy: electronic communications: search warrant\n\n - 2022 California Code Financial Code - FIN DIVISION 1.4 - CALIFORNIA\n FINANCIAL INFORMATION PRIVACY ACT\n\n - 2010 California Code Financial Code Division 1.2. California Financial\n Information Privacy Act",
"## Data Fields\n\n- text: the document text",
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"# Importing the Dataset\n\nTo use this dataset, you need to download it into your Python environment, then import it using the Hugging Face 'datasets' library:"
]
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"## Data Fields\n\n- text: the document text",
"## Language\nEnglish",
"# Importing the Dataset\n\nTo use this dataset, you need to download it into your Python environment, then import it using the Hugging Face 'datasets' library:"
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"passage: TAGS\n#language-English #license-cc-by-nc-sa-4.0 #region-us \n## Dataset Description\n This dataset contains a collection of law documents related to Terms of\n Service, general federal law, and California law. It includes documents\n sourced from pile-of-law and various other legal sources.\n\n\n Documents from pile-of-law:\n\n - U.S. Code of Federal Regulations\n\n - U.S. State Codes\n\n - The United States Code\n\n - Educational Casebooks released under open CC licenses\n\n - Unannotated Terms of Service contracts\n\n - Advisory opinions by the Federal Trade Commission\n\n\n Documents from other sources:\n\n - California's Anti-SLAPP Statute\n\n - California Confidentiality of Medical Information Act (CMIA)\n\n - CALIFORNIA CONSUMER PRIVACY ACT OF 2018\n\n - THE CALIFORNIA PRIVACY RIGHTS ACT OF 2020\n\n - California's Electronic Communications Privacy Act (CalECPA)\n\n - California's Unfair Competition Law\n\n - Consumer Legal Remedies Act (CLRA)\n\n - Judicial Branch Contracting Manual\n\n - Online Privacy Protection Act (OPPA) - Minors\n\n - THE ELECTRONIC COMMUNICATIONS PRIVACY ACT- PROMOTING SECURITY AND PROTECTING\n PRIVACY IN THE DIGITAL AGE\n\n - Senate Bill no. 178: Privacy: electronic communications: search warrant\n\n - 2022 California Code Financial Code - FIN DIVISION 1.4 - CALIFORNIA\n FINANCIAL INFORMATION PRIVACY ACT\n\n - 2010 California Code Financial Code Division 1.2. California Financial\n Information Privacy Act## Data Fields\n\n- text: the document text## Language\nEnglish# Importing the Dataset\n\nTo use this dataset, you need to download it into your Python environment, then import it using the Hugging Face 'datasets' library:"
]
|
a56a086d2688b4e8a70d61af1ecf0994de25adbe | # The list of all subsets in the dataset
Each subset is generated splitting videos from given particular ukrainiam YouTube channel
All subsets are in test split
- "opodcast" subset is from channel "О! ПОДКАСТ"
- "rozdympodcast" subset is from channel "Роздум | Подкаст"
- "test" subset is just a small subset of samples
# Loading a particular subset
```
>>> data_files = {"train": "data/<your_subset>.parquet"}
>>> data = load_dataset("Zarakun/youtube_ua_subtitles_test", data_files=data_files)
>>> data
DatasetDict({
train: Dataset({
features: ['audio', 'rate', 'duration', 'sentence'],
num_rows: <some_number>
})
})
``` | Zarakun/youtube_ua_subtitles_test | [
"task_categories:automatic-speech-recognition",
"region:us"
]
| 2023-11-20T16:55:36+00:00 | {"task_categories": ["automatic-speech-recognition"], "pretty_name": "MangoSpeech", "configs": [{"config_name": "rozdympodcast", "data_files": "data/rozdympodcast.parquet"}, {"config_name": "opodcast", "data_files": "data/opodcast.parquet"}, {"config_name": "test", "data_files": "data/test.parquet"}]} | 2024-01-17T10:35:06+00:00 | []
| []
| TAGS
#task_categories-automatic-speech-recognition #region-us
| # The list of all subsets in the dataset
Each subset is generated splitting videos from given particular ukrainiam YouTube channel
All subsets are in test split
- "opodcast" subset is from channel "О! ПОДКАСТ"
- "rozdympodcast" subset is from channel "Роздум | Подкаст"
- "test" subset is just a small subset of samples
# Loading a particular subset
| [
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"# Loading a particular subset"
]
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"passage: TAGS\n#task_categories-automatic-speech-recognition #region-us \n# The list of all subsets in the dataset\nEach subset is generated splitting videos from given particular ukrainiam YouTube channel\nAll subsets are in test split\n\n- \"opodcast\" subset is from channel \"О! ПОДКАСТ\"\n- \"rozdympodcast\" subset is from channel \"Роздум | Подкаст\" \n- \"test\" subset is just a small subset of samples# Loading a particular subset"
]
|
c7d3fc14e8e5f8e075312aa11d002f87433ca5d6 | ## Dataset Description
- **Repository:** [GitHub Repository](https://kgxqr.github.io/) | FudanSELab/SO_KGXQR_HISTORY | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
]
| 2023-11-20T17:04:47+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "history_csharp", "features": [{"name": "so_question_id", "dtype": "string"}, {"name": "historical_title", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 133783, "num_examples": 2000}], "download_size": 95916, "dataset_size": 133783}, {"config_name": "history_java", "features": [{"name": "so_question_id", "dtype": "string"}, {"name": "historical_title", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 132058, "num_examples": 2000}], "download_size": 93956, "dataset_size": 132058}, {"config_name": "history_javascript", "features": [{"name": "so_question_id", "dtype": "string"}, {"name": "historical_title", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 132306, "num_examples": 2000}], "download_size": 91896, "dataset_size": 132306}, {"config_name": "history_python", "features": [{"name": "so_question_id", "dtype": "string"}, {"name": "historical_title", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 132390, "num_examples": 2000}], "download_size": 92772, "dataset_size": 132390}], "configs": [{"config_name": "history_csharp", "data_files": [{"split": "test", "path": "history_csharp/test-*"}]}, {"config_name": "history_java", "data_files": [{"split": "test", "path": "history_java/test-*"}]}, {"config_name": "history_javascript", "data_files": [{"split": "test", "path": "history_javascript/test-*"}]}, {"config_name": "history_python", "data_files": [{"split": "test", "path": "history_python/test-*"}]}]} | 2023-11-20T17:11:05+00:00 | []
| [
"en"
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| TAGS
#size_categories-1K<n<10K #language-English #license-mit #region-us
| ## Dataset Description
- Repository: GitHub Repository | [
"## Dataset Description\n\n- Repository: GitHub Repository"
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|
1c84d46dabfb127bfc1b2cc62823cad3dc2b7b90 | # Dataset Card for "CID1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Lollitor/CID1 | [
"region:us"
]
| 2023-11-20T17:22:19+00:00 | {"dataset_info": {"config_name": "Lollitor", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40847, "num_examples": 435}], "download_size": 9220, "dataset_size": 40847}, "configs": [{"config_name": "Lollitor", "data_files": [{"split": "train", "path": "Lollitor/train-*"}]}]} | 2023-11-20T17:22:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "CID1"
More Information needed | [
"# Dataset Card for \"CID1\"\n\nMore Information needed"
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"# Dataset Card for \"CID1\"\n\nMore Information needed"
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|
3adb0e830a3618b4902537345ca858c6fb546467 | # Dataset Card for "CID13"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Lollitor/CID13 | [
"region:us"
]
| 2023-11-20T17:23:17+00:00 | {"dataset_info": {"config_name": "Lollitor", "features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10852, "num_examples": 176}], "download_size": 3806, "dataset_size": 10852}, "configs": [{"config_name": "Lollitor", "data_files": [{"split": "train", "path": "Lollitor/train-*"}]}]} | 2023-11-22T15:12:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "CID13"
More Information needed | [
"# Dataset Card for \"CID13\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
"# Dataset Card for \"CID13\"\n\nMore Information needed"
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|
5fd65c30f0d2c84fca87027227123f005203a393 | # Dataset Card for "redpajama_v2_sample_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sade-adrien/redpajama_v2_sample_1M | [
"region:us"
]
| 2023-11-20T17:24:05+00:00 | {"dataset_info": {"features": [{"name": "raw_content", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "quality_signals", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10406779209, "num_examples": 1000000}], "download_size": 4624261556, "dataset_size": 10406779209}} | 2023-11-20T17:27:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "redpajama_v2_sample_1M"
More Information needed | [
"# Dataset Card for \"redpajama_v2_sample_1M\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
86826a6e309ee050132f76b3f31e0405997ac04f | # Dataset Card for "wcr_base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | PaulLoisel/wcr_base | [
"region:us"
]
| 2023-11-20T17:33:06+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": "Age", "dtype": "int64"}, {"name": "Title", "dtype": "string"}, {"name": "Review Text", "dtype": "string"}, {"name": "Rating", "dtype": "int64"}, {"name": "label", "dtype": "int64"}, {"name": "Positive Feedback Count", "dtype": "int64"}, {"name": "Division Name", "dtype": "string"}, {"name": "Department Name", "dtype": "string"}, {"name": "Class Name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 145123.3, "num_examples": 350}, {"name": "test", "num_bytes": 31097.85, "num_examples": 75}, {"name": "val", "num_bytes": 31097.85, "num_examples": 75}], "download_size": 129979, "dataset_size": 207319.0}} | 2023-11-20T17:33:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wcr_base"
More Information needed | [
"# Dataset Card for \"wcr_base\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"wcr_base\"\n\nMore Information needed"
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|
31f64ff29ba597dbb81434850def0a522e762b27 |
# Bangumi Image Base of Kamikaze Kaitou Jeanne
This is the image base of bangumi Kamikaze Kaitou Jeanne, we detected 43 characters, 3600 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 527 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 39 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 690 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 38 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 33 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 26 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 110 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 28 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 435 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 351 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 45 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 69 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 14 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 78 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 54 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 21 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 23 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 45 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 18 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 159 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 22 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 103 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 42 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 12 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 14 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 30 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 12 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 18 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 12 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 11 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 208 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 28 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 24 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 13 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 34 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 7 | [Download](37/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 12 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 17 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 109 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/kamikazekaitoujeanne | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| 2023-11-20T17:50:43+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-20T18:57:19+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Kamikaze Kaitou Jeanne
============================================
This is the image base of bangumi Kamikaze Kaitou Jeanne, we detected 43 characters, 3600 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| []
| [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
]
| [
25
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
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|
e5a5afc359947d449b735ec10c96ae3e04af123e | # Dataset Card for "VNTL-2k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lmg-anon/VNTL-2k | [
"region:us"
]
| 2023-11-20T17:56:51+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 87890178, "num_examples": 16887}], "download_size": 0, "dataset_size": 87890178}} | 2023-11-20T20:41:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VNTL-2k"
More Information needed | [
"# Dataset Card for \"VNTL-2k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
6d079f41b100ff288beabcc4802cba1342bd9689 |
# Labels
0: normal
1: toxic | JestemKamil/text-classification-pl | [
"region:us"
]
| 2023-11-20T18:22:21+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6493727, "num_examples": 35672}], "download_size": 4309231, "dataset_size": 6493727}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-22T14:11:42+00:00 | []
| []
| TAGS
#region-us
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# Labels
0: normal
1: toxic | [
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|
54f4f0ee7f00f9725f170f44fac40cb3954879e2 | # Dataset Card for "tranlation_envi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vlsp-2023-vllm/tranlation_envi | [
"region:us"
]
| 2023-11-20T18:23:33+00:00 | {"dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "fewshot_samples", "list": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1150113.9096666668, "num_examples": 1103}], "download_size": 230892, "dataset_size": 1150113.9096666668}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2023-11-20T18:23:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tranlation_envi"
More Information needed | [
"# Dataset Card for \"tranlation_envi\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
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|
16bcee0fb27069c1a7e867efce5ef12a9f72d845 | # Dataset Card for "Dermnet-Test-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rkdeva/Dermnet-Test-1 | [
"region:us"
]
| 2023-11-20T18:30:25+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 376769298.178, "num_examples": 3937}], "download_size": 370140973, "dataset_size": 376769298.178}} | 2023-11-20T18:32:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Dermnet-Test-1"
More Information needed | [
"# Dataset Card for \"Dermnet-Test-1\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
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|
9c21f562b4974bd2dd2737bf1cd411b756901a48 | # Dataset Card for "Dermnet-Test-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rkdeva/Dermnet-Test-2 | [
"region:us"
]
| 2023-11-20T18:33:11+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 376769322.178, "num_examples": 3937}], "download_size": 370140971, "dataset_size": 376769322.178}} | 2023-11-20T18:35:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Dermnet-Test-2"
More Information needed | [
"# Dataset Card for \"Dermnet-Test-2\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
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|
73798917053d1287b88dd2a203df5303cb85f2c4 | # Dataset Card for "imagenet_dino"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | danjacobellis/imagenet_dino | [
"region:us"
]
| 2023-11-20T18:37:49+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}, {"name": "cls_token", "sequence": {"sequence": "float32"}}, {"name": "patch_tokens", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 20224716800, "num_examples": 12800}, {"name": "validation", "num_bytes": 7900280000, "num_examples": 5000}], "download_size": 23726734894, "dataset_size": 28124996800}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-22T15:05:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "imagenet_dino"
More Information needed | [
"# Dataset Card for \"imagenet_dino\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"imagenet_dino\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"imagenet_dino\"\n\nMore Information needed"
]
|
2b09e16ec834064b821a1803aecbde594b51029e | # Dataset Card for "iliad_odyssey_aligned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pnadel/iliad_odyssey_aligned | [
"region:us"
]
| 2023-11-20T18:38:42+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "sentid", "dtype": "string"}, {"name": "cit", "dtype": "string"}, {"name": "Eng", "dtype": "string"}, {"name": "Gk", "dtype": "string"}, {"name": "Lems", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5475867.200602134, "num_examples": 12223}, {"name": "test", "num_bytes": 1369078.7993978662, "num_examples": 3056}], "download_size": 3953094, "dataset_size": 6844946.0}} | 2023-11-20T18:38:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "iliad_odyssey_aligned"
More Information needed | [
"# Dataset Card for \"iliad_odyssey_aligned\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"iliad_odyssey_aligned\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"iliad_odyssey_aligned\"\n\nMore Information needed"
]
|
a4be6890758f7d9919ee29243d2ff528c1d4d133 | # Dataset Card for "redpajama_v2_32k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sade-adrien/redpajama_v2_32k | [
"region:us"
]
| 2023-11-20T19:24:07+00:00 | {"dataset_info": {"features": [{"name": "raw_content", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "quality_signals", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3919516915.1525173, "num_examples": 364920}], "download_size": 18972252318, "dataset_size": 3919516915.1525173}} | 2023-11-20T21:43:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "redpajama_v2_32k"
More Information needed | [
"# Dataset Card for \"redpajama_v2_32k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"redpajama_v2_32k\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"redpajama_v2_32k\"\n\nMore Information needed"
]
|
dcfd1a8075dffd498fa190916fe28b1e0f8300e9 |
## ChessInstruct
The ChessInstruct Dataset serves as the foundation for training and fine-tuning Language Models (LLMs) specifically in the realm of chess instruction.
Derived from the [laion/strategic_game_chess](https://huggingface.co/datasets/laion/strategic_game_chess) dataset, this meticulously curated dataset encompasses a wide array of annotated instructional chess content.
Features of the ChessInstruct Dataset:
* **Rich and Diverse Content**: Curated with a broad spectrum of instructional resources including annotated games, strategic analyses (incoming) and positional evaluations, the dataset facilitates comprehensive learning and modeling.
* **Customizable Training Resource**: The ChessInstruct Dataset allows for the tailored fine-tuning of any Language Model, enabling researchers and practitioners to adapt and optimize LLMs for chess-specific instructional contexts.
* **Annotated Instructional Insights**: Detailed annotations and instructional cues within the dataset provide valuable guidance for language model training, emphasizing strategic moves, tactics, and decision-making processes.
## Usage
The ChessInstruct dataset comprises four primary columns:
* `task`: This column contains instruct prompts related to various chess scenarios, such as predicting the winner given a set of chess moves or identifying the last move in a sequence.
* `input`: The input column provides supplementary information, usually a series of chess moves, to support the instruct prompt. These inputs are presented as JSON-serialized strings.
* `expected_output`: This column presents the anticipated or expected output corresponding to the instruct task. The expected outputs are also serialized as JSON strings.
* `KIND`: The KIND column categorizes the type of instruct prompt, delineating the nature of the task, whether it involves identifying winning scenarios, predicting subsequent moves, or performing other chess-related analyses.
### Distribution
| Task | Number of samples training set | Number of samples test set | Distribution |
|------|--------------------------------|----------------------------|--------------|
| Finding last movement | 13500 | 1500 |15% |
| Finding game's score | 18000 | 2000 | 20% |
| Finding missing movements | 13500 | 1500 | 15% |
| Finding the best possible move to do | 18000 | 2000 | 20% |
| Finding who is advantaged in the game | 18000 | 2000 | 20% |
| Sorting FENs from earliest to oldest in the game | 9000 | 1000 | 10% |
## Reproduction
All the necessary code to reproduce this dataset is available here: [Thytu/StockLLM](https://github.com/Thytu/StockLLM)
## Citation
This dataset is based on [laion/strategic_game_chess](https://huggingface.co/datasets/laion/strategic_game_chess?row=0) which I thank dearly for the data | Thytu/ChessInstruct | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-11-20T19:25:18+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Chess Instruct"} | 2023-11-26T11:45:53+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us
| ChessInstruct
-------------
The ChessInstruct Dataset serves as the foundation for training and fine-tuning Language Models (LLMs) specifically in the realm of chess instruction.
Derived from the laion/strategic\_game\_chess dataset, this meticulously curated dataset encompasses a wide array of annotated instructional chess content.
Features of the ChessInstruct Dataset:
* Rich and Diverse Content: Curated with a broad spectrum of instructional resources including annotated games, strategic analyses (incoming) and positional evaluations, the dataset facilitates comprehensive learning and modeling.
* Customizable Training Resource: The ChessInstruct Dataset allows for the tailored fine-tuning of any Language Model, enabling researchers and practitioners to adapt and optimize LLMs for chess-specific instructional contexts.
* Annotated Instructional Insights: Detailed annotations and instructional cues within the dataset provide valuable guidance for language model training, emphasizing strategic moves, tactics, and decision-making processes.
Usage
-----
The ChessInstruct dataset comprises four primary columns:
* 'task': This column contains instruct prompts related to various chess scenarios, such as predicting the winner given a set of chess moves or identifying the last move in a sequence.
* 'input': The input column provides supplementary information, usually a series of chess moves, to support the instruct prompt. These inputs are presented as JSON-serialized strings.
* 'expected\_output': This column presents the anticipated or expected output corresponding to the instruct task. The expected outputs are also serialized as JSON strings.
* 'KIND': The KIND column categorizes the type of instruct prompt, delineating the nature of the task, whether it involves identifying winning scenarios, predicting subsequent moves, or performing other chess-related analyses.
### Distribution
Reproduction
------------
All the necessary code to reproduce this dataset is available here: Thytu/StockLLM
This dataset is based on laion/strategic\_game\_chess which I thank dearly for the data
| [
"### Distribution\n\n\n\nReproduction\n------------\n\n\nAll the necessary code to reproduce this dataset is available here: Thytu/StockLLM\n\n\nThis dataset is based on laion/strategic\\_game\\_chess which I thank dearly for the data"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n",
"### Distribution\n\n\n\nReproduction\n------------\n\n\nAll the necessary code to reproduce this dataset is available here: Thytu/StockLLM\n\n\nThis dataset is based on laion/strategic\\_game\\_chess which I thank dearly for the data"
]
| [
42,
55
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| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n### Distribution\n\n\n\nReproduction\n------------\n\n\nAll the necessary code to reproduce this dataset is available here: Thytu/StockLLM\n\n\nThis dataset is based on laion/strategic\\_game\\_chess which I thank dearly for the data"
]
|
5fb990ff8af3846233b7b5f6c4f0d9b9a54133b2 | # Dataset Card for "donut_model_data_for_german_invoice_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Aoschu/donut_model_data_for_german_invoice_2 | [
"region:us"
]
| 2023-11-20T20:29:45+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2062396.0, "num_examples": 14}], "download_size": 1621615, "dataset_size": 2062396.0}} | 2023-11-20T20:29:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "donut_model_data_for_german_invoice_2"
More Information needed | [
"# Dataset Card for \"donut_model_data_for_german_invoice_2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"donut_model_data_for_german_invoice_2\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"donut_model_data_for_german_invoice_2\"\n\nMore Information needed"
]
|
02071f54f8bc425cd00796cfd8a97f73e1f3bc0f | # Easy MNIST
MNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory.
## mnist_png.zip
MNIST in the png format.
```
label path
0 5 data/0.png
1 0 data/1.png
2 4 data/2.png
3 1 data/3.png
4 9 data/4.png
... ... ...
69995 2 data/69995.png
69996 3 data/69996.png
69997 4 data/69997.png
69998 5 data/69998.png
69999 6 data/69999.png
```
## mnist_numpy.zip
MNIST in the npy format.
```
label path
0 5 data/0.npy
1 0 data/1.npy
2 4 data/2.npy
3 1 data/3.npy
4 9 data/4.npy
... ... ...
69995 2 data/69995.npy
69996 3 data/69996.npy
69997 4 data/69997.npy
69998 5 data/69998.npy
69999 6 data/69999.npy
```
## mnist_numpy_flat.zip
MNIST in the npy format, flattened to 784 dimensional vectors.
```
label path
0 5 data/0.npy
1 0 data/1.npy
2 4 data/2.npy
3 1 data/3.npy
4 9 data/4.npy
... ... ...
69995 2 data/69995.npy
69996 3 data/69996.npy
69997 4 data/69997.npy
69998 5 data/69998.npy
69999 6 data/69999.npy
```
## Acknowledgements
- Yann LeCun, Courant Institute, NYU
- Corinna Cortes, Google Labs, New York
- Christopher J.C. Burges, Microsoft Research, Redmond
| hayden-donnelly/easy-mnist | [
"task_categories:image-classification",
"task_categories:unconditional-image-generation",
"size_categories:10K<n<100K",
"region:us"
]
| 2023-11-20T21:31:37+00:00 | {"size_categories": ["10K<n<100K"], "task_categories": ["image-classification", "unconditional-image-generation"], "pretty_name": "Easy MNIST"} | 2023-11-20T21:45:49+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-unconditional-image-generation #size_categories-10K<n<100K #region-us
| # Easy MNIST
MNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory.
## mnist_png.zip
MNIST in the png format.
## mnist_numpy.zip
MNIST in the npy format.
## mnist_numpy_flat.zip
MNIST in the npy format, flattened to 784 dimensional vectors.
## Acknowledgements
- Yann LeCun, Courant Institute, NYU
- Corinna Cortes, Google Labs, New York
- Christopher J.C. Burges, Microsoft Research, Redmond
| [
"# Easy MNIST\nMNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory.",
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"## mnist_numpy.zip\nMNIST in the npy format.",
"## mnist_numpy_flat.zip\nMNIST in the npy format, flattened to 784 dimensional vectors.",
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"## mnist_png.zip\nMNIST in the png format.",
"## mnist_numpy.zip\nMNIST in the npy format.",
"## mnist_numpy_flat.zip\nMNIST in the npy format, flattened to 784 dimensional vectors.",
"## Acknowledgements\n- Yann LeCun, Courant Institute, NYU\n- Corinna Cortes, Google Labs, New York\n- Christopher J.C. Burges, Microsoft Research, Redmond"
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"passage: TAGS\n#task_categories-image-classification #task_categories-unconditional-image-generation #size_categories-10K<n<100K #region-us \n# Easy MNIST\nMNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory.## mnist_png.zip\nMNIST in the png format.## mnist_numpy.zip\nMNIST in the npy format.## mnist_numpy_flat.zip\nMNIST in the npy format, flattened to 784 dimensional vectors.## Acknowledgements\n- Yann LeCun, Courant Institute, NYU\n- Corinna Cortes, Google Labs, New York\n- Christopher J.C. Burges, Microsoft Research, Redmond"
]
|
92c61cc41a6bf0b2dc9742f2c97a1b8ce7bfe0aa | CauaMatheus/Testando | [
"language:en",
"region:us"
]
| 2023-11-20T21:53:42+00:00 | {"language": ["en"]} | 2023-11-25T16:24:03+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
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| [
"TAGS\n#language-English #region-us \n"
]
| [
10
]
| [
"passage: TAGS\n#language-English #region-us \n"
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|
||
8ad310293e07528bb6b78e26813ef7598981bdc2 | # Dataset Card for "ms-marco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eitanturok/ms-marco | [
"region:us"
]
| 2023-11-20T22:01:23+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}, {"split": "train", "path": "data/train-*"}, {"split": "small", "path": "data/small-*"}]}], "dataset_info": {"features": [{"name": "passages", "sequence": "string"}, {"name": "query", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "query_type", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 181853458, "num_examples": 55636}, {"name": "train", "num_bytes": 1789000138, "num_examples": 503370}, {"name": "small", "num_bytes": 351268, "num_examples": 100}], "download_size": 1049524677, "dataset_size": 1971204864}} | 2023-11-29T19:42:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ms-marco"
More Information needed | [
"# Dataset Card for \"ms-marco\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ms-marco\"\n\nMore Information needed"
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| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"ms-marco\"\n\nMore Information needed"
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|
0e99adbea77823977fd312b83fa7f5fb0640de8f | # Dataset Card for "redpajama_v2_sample_10M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sade-adrien/redpajama_v2_sample_10M | [
"region:us"
]
| 2023-11-20T22:02:53+00:00 | {"dataset_info": {"features": [{"name": "raw_content", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "quality_signals", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 104068894016, "num_examples": 10000000}], "download_size": 0, "dataset_size": 104068894016}} | 2023-11-21T03:30:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "redpajama_v2_sample_10M"
More Information needed | [
"# Dataset Card for \"redpajama_v2_sample_10M\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
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"passage: TAGS\n#region-us \n# Dataset Card for \"redpajama_v2_sample_10M\"\n\nMore Information needed"
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|
7577c73577f3ba0c9c13a170e2557713619c41ce |
# MoisesDB
Moises Dataset for Source Separation
### Dataset Description
- **Homepage:** [MoisesDB homepage](https://developer.moises.ai/research/)
- **Repository:** [MoisesDB repository](https://github.com/moises-ai/moises-db)
- **Paper:** [Moisesdb: A dataset for source separation beyond 4-stems](https://arxiv.org/abs/2307.15913)
- **Point of Contact:** [Igor Pereira](mailto:[email protected])
### Dataset Summary
MoisesDB is a dataset for source separation. It provides a collection of tracks and their separated stems (vocals, bass, drums, etc.). The dataset is used to evaluate the performance of source separation algorithms.
# Download the data
Please download the dataset at our research [website](https://developer.moises.ai/research), extract it and configure the environment variable `MOISESDB_PATH` accordingly.
```
export MOISESDB_PATH=./moises-db-data
```
The directory structure should be
```
moisesdb:
moisesdb_v0.1
track uuid 0
track uuid 1
.
.
.
```
# Install
You can install this package with
```
pip install git+https://github.com/moises-ai/moises-db.git
```
# Usage
## `MoisesDB`
After downloading and configuring the path for the dataset, you can create an instance of `MoisesDB` to access the tracks. You can also provide the dataset path with the `data_path` argument.
```
from moisesdb.dataset import MoisesDB
db = MoisesDB(
data_path='./moisesdb',
sample_rate=44100
)
```
The `MoisesDB` object has iterator properties that you can use to access all files within the dataset.
```
n_songs = len(db)
track = db[0] # Returns a MoisesDBTrack object
```
## `MoisesDBTrack`
The `MoisesDBTrack` object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem.
You can access all the stems and mixture from the `stem` and `audio` properties. The `stem` property returns a dictionary whith available stems as keys and `nd.array` on values. The `audio` property results in a `nd.array` with the mixture.
```
track = db[0]
stems = track.stems # stems = {'vocals': ..., 'bass': ..., ...}
mixture track.audio # mixture = nd.array
```
The `MoisesDBTrack` object also contains other non-audio information from the track such as:
- `track.id`
- `track.provider`
- `track.artist`
- `track.name`
- `track.genre`
- `track.sources`
- `track.bleedings`
- `track.activity`
The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the `save_stems` method of the `MoisesDBTrack`.
```
track = db[0]
path = './moises-db-stems/0'
track.save_stems(path)
```
# Performance Evaluation
We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the `MoisesDB`. These results are located in `csv` files at the `benchmark` folder.
# Citing
If you used the `MoisesDB` dataset on your research, please cite the following paper.
```
@misc{pereira2023moisesdb,
title={Moisesdb: A dataset for source separation beyond 4-stems},
author={Igor Pereira and Felipe Araújo and Filip Korzeniowski and Richard Vogl},
year={2023},
eprint={2307.15913},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
# Licensing
`MoisesDB` is distributed with the NC-RCL license.
```
"Non-Commercial Research Community license (NC-RCL)
Limited Redistribution: You are permitted to copy and utilize the provided audio material in any medium or format, as long as it is done only for non-commercial purposes within the research community, and the redistribution is conducted solely through the platform moises.ai or other platforms explicitly authorized by the licensor. Redistribution outside the authorized platforms is not allowed without the licensor's written consent.
Attribution: You must give appropriate credit (including the artist's name and the song's title), and provide a link to this license or a notice indicating the terms of this license.
Non-Commercial Use: You cannot use the material for any commercial purposes or financial gain. This includes, but is not limited to, the sale, licensing, or rental of the material, as well as any use where the primary aim is to generate revenue or profits.
No Derivative Works: You cannot create, remix, adapt, or build upon the material, unless explicitly permitted by the artist.
Preservation of Legal Notices: You cannot remove any copyright or other proprietary notices which are included in or attached to the material.
Termination: If you fail to comply with this license, your rights to use the material will be terminated automatically.
Voice Cloning Restriction: You are prohibited from using the vocal stems or any part of the audio material to create a public digital imitation of the artist's voice (e.g: a vocal clone or replica). This includes, but is not limited to, the utilization of voice synthesis technology, deep learning algorithms, and other artificial intelligence-based tools."
```
| wearemusicai/moisesdb | [
"language:en",
"license:other",
"audio",
"music",
"source separation",
"arxiv:2307.15913",
"region:us"
]
| 2023-11-20T22:05:24+00:00 | {"language": ["en"], "license": "other", "pretty_name": "MoisesDB", "tags": ["audio", "music", "source separation"], "license_name": "ncrlc", "license_link": "https://github.com/moises-ai/moises-db"} | 2023-11-27T19:36:27+00:00 | [
"2307.15913"
]
| [
"en"
]
| TAGS
#language-English #license-other #audio #music #source separation #arxiv-2307.15913 #region-us
|
# MoisesDB
Moises Dataset for Source Separation
### Dataset Description
- Homepage: MoisesDB homepage
- Repository: MoisesDB repository
- Paper: Moisesdb: A dataset for source separation beyond 4-stems
- Point of Contact: Igor Pereira
### Dataset Summary
MoisesDB is a dataset for source separation. It provides a collection of tracks and their separated stems (vocals, bass, drums, etc.). The dataset is used to evaluate the performance of source separation algorithms.
# Download the data
Please download the dataset at our research website, extract it and configure the environment variable 'MOISESDB_PATH' accordingly.
The directory structure should be
# Install
You can install this package with
# Usage
## 'MoisesDB'
After downloading and configuring the path for the dataset, you can create an instance of 'MoisesDB' to access the tracks. You can also provide the dataset path with the 'data_path' argument.
The 'MoisesDB' object has iterator properties that you can use to access all files within the dataset.
## 'MoisesDBTrack'
The 'MoisesDBTrack' object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem.
You can access all the stems and mixture from the 'stem' and 'audio' properties. The 'stem' property returns a dictionary whith available stems as keys and 'URL' on values. The 'audio' property results in a 'URL' with the mixture.
The 'MoisesDBTrack' object also contains other non-audio information from the track such as:
- 'URL'
- 'track.provider'
- 'URL'
- 'URL'
- 'URL'
- 'track.sources'
- 'track.bleedings'
- 'track.activity'
The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the 'save_stems' method of the 'MoisesDBTrack'.
# Performance Evaluation
We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the 'MoisesDB'. These results are located in 'csv' files at the 'benchmark' folder.
# Citing
If you used the 'MoisesDB' dataset on your research, please cite the following paper.
# Licensing
'MoisesDB' is distributed with the NC-RCL license.
| [
"# MoisesDB\nMoises Dataset for Source Separation",
"### Dataset Description\n\n- Homepage: MoisesDB homepage\n- Repository: MoisesDB repository\n- Paper: Moisesdb: A dataset for source separation beyond 4-stems\n- Point of Contact: Igor Pereira",
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"## 'MoisesDBTrack'\n\nThe 'MoisesDBTrack' object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem.\n\nYou can access all the stems and mixture from the 'stem' and 'audio' properties. The 'stem' property returns a dictionary whith available stems as keys and 'URL' on values. The 'audio' property results in a 'URL' with the mixture.\n\n\n\nThe 'MoisesDBTrack' object also contains other non-audio information from the track such as:\n- 'URL'\n- 'track.provider'\n- 'URL'\n- 'URL'\n- 'URL'\n- 'track.sources'\n- 'track.bleedings'\n- 'track.activity'\n\nThe stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the 'save_stems' method of the 'MoisesDBTrack'.",
"# Performance Evaluation\n\nWe run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the 'MoisesDB'. These results are located in 'csv' files at the 'benchmark' folder.",
"# Citing\n\nIf you used the 'MoisesDB' dataset on your research, please cite the following paper.",
"# Licensing\n\n'MoisesDB' is distributed with the NC-RCL license."
]
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"# Performance Evaluation\n\nWe run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the 'MoisesDB'. These results are located in 'csv' files at the 'benchmark' folder.",
"# Citing\n\nIf you used the 'MoisesDB' dataset on your research, please cite the following paper.",
"# Licensing\n\n'MoisesDB' is distributed with the NC-RCL license."
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]
|
f87e558b50a8600f68ec5c2fc65eaed458ac259e | # Dataset Card for "redpajama_v2_sample_100M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sade-adrien/redpajama_v2_sample_100M | [
"region:us"
]
| 2023-11-20T23:07:38+00:00 | {"dataset_info": {"features": [{"name": "raw_content", "dtype": "string"}, {"name": "doc_id", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "quality_signals", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1043463774444, "num_examples": 100000000}], "download_size": 226895559008, "dataset_size": 1043463774444}} | 2023-11-21T06:00:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "redpajama_v2_sample_100M"
More Information needed | [
"# Dataset Card for \"redpajama_v2_sample_100M\"\n\nMore Information needed"
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|
8be91794556ef12542883927e049f35215cf0a87 | # Dataset Card for "MedQuad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Nimsi2613/MedQuad | [
"region:us"
]
| 2023-11-21T01:29:01+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": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19604993.268292684, "num_examples": 14800}, {"name": "test", "num_bytes": 794797.0243902439, "num_examples": 600}, {"name": "valid", "num_bytes": 1324661.7073170731, "num_examples": 1000}], "download_size": 10176212, "dataset_size": 21724452.0}} | 2023-11-21T01:29:05+00:00 | []
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#region-us
| # Dataset Card for "MedQuad"
More Information needed | [
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c2e5ddbf912c899ded2af1752c1e7399f6d69adf | # Ultrachat like using Malaysian context
Prepare multiturn dialogue between user and assistant for malaysian context,
1. Astroawani, https://huggingface.co/datasets/malaysia-ai/crawl-astroawani, [ultrachat-astroawani-malay.jsonl](ultrachat-astroawani-malay.jsonl), 60198 rows, 477 MB.
2. Crossref `melayu` papers, https://huggingface.co/datasets/mesolitica/crawl-my-website/resolve/main/melayu-pdf.jsonl, [ultrachat-crossref-melayu-malay.jsonl](ultrachat-crossref-melayu-malay.jsonl), 9959 rows, 187 MB
3. Epenerbitan, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/e-penerbitan.jsonl, [ultrachat-epenerbitan-malay.jsonl](ultrachat-epenerbitan-malay.jsonl), 4567 rows, 73.4 MB
4. gov.my pdf, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/gov.my.jsonl, [ultrachat-gov.my.jsonl](ultrachat-gov.my.jsonl), 10128 rows, 160 MB.
5. JurnalDBP, https://github.com/mesolitica/malaysian-dataset/tree/master/crawl/jurnaldbp, [ultrachat-jurnaldbp-malay.jsonl](ultrachat-jurnaldbp-malay.jsonl), 6440 rows, 115 MB.
6. lom.agc.gov.my.jsonl, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/lom.agc.gov.my.jsonl, [ultrachat-lom-agc.jsonl](ultrachat-lom-agc.jsonl), 8044 rows, 126 MB.
7. MS Wikipedia, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/wikipedia-2023-10-01.jsonl, [ultrachat-ms-wikipedia.jsonl](ultrachat-ms-wikipedia.jsonl), 4408 rows, 57.9 MB
8. Hansard, https://huggingface.co/datasets/malaysia-ai/dedup-text-dataset/resolve/main/hansard.jsonl, [ultrachat-hansard-malay.jsonl](ultrachat-hansard-malay.jsonl), 72538 rows, 862 MB.
9. Textbooks, https://huggingface.co/datasets/open-phi/textbooks, [ultrachat-textbooks.jsonl](ultrachat-textbooks.jsonl), 49842 rows, 1.19 GB.
10. https://maktabahalbakri.com/, [ultrachat-maktabahalbakri.com.jsonl](ultrachat-maktabahalbakri.com.jsonl), 3350 rows, 76.6 MB.
11. https://muftiwp.gov.my/ms/, [ultrachat-muftiwp.gov.my.texts.jsonl](ultrachat-muftiwp.gov.my.texts.jsonl), 3834 rows, 83.6 MB.
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/chatbot/malaysian-ultrachat
There are bad pairs you need to reject, follow https://github.com/mesolitica/malaysian-dataset/blob/master/llm-instruction/prepare-malaysian-ultrachat.ipynb
## Example data
```python
[{'role': 'context',
'content': 'LIGA\xa0Malaysia (Liga-M) terus menerima tamparan hebat apabila UiTM FC sah menarik diri daripada saingan Liga Super musim ini bagi menyusuli jejak Petaling Jaya City.\n\n\n\r\nPerkara itu disahkan sendiri oleh Presiden The Lion Troops, Profesor Dr. Sazili Shahibi, selepas dua musim beraksi di pentas Liga Super.\n\n\n\r\nSelain masalah kewangan, Dr Sazili menjelaskan ada punca lain mengapa pihak pengurusan mengibarkan bendera putih.\n\n\n \n \n\r\n"Ahli Lembaga Pengarah UiTM telah bermesyuarat dan (kami mengambil) keputusan untuk menarik diri daripada liga pada musim depan.\n\n\n\r\n"Kewangan adalah salah faktornya, tapi kami rasa daripada segi kemampuan, ia tidak sesuai buat pemain-pemain kami," katanya ketika dihubungi \nAstro Arena\n\xa0pada Rabu.\n\n\n\r\nSazili juga berhasrat memaklumkan UiTM bakal beralih ke saingan Liga M3 musim ini.\n\n\n\r\nUntuk rekod, UiTM muncul pasukan berstatus universiti pertama yang layak beraksi dalam Liga Super.\n\n\n\r\nDalam pada itu, Liga Bolasepak Malaysia (MFLhanya akan menjelaskan situasi berkenaan menerusi kenyataan rasmi bakal dalam masa terdekat selepas hanya 14 pasukan yang akan bertanding di pentas Liga Super musim ini. -\nStadium Astro',
'content_ms': None,
'indon': False},
{'role': 'user',
'content': 'What are the reasons given by UiTM FC for withdrawing from Liga Malaysia?',
'content_ms': 'Apakah alasan yang diberikan oleh UiTM FC kerana menarik diri daripada Liga Malaysia?',
'indon': False},
{'role': 'assistant',
'content': 'Menurut Presiden UiTM FC, Profesor Dr. Sazili Shahibi, mereka mengambil keputusan untuk menarik diri daripada Liga Malaysia musim ini atas beberapa sebab. Salah satu sebab utamanya adalah masalah kewangan yang dihadapi oleh pasukan. Selain itu, beliau juga menyebut bahawa kemampuan pasukan UiTM tidak sesuai untuk bersaing dalam Liga Super. Selain masalah ini, tidak ada sebab lain yang diberikan dalam laporan tersebut.',
'content_ms': 'Menurut Presiden UiTM FC, Profesor Dr. Sazili Shahibi, mereka membuat keputusan untuk menarik diri daripada Liga Malaysia musim ini atas beberapa sebab. Salah satu sebab utama ialah masalah kewangan yang dihadapi oleh pasukan. Selain itu, beliau juga menyatakan bahawa keupayaan pasukan UiTM tidak sesuai untuk bersaing dalam Liga Super. Selain masalah ini, tiada sebab lain diberikan dalam laporan tersebut.',
'indon': False},
{'role': 'user',
'content': 'Alamak, memang sedihlah tengok UiTM FC tarik diri dari Liga Malaysia. Macam mana pula dengan penonton setia yang selalu sokong pasukan tu? Harap-harap ada jalan penyelesaian untuk masalah kewangan mereka.',
'content_ms': 'Alamak, memang sedih tengok UiTM FC menarik diri dari Liga Malaysia. Bagaimana pula dengan penonton setia yang selalu menyokong pasukan itu? Harap-harap ada jalan penyelesaian untuk masalah kewangan mereka.',
'indon': False},
{'role': 'assistant',
'content': 'Ya, memang sedih melihat UiTM FC menarik diri dari Liga Malaysia. Bagi penonton setia yang sentiasa menyokong pasukan tersebut, tentu mereka juga berasa kecewa. Kami juga berharap ada jalan penyelesaian untuk masalah kewangan yang dihadapi oleh UiTM FC. Mungkin pihak pengurusan boleh mencari sponsor atau menyusun semula strategi kewangan untuk membolehkan pasukan tersebut terus beraksi dalam liga pada masa hadapan. Semoga mereka dapat menemui penyelesaian yang baik dan kembali bersaing di pentas bola sepak Malaysia.',
'content_ms': 'Ya, memang sedih melihat UiTM FC menarik diri dari Liga Malaysia. Bagi penonton setia yang sentiasa menyokong pasukan tersebut, tentu mereka juga berasa kecewa. Kami juga berharap ada jalan penyelesaian untuk masalah kewangan yang dihadapi oleh UiTM FC. Mungkin pihak pengurusan boleh mencari penaja atau menyusun semula strategi kewangan untuk membolehkan pasukan tersebut terus beraksi dalam liga pada masa hadapan. Semoga mereka dapat menemui penyelesaian yang baik dan kembali bersaing di pentas bola sepak Malaysia.',
'indon': False},
{'role': 'user',
'content': 'Ya betul tu, banyak peminat setia UiTM FC yang akan kecewa dengan keputusan mereka tarik diri dari Liga Malaysia. Harapnya pihak pengurusan dapat cari jalan penyelesaian secepat mungkin supaya pasukan tu masih dapat beraksi dalam liga. Saya pun tak sabar nak tengok prestasi UiTM FC lagi musim hadapan. Semoga ada berita baik dalam masa terdekat.',
'content_ms': 'Ya, betul tu. Banyak peminat setia UiTM FC yang akan kecewa dengan keputusan mereka menarik diri dari Liga Malaysia. Harapnya pihak pengurusan dapat mencari jalan penyelesaian secepat mungkin supaya pasukan itu masih dapat beraksi dalam liga. Saya pun tak sabar nak tengok prestasi UiTM FC lagi musim depan. Semoga ada berita baik dalam masa terdekat.',
'indon': False},
{'role': 'assistant',
'content': 'Saya juga setuju dengan anda. Semoga pihak pengurusan UiTM FC dapat mencari jalan penyelesaian secepat mungkin agar pasukan tersebut masih dapat berlaga dalam Liga Malaysia. Benar, banyak peminat yang ingin melihat prestasi UiTM FC musim hadapan. Mari kita nantikan berita baik daripada mereka dalam masa terdekat. Harapnya mereka dapat mengatasi masalah kewangan dan kembali membanggakan para penyokong. Kita terus sokong UiTM FC!',
'content_ms': 'Saya juga bersetuju dengan anda. Semoga pihak pengurusan UiTM FC dapat mencari jalan penyelesaian secepat mungkin supaya pasukan tersebut masih dapat bersaing dalam Liga Malaysia. Benar, ramai peminat yang ingin melihat prestasi UiTM FC musim depan. Mari kita nantikan berita baik daripada mereka dalam masa terdekat. Harapnya mereka dapat mengatasi masalah kewangan dan kembali membanggakan para penyokong. Kita terus sokong UiTM FC!',
'indon': False},
{'role': 'user',
'content': 'Ya lah, harapnya UiTM FC dapat dapat mencari penyelesaian yang cepat. Penyokong setia UiTM FC mesti excited nak tengok pasukan beraksi lagi musim hadapan. Semoga ada berita gembira dalam masa terdekat!',
'content_ms': 'Ya lah, harapnya UiTM FC dapat mencari penyelesaian yang cepat. Penyokong setia UiTM FC mesti teruja untuk melihat pasukan beraksi lagi musim depan. Semoga ada berita gembira dalam masa terdekat!',
'indon': False},
{'role': 'assistant',
'content': 'Betul tu, penyokong setia UiTM FC pasti sangat teruja melihat pasukan beraksi di musim hadapan. Kita harap pihak pengurusan dapat mencari penyelesaian dengan pantas supaya pasukan dapat kembali ke gelanggang. Kita tunggu berita gembira yang akan datang dan terus menyokong UiTM FC dengan penuh semangat. Biar bola sepak Malaysia sentiasa berwarna dengan kehadiran pasukan hebat seperti UiTM FC!',
'content_ms': 'Betul tu, penyokong setia UiTM FC pasti sangat teruja melihat pasukan beraksi pada musim depan. Kita harap pihak pengurusan dapat mencari penyelesaian dengan pantas supaya pasukan dapat kembali ke gelanggang. Kita tunggu berita gembira yang akan datang dan terus menyokong UiTM FC dengan penuh semangat. Biar bola sepak Malaysia sentiasa berwarna dengan kehadiran pasukan hebat seperti UiTM FC!',
'indon': False},
{'role': 'user',
'content': 'Ya betul tu, memang berharap pasukan UiTM FC dapat segera mencari jalan penyelesaian. Setiap pasukan memang perlu sokongan dan kami sebagai penyokong setia akan terus memberikan sokongan kepada pasukan kesayangan kami. Semoga ada berita gembira yang akan datang!',
'content_ms': 'Ya, betul tu. Memang berharap pasukan UiTM FC dapat segera mencari jalan penyelesaian. Setiap pasukan memang perlukan sokongan dan kami sebagai penyokong setia akan terus memberikan sokongan kepada pasukan kesayangan kami. Semoga ada berita gembira yang akan datang!',
'indon': False},
{'role': 'assistant',
'content': 'Setiap pasukan memang memerlukan sokongan daripada penyokong setia seperti anda. Anda adalah aset berharga bagi pasukan UiTM FC. Teruskan menyokong dengan penuh semangat dan harapan. Kita berdoa agar pihak pengurusan dapat mencari jalan penyelesaian dengan segera. Bersama-sama, mari kita nantikan berita gembira yang akan datang dan terus memberikan sokongan kepada pasukan kesayangan kita. UiTM FC pasti akan bangkit dan menyampaikan prestasi yang cemerlang musim hadapan!',
'content_ms': 'Setiap pasukan memang memerlukan sokongan daripada penyokong setia seperti anda. Anda adalah aset berharga bagi pasukan UiTM FC. Teruskan menyokong dengan penuh semangat dan harapan. Kita berdoa agar pihak pengurusan dapat mencari jalan penyelesaian dengan segera. Bersama-sama, mari kita nantikan berita gembira yang akan datang dan terus memberikan sokongan kepada pasukan kesayangan kita. UiTM FC pasti akan bangkit dan menyampaikan prestasi yang cemerlang musim depan!',
'indon': False}]
``` | mesolitica/malaysian-ultrachat | [
"task_categories:conversational",
"language:ms",
"region:us"
]
| 2023-11-21T01:42:53+00:00 | {"language": ["ms"], "task_categories": ["conversational"], "pretty_name": "malaysian-ultrachat"} | 2024-02-02T08:02:59+00:00 | []
| [
"ms"
]
| TAGS
#task_categories-conversational #language-Malay (macrolanguage) #region-us
| # Ultrachat like using Malaysian context
Prepare multiturn dialogue between user and assistant for malaysian context,
1. Astroawani, URL URL, 60198 rows, 477 MB.
2. Crossref 'melayu' papers, URL URL, 9959 rows, 187 MB
3. Epenerbitan, URL URL, 4567 rows, 73.4 MB
4. URL pdf, URL URL, 10128 rows, 160 MB.
5. JurnalDBP, URL URL, 6440 rows, 115 MB.
6. URL, URL URL, 8044 rows, 126 MB.
7. MS Wikipedia, URL URL, 4408 rows, 57.9 MB
8. Hansard, URL URL, 72538 rows, 862 MB.
9. Textbooks, URL URL, 49842 rows, 1.19 GB.
10. URL URL, 3350 rows, 76.6 MB.
11. URL URL, 3834 rows, 83.6 MB.
Notebooks at URL
There are bad pairs you need to reject, follow URL
## Example data
| [
"# Ultrachat like using Malaysian context\n\nPrepare multiturn dialogue between user and assistant for malaysian context,\n\n1. Astroawani, URL URL, 60198 rows, 477 MB.\n2. Crossref 'melayu' papers, URL URL, 9959 rows, 187 MB\n3. Epenerbitan, URL URL, 4567 rows, 73.4 MB\n4. URL pdf, URL URL, 10128 rows, 160 MB.\n5. JurnalDBP, URL URL, 6440 rows, 115 MB.\n6. URL, URL URL, 8044 rows, 126 MB.\n7. MS Wikipedia, URL URL, 4408 rows, 57.9 MB\n8. Hansard, URL URL, 72538 rows, 862 MB.\n9. Textbooks, URL URL, 49842 rows, 1.19 GB.\n10. URL URL, 3350 rows, 76.6 MB.\n11. URL URL, 3834 rows, 83.6 MB.\n\nNotebooks at URL\n\nThere are bad pairs you need to reject, follow URL",
"## Example data"
]
| [
"TAGS\n#task_categories-conversational #language-Malay (macrolanguage) #region-us \n",
"# Ultrachat like using Malaysian context\n\nPrepare multiturn dialogue between user and assistant for malaysian context,\n\n1. Astroawani, URL URL, 60198 rows, 477 MB.\n2. Crossref 'melayu' papers, URL URL, 9959 rows, 187 MB\n3. Epenerbitan, URL URL, 4567 rows, 73.4 MB\n4. URL pdf, URL URL, 10128 rows, 160 MB.\n5. JurnalDBP, URL URL, 6440 rows, 115 MB.\n6. URL, URL URL, 8044 rows, 126 MB.\n7. MS Wikipedia, URL URL, 4408 rows, 57.9 MB\n8. Hansard, URL URL, 72538 rows, 862 MB.\n9. Textbooks, URL URL, 49842 rows, 1.19 GB.\n10. URL URL, 3350 rows, 76.6 MB.\n11. URL URL, 3834 rows, 83.6 MB.\n\nNotebooks at URL\n\nThere are bad pairs you need to reject, follow URL",
"## Example data"
]
| [
26,
224,
4
]
| [
"passage: TAGS\n#task_categories-conversational #language-Malay (macrolanguage) #region-us \n# Ultrachat like using Malaysian context\n\nPrepare multiturn dialogue between user and assistant for malaysian context,\n\n1. Astroawani, URL URL, 60198 rows, 477 MB.\n2. Crossref 'melayu' papers, URL URL, 9959 rows, 187 MB\n3. Epenerbitan, URL URL, 4567 rows, 73.4 MB\n4. URL pdf, URL URL, 10128 rows, 160 MB.\n5. JurnalDBP, URL URL, 6440 rows, 115 MB.\n6. URL, URL URL, 8044 rows, 126 MB.\n7. MS Wikipedia, URL URL, 4408 rows, 57.9 MB\n8. Hansard, URL URL, 72538 rows, 862 MB.\n9. Textbooks, URL URL, 49842 rows, 1.19 GB.\n10. URL URL, 3350 rows, 76.6 MB.\n11. URL URL, 3834 rows, 83.6 MB.\n\nNotebooks at URL\n\nThere are bad pairs you need to reject, follow URL## Example data"
]
|
0758c4f55e17f8f835b72416c11aec3a3f6b257b | # Dataset Card for "train-tokenizor-ds-T5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nlplabtdtu/train-tokenizor-ds-T5 | [
"region:us"
]
| 2023-11-21T02:25:15+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7956427488, "num_examples": 1885715}], "download_size": 2662966309, "dataset_size": 7956427488}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-21T02:29:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "train-tokenizor-ds-T5"
More Information needed | [
"# Dataset Card for \"train-tokenizor-ds-T5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"train-tokenizor-ds-T5\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"train-tokenizor-ds-T5\"\n\nMore Information needed"
]
|
d90a007615354607f444c82fa52e401e19621c4c | Dữ liệu các trường dưới dạng markdown
# Lưu ý:
- Các headding thuộc dạng ATX
- Dữ liệu có table
# Một vài thông tin mà mỗi dòng có thể lưu
Mỗi dòng khả năng cao sẽ có cấu trúc dưới đây.\
Tham khảo qua: [HUST](https://reviewedu.net/school/truong-dai-hoc-bach-khoa-ha-noi-hust) \
Tuy là cấu trúc chung nhưng không phải dòng nào cũng có đầy đủ thông tin hoặc cùng key
```json
{
"thông tin chung": "thông tin liên lạc + website + giới thiệu trường",
"Mục tiêu phát triển": "mục tiêu phát triển + cam kết của trường",
"Lịch sử phát triển": "lịch sử phát triển của trường từ thành lập đến giờ",
"Đội ngũ cán bộ": "",
"Cơ sở vật chất": "",
"Đối tượng và phạm vi tuyển sinh": "",
"Thời gian xét tuyển": "có thể là table",
"Phương thức tuyển sinh": "có thể là table",
"Các ngành tuyển sinh": "có thể là table",
"Học phí": "có thể là table",
"Điểm chuẩn": "có thể là table",
"Những quyền lợi của sinh viên khi theo học tại Trường": "",
"Cơ hội ra trường": "cơ hội nghề nghiệp tương lai",
"tổng kết": "table tổng kết"
}
``` | H4438/full-md-universities | [
"region:us"
]
| 2023-11-21T02:25:18+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "date", "dtype": "string"}, {"name": "university", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "alias", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14349086, "num_examples": 684}], "download_size": 4572000, "dataset_size": 14349086}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-21T10:26:02+00:00 | []
| []
| TAGS
#region-us
| Dữ liệu các trường dưới dạng markdown
# Lưu ý:
- Các headding thuộc dạng ATX
- Dữ liệu có table
# Một vài thông tin mà mỗi dòng có thể lưu
Mỗi dòng khả năng cao sẽ có cấu trúc dưới đây.\
Tham khảo qua: HUST \
Tuy là cấu trúc chung nhưng không phải dòng nào cũng có đầy đủ thông tin hoặc cùng key
| [
"# Lưu ý:\n- Các headding thuộc dạng ATX\n- Dữ liệu có table",
"# Một vài thông tin mà mỗi dòng có thể lưu\nMỗi dòng khả năng cao sẽ có cấu trúc dưới đây.\\\nTham khảo qua: HUST \\\nTuy là cấu trúc chung nhưng không phải dòng nào cũng có đầy đủ thông tin hoặc cùng key"
]
| [
"TAGS\n#region-us \n",
"# Lưu ý:\n- Các headding thuộc dạng ATX\n- Dữ liệu có table",
"# Một vài thông tin mà mỗi dòng có thể lưu\nMỗi dòng khả năng cao sẽ có cấu trúc dưới đây.\\\nTham khảo qua: HUST \\\nTuy là cấu trúc chung nhưng không phải dòng nào cũng có đầy đủ thông tin hoặc cùng key"
]
| [
6,
18,
52
]
| [
"passage: TAGS\n#region-us \n# Lưu ý:\n- Các headding thuộc dạng ATX\n- Dữ liệu có table# Một vài thông tin mà mỗi dòng có thể lưu\nMỗi dòng khả năng cao sẽ có cấu trúc dưới đây.\\\nTham khảo qua: HUST \\\nTuy là cấu trúc chung nhưng không phải dòng nào cũng có đầy đủ thông tin hoặc cùng key"
]
|
2d6de3357589f36cbe3b48b0a3b453735c1241cf | # Dataset Card for "val-tokenizor-ds-T5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nlplabtdtu/val-tokenizor-ds-T5 | [
"region:us"
]
| 2023-11-21T02:29:41+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 885510284, "num_examples": 209524}], "download_size": 296327037, "dataset_size": 885510284}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-21T02:30:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "val-tokenizor-ds-T5"
More Information needed | [
"# Dataset Card for \"val-tokenizor-ds-T5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"val-tokenizor-ds-T5\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"val-tokenizor-ds-T5\"\n\nMore Information needed"
]
|
eb97dbedd97529e0087f3dbd8699fac0702e4c5f | Dữ liệu các trường đại học dưới dạng văn bản
# Lưu ý:
- Bên trong dữ liệu này có table
# Một vài thông tin mà mỗi dòng có thể lưu
Mỗi dòng khả năng cao sẽ có cấu trúc dưới đây.\
Tham khảo qua: [HUST](https://reviewedu.net/school/truong-dai-hoc-bach-khoa-ha-noi-hust) \
Tuy là cấu trúc chung nhưng không phải dòng nào cũng có đầy đủ thông tin hoặc cùng key
```json
{
"thông tin chung": "thông tin liên lạc + website + giới thiệu trường",
"Mục tiêu phát triển": "mục tiêu phát triển + cam kết của trường",
"Lịch sử phát triển": "lịch sử phát triển của trường từ thành lập đến giờ",
"Đội ngũ cán bộ": "",
"Cơ sở vật chất": "",
"Đối tượng và phạm vi tuyển sinh": "",
"Thời gian xét tuyển": "có thể là table",
"Phương thức tuyển sinh": "có thể là table",
"Các ngành tuyển sinh": "có thể là table",
"Học phí": "có thể là table",
"Điểm chuẩn": "có thể là table",
"Những quyền lợi của sinh viên khi theo học tại Trường": "",
"Cơ hội ra trường": "cơ hội nghề nghiệp tương lai",
"tổng kết": "table tổng kết"
}
``` | H4438/full-text-universities | [
"region:us"
]
| 2023-11-21T02:33:32+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "date", "dtype": "string"}, {"name": "alias", "dtype": "string"}, {"name": "university", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9075720, "num_examples": 684}], "download_size": 3651077, "dataset_size": 9075720}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-21T10:28:39+00:00 | []
| []
| TAGS
#region-us
| Dữ liệu các trường đại học dưới dạng văn bản
# Lưu ý:
- Bên trong dữ liệu này có table
# Một vài thông tin mà mỗi dòng có thể lưu
Mỗi dòng khả năng cao sẽ có cấu trúc dưới đây.\
Tham khảo qua: HUST \
Tuy là cấu trúc chung nhưng không phải dòng nào cũng có đầy đủ thông tin hoặc cùng key
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]
|
d9498d5174914ad15327519d3ee7c60e0d64c2b8 | # Dataset Card for "SourceDetection_mb23-music_caps_4sec_wave_type_continuous"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DynamicSuperb/SourceDetection_mb23-music_caps_4sec_wave_type_continuous | [
"region:us"
]
| 2023-11-21T02:56:33+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "audio", "dtype": "audio"}, {"name": "audio2", "dtype": "audio"}, {"name": "file", "dtype": "string"}, {"name": "file2", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "number2", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 2117640266.0, "num_examples": 3000}], "download_size": 2087717436, "dataset_size": 2117640266.0}} | 2023-11-21T04:57:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SourceDetection_mb23-music_caps_4sec_wave_type_continuous"
More Information needed | [
"# Dataset Card for \"SourceDetection_mb23-music_caps_4sec_wave_type_continuous\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"SourceDetection_mb23-music_caps_4sec_wave_type_continuous\"\n\nMore Information needed"
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|
91d3ca0ffe2b77d49e451f359b5a3ee13bb3e1c8 | # Dataset Card for "s-c4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nlplabtdtu/s-c4 | [
"region:us"
]
| 2023-11-21T02:58:13+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "perplexity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 3488628609, "num_examples": 777159}], "download_size": 1734360231, "dataset_size": 3488628609}} | 2023-11-21T03:00:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "s-c4"
More Information needed | [
"# Dataset Card for \"s-c4\"\n\nMore Information needed"
]
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"TAGS\n#region-us \n",
"# Dataset Card for \"s-c4\"\n\nMore Information needed"
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|
769035956a283a5b4f00aeb98a3cd2b5d738e09c | # Dataset Card for "s-wikicorpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nlplabtdtu/s-wikicorpus | [
"region:us"
]
| 2023-11-21T03:00:42+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": 2455296583, "num_examples": 1118180}], "download_size": 1536183681, "dataset_size": 2455296583}} | 2023-11-21T03:03:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "s-wikicorpus"
More Information needed | [
"# Dataset Card for \"s-wikicorpus\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"s-wikicorpus\"\n\nMore Information needed"
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|
eccf2eb0b7073d779b35906d11acd75cc27a9cee | # Dataset Card for "EC_fold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lhallee/EC_fold | [
"region:us"
]
| 2023-11-21T03:35:27+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "seqs", "dtype": "string"}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30167609, "num_examples": 13089}, {"name": "valid", "num_bytes": 3394049, "num_examples": 1465}, {"name": "test", "num_bytes": 3655560, "num_examples": 1604}], "download_size": 9383528, "dataset_size": 37217218}} | 2023-11-21T03:35:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "EC_fold"
More Information needed | [
"# Dataset Card for \"EC_fold\"\n\nMore Information needed"
]
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"TAGS\n#region-us \n",
"# Dataset Card for \"EC_fold\"\n\nMore Information needed"
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|
62cc5197d05c791af3c2521871c78d6d72d6c884 | # Dataset Card for "EC_reg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lhallee/EC_reg | [
"region:us"
]
| 2023-11-21T03:35:35+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "seqs", "dtype": "string"}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26623731, "num_examples": 13090}, {"name": "valid", "num_bytes": 2988422, "num_examples": 1465}, {"name": "test", "num_bytes": 3241706, "num_examples": 1604}], "download_size": 5227567, "dataset_size": 32853859}} | 2023-11-21T03:35:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "EC_reg"
More Information needed | [
"# Dataset Card for \"EC_reg\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
"# Dataset Card for \"EC_reg\"\n\nMore Information needed"
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|
155532b593d39721ae2abe2b39829976f56b9f8e | # Dataset Card for "gpt-generated-news-paragraphs-v1.1"
- This dataset was created solely for the purpose of code testing.
- This dataset was generated from prompting chatGPT to create sample pieces of news setences according to a topic.
- Sample prompt: "generate 50 paragraphs on the topic of "very recent breaking news on wars and conflicts events" with some sample location names. One example: "a missile struck near a residential building in Kiev last night. Russia denied Ukraine's accusations of attacking non-military targets""
- The output paragraphs were then used to construct huggingface dataset.
- Changes from v1.0: added column `class_name` for ease of use in downstream tasks
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | joshuapsa/gpt-generated-news-paragraphs-v1.1 | [
"region:us"
]
| 2023-11-21T03:46:02+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "class_name", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "aviation", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "cybersecurity", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "domestic_unrest_violence", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "extreme_weather", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "forced_labor", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "general_biz_trend", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "individual_accidents_tragedies", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "later_report", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "lawsuit_legal_insurance", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "leisure_other_news", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "maritime", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "pandemics_large_scale_diseases", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "railway", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "strike", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "trade_war_embargos_bans", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "transportation_trends_projects", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "war_conflict", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "warehouse_fire", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "class_index", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "label", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 419816, "num_examples": 720}, {"name": "valid", "num_bytes": 52468, "num_examples": 90}, {"name": "test", "num_bytes": 52223, "num_examples": 90}], "download_size": 179362, "dataset_size": 524507}} | 2023-11-21T03:49:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "gpt-generated-news-paragraphs-v1.1"
- This dataset was created solely for the purpose of code testing.
- This dataset was generated from prompting chatGPT to create sample pieces of news setences according to a topic.
- Sample prompt: "generate 50 paragraphs on the topic of "very recent breaking news on wars and conflicts events" with some sample location names. One example: "a missile struck near a residential building in Kiev last night. Russia denied Ukraine's accusations of attacking non-military targets""
- The output paragraphs were then used to construct huggingface dataset.
- Changes from v1.0: added column 'class_name' for ease of use in downstream tasks
More Information needed | [
"# Dataset Card for \"gpt-generated-news-paragraphs-v1.1\"\n- This dataset was created solely for the purpose of code testing.\n- This dataset was generated from prompting chatGPT to create sample pieces of news setences according to a topic.\n- Sample prompt: \"generate 50 paragraphs on the topic of \"very recent breaking news on wars and conflicts events\" with some sample location names. One example: \"a missile struck near a residential building in Kiev last night. Russia denied Ukraine's accusations of attacking non-military targets\"\"\n- The output paragraphs were then used to construct huggingface dataset.\n- Changes from v1.0: added column 'class_name' for ease of use in downstream tasks\nMore Information needed"
]
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"passage: TAGS\n#region-us \n# Dataset Card for \"gpt-generated-news-paragraphs-v1.1\"\n- This dataset was created solely for the purpose of code testing.\n- This dataset was generated from prompting chatGPT to create sample pieces of news setences according to a topic.\n- Sample prompt: \"generate 50 paragraphs on the topic of \"very recent breaking news on wars and conflicts events\" with some sample location names. One example: \"a missile struck near a residential building in Kiev last night. Russia denied Ukraine's accusations of attacking non-military targets\"\"\n- The output paragraphs were then used to construct huggingface dataset.\n- Changes from v1.0: added column 'class_name' for ease of use in downstream tasks\nMore Information needed"
]
|
8749b74ccc6a19144db0cf37fb33f650a7208e2a | # Dataset Card for "arxiv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vietgpt/arxiv | [
"region:us"
]
| 2023-11-21T03:57:36+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "meta", "struct": [{"name": "timestamp", "dtype": "timestamp[s]"}, {"name": "yymm", "dtype": "string"}, {"name": "arxiv_id", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "url", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 89337072771, "num_examples": 1558306}], "download_size": 40941434576, "dataset_size": 89337072771}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-21T04:52:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "arxiv"
More Information needed | [
"# Dataset Card for \"arxiv\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"arxiv\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"arxiv\"\n\nMore Information needed"
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|
4469c294261aa2c61ed9ce34b7b35a28a891e475 | # Dataset Card for "random25eof_find_passage_train10000_eval100_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random25eof_find_passage_train10000_eval100_rare | [
"region:us"
]
| 2023-11-21T04:01:15+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": 2111428, "num_examples": 20100}, {"name": "validation", "num_bytes": 11904, "num_examples": 100}], "download_size": 707669, "dataset_size": 2123332}} | 2023-11-21T04:01:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random25eof_find_passage_train10000_eval100_rare"
More Information needed | [
"# Dataset Card for \"random25eof_find_passage_train10000_eval100_rare\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"random25eof_find_passage_train10000_eval100_rare\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"random25eof_find_passage_train10000_eval100_rare\"\n\nMore Information needed"
]
|
b34f5fca311f879fc3afa719758be335e2a6838f | # Dataset Card for "random25eof_find_passage_train1000_eval100_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/random25eof_find_passage_train1000_eval100_rare | [
"region:us"
]
| 2023-11-21T04:01:24+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": 219292, "num_examples": 2100}, {"name": "validation", "num_bytes": 11904, "num_examples": 100}], "download_size": 0, "dataset_size": 231196}} | 2023-11-21T04:01:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random25eof_find_passage_train1000_eval100_rare"
More Information needed | [
"# Dataset Card for \"random25eof_find_passage_train1000_eval100_rare\"\n\nMore Information needed"
]
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"TAGS\n#region-us \n",
"# Dataset Card for \"random25eof_find_passage_train1000_eval100_rare\"\n\nMore Information needed"
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30
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"passage: TAGS\n#region-us \n# Dataset Card for \"random25eof_find_passage_train1000_eval100_rare\"\n\nMore Information needed"
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|
4a1bb324202e4ba2c680887f9bc2efc86afd8165 | ## Verified-Camel-KO
이 데이터셋은 https://huggingface.co/datasets/LDJnr/Verified-Camel 의 한국어 번역입니다.
GPT4 Turbo로 번역한 뒤, 약간의 수정을 거쳤습니다.
이 데이터에 대한 방침은 전부 원 저자의 방침을 따릅니다.
## This is the Official Verified Camel dataset. Just over 100 verified examples, and many more coming soon!
- Comprised of over 100 highly filtered and curated examples from specific portions of CamelAI stem datasets.
- These examples are verified to be true by experts in the specific related field, with atleast a bachelors degree in the subject.
- Roughly 30-40% of the originally curated data from CamelAI was found to have atleast minor errors and/or incoherent questions(as determined by experts in said field)
## Purpose?
- This dataset is not intended to be trained on by itself(besides perhaps interesting research purposes) however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!
## Quality filtering and cleaning.
- Extensive cleaning was done to make sure there is no possible instances of overt AI moralizing or related behaviour, such as "As an AI language model" and "September 2021"
- This was done for the initial curation due to the responses being originally created by GPT-4.
## Future Plans & How you can help!
This is a relatively early build amongst the grand plans for the future of what I plan to work on!
In the near future we plan on leveraging the help of even more domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord! | kuotient/Verified-Camel-KO | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:ko",
"license:apache-2.0",
"Physics",
"Biology",
"Math",
"Chemistry",
"Culture",
"Logic",
"region:us"
]
| 2023-11-21T04:03:18+00:00 | {"language": ["ko"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["conversational", "question-answering", "text-generation"], "pretty_name": "Verified-Camel-KO", "tags": ["Physics", "Biology", "Math", "Chemistry", "Culture", "Logic"]} | 2023-11-21T04:20:55+00:00 | []
| [
"ko"
]
| TAGS
#task_categories-conversational #task_categories-question-answering #task_categories-text-generation #size_categories-n<1K #language-Korean #license-apache-2.0 #Physics #Biology #Math #Chemistry #Culture #Logic #region-us
| ## Verified-Camel-KO
이 데이터셋은 URL 의 한국어 번역입니다.
GPT4 Turbo로 번역한 뒤, 약간의 수정을 거쳤습니다.
이 데이터에 대한 방침은 전부 원 저자의 방침을 따릅니다.
## This is the Official Verified Camel dataset. Just over 100 verified examples, and many more coming soon!
- Comprised of over 100 highly filtered and curated examples from specific portions of CamelAI stem datasets.
- These examples are verified to be true by experts in the specific related field, with atleast a bachelors degree in the subject.
- Roughly 30-40% of the originally curated data from CamelAI was found to have atleast minor errors and/or incoherent questions(as determined by experts in said field)
## Purpose?
- This dataset is not intended to be trained on by itself(besides perhaps interesting research purposes) however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!
## Quality filtering and cleaning.
- Extensive cleaning was done to make sure there is no possible instances of overt AI moralizing or related behaviour, such as "As an AI language model" and "September 2021"
- This was done for the initial curation due to the responses being originally created by GPT-4.
## Future Plans & How you can help!
This is a relatively early build amongst the grand plans for the future of what I plan to work on!
In the near future we plan on leveraging the help of even more domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord! | [
"## Verified-Camel-KO\n이 데이터셋은 URL 의 한국어 번역입니다.\n\nGPT4 Turbo로 번역한 뒤, 약간의 수정을 거쳤습니다.\n\n이 데이터에 대한 방침은 전부 원 저자의 방침을 따릅니다.",
"## This is the Official Verified Camel dataset. Just over 100 verified examples, and many more coming soon!\n\n - Comprised of over 100 highly filtered and curated examples from specific portions of CamelAI stem datasets. \n\n - These examples are verified to be true by experts in the specific related field, with atleast a bachelors degree in the subject.\n\n - Roughly 30-40% of the originally curated data from CamelAI was found to have atleast minor errors and/or incoherent questions(as determined by experts in said field)",
"## Purpose?\n\n - This dataset is not intended to be trained on by itself(besides perhaps interesting research purposes) however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!",
"## Quality filtering and cleaning.\n\n - Extensive cleaning was done to make sure there is no possible instances of overt AI moralizing or related behaviour, such as \"As an AI language model\" and \"September 2021\"\n\n - This was done for the initial curation due to the responses being originally created by GPT-4.",
"## Future Plans & How you can help!\n\nThis is a relatively early build amongst the grand plans for the future of what I plan to work on! \n\nIn the near future we plan on leveraging the help of even more domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.\n\nIf you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!"
]
| [
"TAGS\n#task_categories-conversational #task_categories-question-answering #task_categories-text-generation #size_categories-n<1K #language-Korean #license-apache-2.0 #Physics #Biology #Math #Chemistry #Culture #Logic #region-us \n",
"## Verified-Camel-KO\n이 데이터셋은 URL 의 한국어 번역입니다.\n\nGPT4 Turbo로 번역한 뒤, 약간의 수정을 거쳤습니다.\n\n이 데이터에 대한 방침은 전부 원 저자의 방침을 따릅니다.",
"## This is the Official Verified Camel dataset. Just over 100 verified examples, and many more coming soon!\n\n - Comprised of over 100 highly filtered and curated examples from specific portions of CamelAI stem datasets. \n\n - These examples are verified to be true by experts in the specific related field, with atleast a bachelors degree in the subject.\n\n - Roughly 30-40% of the originally curated data from CamelAI was found to have atleast minor errors and/or incoherent questions(as determined by experts in said field)",
"## Purpose?\n\n - This dataset is not intended to be trained on by itself(besides perhaps interesting research purposes) however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!",
"## Quality filtering and cleaning.\n\n - Extensive cleaning was done to make sure there is no possible instances of overt AI moralizing or related behaviour, such as \"As an AI language model\" and \"September 2021\"\n\n - This was done for the initial curation due to the responses being originally created by GPT-4.",
"## Future Plans & How you can help!\n\nThis is a relatively early build amongst the grand plans for the future of what I plan to work on! \n\nIn the near future we plan on leveraging the help of even more domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.\n\nIf you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!"
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| [
"passage: TAGS\n#task_categories-conversational #task_categories-question-answering #task_categories-text-generation #size_categories-n<1K #language-Korean #license-apache-2.0 #Physics #Biology #Math #Chemistry #Culture #Logic #region-us \n## Verified-Camel-KO\n이 데이터셋은 URL 의 한국어 번역입니다.\n\nGPT4 Turbo로 번역한 뒤, 약간의 수정을 거쳤습니다.\n\n이 데이터에 대한 방침은 전부 원 저자의 방침을 따릅니다.## This is the Official Verified Camel dataset. Just over 100 verified examples, and many more coming soon!\n\n - Comprised of over 100 highly filtered and curated examples from specific portions of CamelAI stem datasets. \n\n - These examples are verified to be true by experts in the specific related field, with atleast a bachelors degree in the subject.\n\n - Roughly 30-40% of the originally curated data from CamelAI was found to have atleast minor errors and/or incoherent questions(as determined by experts in said field)## Purpose?\n\n - This dataset is not intended to be trained on by itself(besides perhaps interesting research purposes) however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!## Quality filtering and cleaning.\n\n - Extensive cleaning was done to make sure there is no possible instances of overt AI moralizing or related behaviour, such as \"As an AI language model\" and \"September 2021\"\n\n - This was done for the initial curation due to the responses being originally created by GPT-4."
]
|
3bf746ede3d32d1a6a19ab0a2eebbf795bb55e79 | Dữ liệu các trường đại học + cao đẳng + học viện
# Giải thích các key
```json
{
"privilege": "quyền lợi học sinh",
"aims": "mục tiêu phát triển",
"history": "lịch sử phát triển",
"general_info": "thông tin chung",
"facilities": "mô tả về cơ sở vật chất",
"addmission_method": "phương thức tuyển sinh",
"addmission_target": "đối tượng tuyển sinh",
"input_condition": "điều kiện vào"
}
```
# Lưu ý:
- dữ liệu này chưa có table: tiền học, ngành tuyển sinh, ...
| H4438/dict-universities | [
"region:us"
]
| 2023-11-21T04:11:32+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_condition", "dtype": "string"}, {"name": "privilege", "dtype": "string"}, {"name": "addmission_target", "dtype": "string"}, {"name": "history", "dtype": "string"}, {"name": "aims", "dtype": "string"}, {"name": "addmission_method", "dtype": "string"}, {"name": "facilities", "dtype": "string"}, {"name": "general_info", "dtype": "string"}, {"name": "satisfy", "dtype": "string"}, {"name": "facility_points", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "intro", "dtype": "string"}, {"name": "phone", "dtype": "string"}, {"name": "address", "dtype": "string"}, {"name": "rate", "dtype": "string"}, {"name": "university", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3120439, "num_examples": 616}], "download_size": 1207638, "dataset_size": 3120439}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-22T09:44:23+00:00 | []
| []
| TAGS
#region-us
| Dữ liệu các trường đại học + cao đẳng + học viện
# Giải thích các key
# Lưu ý:
- dữ liệu này chưa có table: tiền học, ngành tuyển sinh, ...
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|
859e524c5e3ee3cb5cb55ab39f35bf671fa22f35 | * 包含 title、正文、中文摘要,可用于训练文本摘要任务
* 论文来自中国知网，版权受限，不能直接公开。下载后请勿上传到公开场合。 | yuyijiong/Chinese_Paper_Abstract | [
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-11-21T05:10:41+00:00 | {"language": ["zh"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"]} | 2023-11-21T05:13:08+00:00 | []
| [
"zh"
]
| TAGS
#size_categories-10K<n<100K #language-Chinese #license-cc-by-nc-4.0 #region-us
| * 包含 title、正文、中文摘要,可用于训练文本摘要任务
* 论文来自中国知网，版权受限，不能直接公开。下载后请勿上传到公开场合。 | []
| [
"TAGS\n#size_categories-10K<n<100K #language-Chinese #license-cc-by-nc-4.0 #region-us \n"
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| [
34
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|
b205dfb9772a19f4cf26664d29e48ca616fd7a9e | # Dataset Card for "QA_Dataset-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rkdeva/QA_Dataset-2 | [
"region:us"
]
| 2023-11-21T05:12:42+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 249203, "num_examples": 103}], "download_size": 112062, "dataset_size": 249203}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-21T05:12:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "QA_Dataset-2"
More Information needed | [
"# Dataset Card for \"QA_Dataset-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
88242e3031b72e12972a4b3b4c7cdc2a9cca53ce | # Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
**Paper or resources for more information:**
[[Paper](https://huggingface.co/papers/2311.08046)] [[Code](https://github.com/PKU-YuanGroup/Chat-UniVi)] | Chat-UniVi/Chat-UniVi-Eval | [
"license:apache-2.0",
"arxiv:2311.08046",
"region:us"
]
| 2023-11-21T05:43:46+00:00 | {"license": "apache-2.0"} | 2023-11-23T02:18:10+00:00 | [
"2311.08046"
]
| []
| TAGS
#license-apache-2.0 #arxiv-2311.08046 #region-us
| # Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Paper or resources for more information:
[Paper] [Code] | [
"# Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding\n\nPaper or resources for more information:\n[Paper] [Code]"
]
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]
|
8c8ca972b9dcffbbd2c8ad18024251378d5f4423 | # LVIS-Instruct4V-mix730k
This is a mixture of our LVIS-Instruct4V dataset with the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG.
Please refer to the Table 7 of [LLaVA 1.5](https://arxiv.org/pdf/2310.03744.pdf) paper for more details.
| X2FD/LVIS-Instruct4V-mix730k | [
"arxiv:2310.03744",
"region:us"
]
| 2023-11-21T06:12:43+00:00 | {} | 2023-11-21T06:22:21+00:00 | [
"2310.03744"
]
| []
| TAGS
#arxiv-2310.03744 #region-us
| # LVIS-Instruct4V-mix730k
This is a mixture of our LVIS-Instruct4V dataset with the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG.
Please refer to the Table 7 of LLaVA 1.5 paper for more details.
| [
"# LVIS-Instruct4V-mix730k\n\nThis is a mixture of our LVIS-Instruct4V dataset with the academic task related data, including ShareGPT, VQAv2, GQA, OKVQA, OCRVQA, AOKVQA, TextCaps, RefCOCO, and VG. \n\nPlease refer to the Table 7 of LLaVA 1.5 paper for more details."
]
| [
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]
|
9353c289716ff7a6ddc6fb3de6a6cc939c5d12b6 | # Dataset Card for "github"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vietgpt/github | [
"region:us"
]
| 2023-11-21T06:24:24+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 196563585312, "num_examples": 28793312}], "download_size": 64794312270, "dataset_size": 196563585312}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-21T07:34:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "github"
More Information needed | [
"# Dataset Card for \"github\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"github\"\n\nMore Information needed"
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| [
6,
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| [
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|
2335aef6b9bc866ad562822b7e47d5f17550fe45 | # Synthetic Kertas 1
Generated using ChatGPT4, originally from,
1. https://huggingface.co/datasets/aisyahhrazak/crawl-soalan
2. https://raw.githubusercontent.com/mesolitica/malaysian-dataset/master/llm-benchmark/tatabahasabm.tripod.com/quiz-tatabahasa.jsonl
3. https://raw.githubusercontent.com/mesolitica/malaysian-dataset/master/llm-benchmark/tatabahasabm.tripod.com-bm-kertas-1/tatabahasabm.tripod.com-bm-kertas1.json
Notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/question-answer/chatgpt4-synthetic-kertas1
- [synthetic-exam.jsonl](synthetic-exam.jsonl), 3612 rows, 606KB.
- [synthetic-latihanbm.jsonl](synthetic-latihanbm.jsonl), 4289 rows, 911 KB.
- [synthetic-tatabahasa-v2.jsonl](synthetic-tatabahasa-v2.jsonl), 5979 rows, 1.12 MB.
- [synthetic-tatabahasa.jsonl](synthetic-tatabahasa.jsonl), 2005 rows, 374 KB.
- [synthetic-tatabahasabm.tripod.com-bm-kertas1.jsonl](synthetic-tatabahasabm.tripod.com-bm-kertas1.jsonl), 439 rows, 650 KB.
## Example data
```python
{'question': '1. ........, kamu sudah pandai bermain gitar sekarang!\nA. Oh\nB. Eh\nC. Hai\nD. Ah',
'answer': 'B'}
``` | mesolitica/chatgpt4-kertas1 | [
"language:ms",
"region:us"
]
| 2023-11-21T06:36:57+00:00 | {"language": ["ms"], "pretty_name": "chatgpt4-malay-kertas1"} | 2024-02-02T06:13:13+00:00 | []
| [
"ms"
]
| TAGS
#language-Malay (macrolanguage) #region-us
| # Synthetic Kertas 1
Generated using ChatGPT4, originally from,
1. URL
2. URL
3. URL
Notebooks at URL
- URL, 3612 rows, 606KB.
- URL, 4289 rows, 911 KB.
- URL, 5979 rows, 1.12 MB.
- URL, 2005 rows, 374 KB.
- URL, 439 rows, 650 KB.
## Example data
| [
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"## Example data"
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]
|
c6d4a59e44b48d5f7b74895fc98a3b69931616cd |
# Licenses
> Comprehensive Dataset of Open Source Licenses
| gnumanth/licenses | [
"region:us"
]
| 2023-11-21T06:47:28+00:00 | {"dataset_info": {"features": [{"name": "other_names", "list": [{"name": "name", "dtype": "string"}, {"name": "note", "dtype": "string"}]}, {"name": "keywords", "sequence": "string"}, {"name": "text", "list": [{"name": "media_type", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "identifiers", "list": [{"name": "identifier", "dtype": "string"}, {"name": "scheme", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "links", "list": [{"name": "note", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "superseded_by", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20834.494382022473, "num_examples": 66}, {"name": "test", "num_bytes": 7260.505617977528, "num_examples": 23}], "download_size": 25625, "dataset_size": 28095.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-11-21T06:52:10+00:00 | []
| []
| TAGS
#region-us
|
# Licenses
> Comprehensive Dataset of Open Source Licenses
| [
"# Licenses\n> Comprehensive Dataset of Open Source Licenses"
]
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|
ac71e03430076e0a3e960eb026eada32c1f5028e | # Dataset Card for "Tiger-MathInstruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nguyenthanhdo/Tiger-MathInstruct | [
"region:us"
]
| 2023-11-21T07:29:41+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "vi", "path": "data/vi-*"}, {"split": "en", "path": "data/en-*"}]}], "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "vi", "num_bytes": 227116640, "num_examples": 262040}, {"name": "en", "num_bytes": 188743056, "num_examples": 262040}], "download_size": 207887300, "dataset_size": 415859696}} | 2023-11-21T07:29:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Tiger-MathInstruct"
More Information needed | [
"# Dataset Card for \"Tiger-MathInstruct\"\n\nMore Information needed"
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| [
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d5f506b2e373050d7860fdd0006c056e36234c3b | # Dataset Card for "Tiger-MathInstruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nlp-vtcc/Tiger-MathInstruct | [
"region:us"
]
| 2023-11-21T07:29:54+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "vi", "path": "data/vi-*"}, {"split": "en", "path": "data/en-*"}]}], "dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "vi", "num_bytes": 227116640, "num_examples": 262040}, {"name": "en", "num_bytes": 188743056, "num_examples": 262040}], "download_size": 207887300, "dataset_size": 415859696}} | 2023-11-21T07:30:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Tiger-MathInstruct"
More Information needed | [
"# Dataset Card for \"Tiger-MathInstruct\"\n\nMore Information needed"
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| [
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|
7817fa8802082aca69533cc0053e7e5edb59ca8b | # Dataset Card for "ko_wiki_sentences_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | deokhk/ko_wiki_sentences_100000 | [
"region:us"
]
| 2023-11-21T07:37:05+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17061018, "num_examples": 100000}, {"name": "dev", "num_bytes": 174799, "num_examples": 1000}], "download_size": 10348119, "dataset_size": 17235817}} | 2023-11-21T07:37:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ko_wiki_sentences_100000"
More Information needed | [
"# Dataset Card for \"ko_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ko_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ko_wiki_sentences_100000\"\n\nMore Information needed"
]
|
7a57755ab9e642144af6a903be590014beda64be | # Dataset Card for "en_wiki_sentences_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | deokhk/en_wiki_sentences_100000 | [
"region:us"
]
| 2023-11-21T07:37:53+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12629524, "num_examples": 100000}, {"name": "dev", "num_bytes": 122796, "num_examples": 1000}], "download_size": 7913615, "dataset_size": 12752320}} | 2023-11-21T07:37:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en_wiki_sentences_100000"
More Information needed | [
"# Dataset Card for \"en_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en_wiki_sentences_100000\"\n\nMore Information needed"
]
|
102ab8d2af2789d6798d653c22ef6b7a6fc0948a | # Dataset Card for "zh_wiki_sentences_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | deokhk/zh_wiki_sentences_100000 | [
"region:us"
]
| 2023-11-21T07:38:08+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13169192, "num_examples": 100000}, {"name": "dev", "num_bytes": 131900, "num_examples": 1000}], "download_size": 9348002, "dataset_size": 13301092}} | 2023-11-21T07:38:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "zh_wiki_sentences_100000"
More Information needed | [
"# Dataset Card for \"zh_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"zh_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"zh_wiki_sentences_100000\"\n\nMore Information needed"
]
|
33a94f3e0d9bedea13026ec2cfde428e84f9dbbe | # Dataset Card for "am_wiki_sentences_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | deokhk/am_wiki_sentences_100000 | [
"region:us"
]
| 2023-11-21T07:38:22+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15005045, "num_examples": 100000}, {"name": "dev", "num_bytes": 114806, "num_examples": 1000}], "download_size": 7271644, "dataset_size": 15119851}} | 2023-11-21T07:38:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "am_wiki_sentences_100000"
More Information needed | [
"# Dataset Card for \"am_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"am_wiki_sentences_100000\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"am_wiki_sentences_100000\"\n\nMore Information needed"
]
|
288bf626248335910f03471dfd4743260cdffdb8 | # Dataset Card for "Dermnet-Test-4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rkdeva/Dermnet-Test-4 | [
"region:us"
]
| 2023-11-21T07:40:18+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}, {"name": "class", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 376800274.178, "num_examples": 3937}], "download_size": 370157597, "dataset_size": 376800274.178}} | 2023-11-21T07:45:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Dermnet-Test-4"
More Information needed | [
"# Dataset Card for \"Dermnet-Test-4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Dermnet-Test-4\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Dermnet-Test-4\"\n\nMore Information needed"
]
|
940bc9e106ccdb0d5a181c0331216f9c87a0c367 |
# Bangumi Image Base of Accel World
This is the image base of bangumi Accel World, we detected 34 characters, 2098 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 146 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 8 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 614 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 140 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 55 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 27 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 8 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 58 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 47 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 16 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 21 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 99 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 13 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 8 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 23 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 10 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 27 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 429 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 14 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 17 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 14 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 28 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 6 | [Download](22/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 23 | 10 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 13 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 14 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 20 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 9 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 6 | [Download](28/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 29 | 5 | [Download](29/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 30 | 5 | [Download](30/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 31 | 10 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 7 | [Download](32/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 171 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/accelworld | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| 2023-11-21T07:50:52+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-21T09:52:30+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Accel World
=================================
This is the image base of bangumi Accel World, we detected 34 characters, 2098 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| []
| [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
]
| [
25
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
]
|
b78fd733e963592394ae343b3ebe219fb03fca33 |
# Bangumi Image Base of Tengen Toppa
This is the image base of bangumi Tengen Toppa, we detected 40 characters, 3081 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 107 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 137 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 104 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 29 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 33 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 36 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 28 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 359 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 73 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 133 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 151 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 32 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 44 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 78 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 25 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 22 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 17 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 44 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 104 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 51 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 37 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 339 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 32 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 11 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 16 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 16 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 53 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 50 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 59 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 23 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 36 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 28 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 19 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 9 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 73 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 13 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 616 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/tengentoppa | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| 2023-11-21T07:51:21+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-21T10:35:47+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Tengen Toppa
==================================
This is the image base of bangumi Tengen Toppa, we detected 40 characters, 3081 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| []
| [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
]
| [
25
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
]
|
0c0521e8863cf1fa6ee15f377dab124a07e6735b |
# Bangumi Image Base of Haikyuu!!
This is the image base of bangumi Haikyuu!!, we detected 63 characters, 19919 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 4856 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 1384 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 631 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1893 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 585 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 1375 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 221 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 169 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 405 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 336 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 304 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 323 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 210 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 769 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 149 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 166 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 89 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 419 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 90 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 78 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 35 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 76 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 97 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 85 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 73 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 2198 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 611 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 340 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 163 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 84 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 22 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 46 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 27 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 102 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 41 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 52 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 29 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 28 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 110 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 20 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 18 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 37 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 87 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 10 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 15 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 11 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 13 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 34 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 34 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 50 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 17 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 285 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 107 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 299 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 11 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 19 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 24 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 14 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 20 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 20 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 34 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 10 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 59 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/haikyuu | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
]
| 2023-11-21T07:51:49+00:00 | {"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]} | 2023-11-21T17:33:41+00:00 | []
| []
| TAGS
#size_categories-10K<n<100K #license-mit #art #region-us
| Bangumi Image Base of Haikyuu!!
===============================
This is the image base of bangumi Haikyuu!!, we detected 63 characters, 19919 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| []
| [
"TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
]
| [
25
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
]
|
3cfcc75f1cab921015002514e858f6b87f7a4bfc |
# Dataset Card for [msc]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **https://smc.org.in**
- **https://gitlab.com/smc/msc-reviewed-speech**
- **https://blog.smc.org.in/malayalam-speech-corpus/**
- **Point of Contact: Kavya Manohar**
### Dataset Summary
- 1541 speech samples
- 75 speech contributors
- 1:38:16 hours of speech
- 482 unique sentences
- 1400 unique words
- 553 unique syllables
- 48 unique phonemes
For more detailed analysis see the python notebook provided [here](https://gitlab.com/smc/msc-reviewed-speech/-/blob/master/analysis/EDA.ipynb)
### Supported Tasks and Leaderboards
Automatic Speech Recognition system development, gender and age identification of speakers
### Languages
Malayalam
## Dataset Structure
- file_name
- speechid
- speaker_id
- review_score
- transcript
- category (optional speech category)
- speaker_gender (optionally self declared)
- speaker_age (optionally self declared)
### Data Instances
### Data Fields
### Data Splits
So specific Splits
## Dataset Creation
The speech data is collected from volunteer users who read and record their speech through a [web application](https://msc.smc.org.in) using their personal devices. The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.
### Curation Rationale
The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.
### Curation Rationale
Those speech samples with at least three positive reviews are included in this dataset.
### Source Data
#### Initial Data Collection and Normalization
The speech data is collected from volunteer contributors who read and record their speech through a [web application](https://msc.smc.org.in). The users optionally provide name, age and gender. There is no further verification. Sentences to read out are curated by MSC Admin. The speech samples are reviewed by other users.
### Personal and Sensitive Information
Every speaker is identified by a unique alphanumeric id and age and gender are published if the speaker has voluntarily published them.
## Considerations for Using the Data
### Social Impact of Dataset
Read speech corpus, recorded in natural environments by the users.
### Dataset Curators
Kavya Manohar
### Licensing Information
CC-BY-SA 4.0
### Citation Information
### Contributions
http://msc.smc.org.in/ | smcproject/MSC | [
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:ml",
"license:cc-by-4.0",
"doi:10.57967/hf/1373",
"region:us"
]
| 2023-11-21T07:51:57+00:00 | {"language": ["ml"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "SMC Malayalam Speech Corpus", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "speechid", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "review_score", "dtype": "int64"}, {"name": "transcript", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "speaker_gender", "dtype": "string"}, {"name": "speaker_age", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 579920220.506, "num_examples": 1541}], "download_size": 422956016, "dataset_size": 579920220.506}} | 2023-11-21T09:49:54+00:00 | []
| [
"ml"
]
| TAGS
#task_categories-automatic-speech-recognition #size_categories-1K<n<10K #language-Malayalam #license-cc-by-4.0 #doi-10.57967/hf/1373 #region-us
|
# Dataset Card for [msc]
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- URL
- URL
- URL
- Point of Contact: Kavya Manohar
### Dataset Summary
- 1541 speech samples
- 75 speech contributors
- 1:38:16 hours of speech
- 482 unique sentences
- 1400 unique words
- 553 unique syllables
- 48 unique phonemes
For more detailed analysis see the python notebook provided here
### Supported Tasks and Leaderboards
Automatic Speech Recognition system development, gender and age identification of speakers
### Languages
Malayalam
## Dataset Structure
- file_name
- speechid
- speaker_id
- review_score
- transcript
- category (optional speech category)
- speaker_gender (optionally self declared)
- speaker_age (optionally self declared)
### Data Instances
### Data Fields
### Data Splits
So specific Splits
## Dataset Creation
The speech data is collected from volunteer users who read and record their speech through a web application using their personal devices. The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.
### Curation Rationale
The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.
### Curation Rationale
Those speech samples with at least three positive reviews are included in this dataset.
### Source Data
#### Initial Data Collection and Normalization
The speech data is collected from volunteer contributors who read and record their speech through a web application. The users optionally provide name, age and gender. There is no further verification. Sentences to read out are curated by MSC Admin. The speech samples are reviewed by other users.
### Personal and Sensitive Information
Every speaker is identified by a unique alphanumeric id and age and gender are published if the speaker has voluntarily published them.
## Considerations for Using the Data
### Social Impact of Dataset
Read speech corpus, recorded in natural environments by the users.
### Dataset Curators
Kavya Manohar
### Licensing Information
CC-BY-SA 4.0
### Contributions
URL | [
"# Dataset Card for [msc]",
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"## Dataset Description\n\n- URL\n- URL\n- URL\n- Point of Contact: Kavya Manohar",
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"### Curation Rationale\n\nThe recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.",
"### Curation Rationale\n\nThose speech samples with at least three positive reviews are included in this dataset.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nThe speech data is collected from volunteer contributors who read and record their speech through a web application. The users optionally provide name, age and gender. There is no further verification. Sentences to read out are curated by MSC Admin. The speech samples are reviewed by other users.",
"### Personal and Sensitive Information\n\nEvery speaker is identified by a unique alphanumeric id and age and gender are published if the speaker has voluntarily published them.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nRead speech corpus, recorded in natural environments by the users.",
"### Dataset Curators\n\nKavya Manohar",
"### Licensing Information\n\nCC-BY-SA 4.0",
"### Contributions\n\nURL"
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"## Dataset Description\n\n- URL\n- URL\n- URL\n- Point of Contact: Kavya Manohar",
"### Dataset Summary\n\n\n\n- 1541 speech samples\n- 75 speech contributors\n- 1:38:16 hours of speech\n- 482 unique sentences\n- 1400 unique words\n- 553 unique syllables\n- 48 unique phonemes\n\nFor more detailed analysis see the python notebook provided here",
"### Supported Tasks and Leaderboards\n\nAutomatic Speech Recognition system development, gender and age identification of speakers",
"### Languages\n\nMalayalam",
"## Dataset Structure\n\n- file_name\n- speechid\n- speaker_id\n- review_score\n- transcript\n- category (optional speech category)\n- speaker_gender (optionally self declared)\n- speaker_age (optionally self declared)",
"### Data Instances",
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"## Dataset Creation\n\nThe speech data is collected from volunteer users who read and record their speech through a web application using their personal devices. The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.",
"### Curation Rationale\n\nThe recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.",
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"### Source Data",
"#### Initial Data Collection and Normalization\n\nThe speech data is collected from volunteer contributors who read and record their speech through a web application. The users optionally provide name, age and gender. There is no further verification. Sentences to read out are curated by MSC Admin. The speech samples are reviewed by other users.",
"### Personal and Sensitive Information\n\nEvery speaker is identified by a unique alphanumeric id and age and gender are published if the speaker has voluntarily published them.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nRead speech corpus, recorded in natural environments by the users.",
"### Dataset Curators\n\nKavya Manohar",
"### Licensing Information\n\nCC-BY-SA 4.0",
"### Contributions\n\nURL"
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"passage: TAGS\n#task_categories-automatic-speech-recognition #size_categories-1K<n<10K #language-Malayalam #license-cc-by-4.0 #doi-10.57967/hf/1373 #region-us \n# Dataset Card for [msc]## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- URL\n- URL\n- URL\n- Point of Contact: Kavya Manohar### Dataset Summary\n\n\n\n- 1541 speech samples\n- 75 speech contributors\n- 1:38:16 hours of speech\n- 482 unique sentences\n- 1400 unique words\n- 553 unique syllables\n- 48 unique phonemes\n\nFor more detailed analysis see the python notebook provided here### Supported Tasks and Leaderboards\n\nAutomatic Speech Recognition system development, gender and age identification of speakers### Languages\n\nMalayalam## Dataset Structure\n\n- file_name\n- speechid\n- speaker_id\n- review_score\n- transcript\n- category (optional speech category)\n- speaker_gender (optionally self declared)\n- speaker_age (optionally self declared)### Data Instances### Data Fields### Data Splits\n\nSo specific Splits## Dataset Creation\n\nThe speech data is collected from volunteer users who read and record their speech through a web application using their personal devices. The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.### Curation Rationale\n\nThe recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published."
]
|
735ae600ba6acf36a0cb98d8df940af654237f20 | Dataset format -
"<s>[INST]<<SYS>> ###Instruction: <</SYS>> ###Context: ###Question: [/INST] ###Answer: </s>"
| HarshalBhg/QA_dataset1 | [
"region:us"
]
| 2023-11-21T08:21:45+00:00 | {} | 2023-11-21T11:59:44+00:00 | []
| []
| TAGS
#region-us
| Dataset format -
"<s>[INST]<<SYS>> ###Instruction: <</SYS>> ###Context: ###Question: [/INST] ###Answer: </s>"
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
|
4cec2892fab1006d2d4cd2b6bfef51fb9b90f1c2 | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Howard001/github-issues | [
"region:us"
]
| 2023-11-21T08:25:30+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "dtype": "null"}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2619287, "num_examples": 200}], "download_size": 463750, "dataset_size": 2619287}} | 2023-11-21T08:25:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "github-issues"
More Information needed | [
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
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| [
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|
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