id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R0bk/XFUN | R0bk | 2022-01-31T06:08:56Z | 263 | 0 | null | [
"license:mit",
"region:us"
] | 2022-01-31T06:08:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: mit
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/scala-nb | ScandEval | 2023-07-05T09:49:50Z | 263 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:nb",
"language:no",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-07-05T09:49:50Z | 2022-06-27T15:13:56.000Z | 2022-06-27T15:13:56 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- nb
- 'no'
size_categories:
- 1K<n<10K
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/TNews-classification | C-MTEB | 2023-07-28T13:31:30Z | 263 | 0 | null | [
"region:us"
] | 2023-07-28T13:31:30Z | 2023-07-28T13:31:12.000Z | 2023-07-28T13:31:12 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '100'
'1': '101'
'2': '102'
'3': '103'
'4': '104'
'5': '106'
'6': '107'
'7': '108'
'8': '109'
'9': '110'
'10': '112'
'11': '113'
'12': '114'
'13': '115'
'14': '116'
- name: idx
dtype: int32
splits:
- name: test
num_bytes: 810970
num_examples: 10000
- name: train
num_bytes: 4245677
num_examples: 53360
- name: validation
num_bytes: 797922
num_examples: 10000
download_size: 4697191
dataset_size: 5854569
---
# Dataset Card for "TNews-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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g-ronimo/lfpodcast | g-ronimo | 2023-08-27T10:12:54Z | 262 | 1 | null | [
"region:us"
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open-llm-leaderboard/details_golaxy__gogpt-7b-bloom | open-llm-leaderboard | 2023-10-14T21:01:45Z | 260 | 0 | null | [
"region:us"
] | 2023-10-14T21:01:45Z | 2023-08-17T23:54:23.000Z | 2023-08-17T23:54:23 | ---
pretty_name: Evaluation run of golaxy/gogpt-7b-bloom
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [golaxy/gogpt-7b-bloom](https://huggingface.co/golaxy/gogpt-7b-bloom) 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 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_golaxy__gogpt-7b-bloom\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-14T21:01:38.341280](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt-7b-bloom/blob/main/results_2023-10-14T21-01-38.341280.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2214765100671141,\n\
\ \"em_stderr\": 0.004252451287967787,\n \"f1\": 0.25772336409395996,\n\
\ \"f1_stderr\": 0.00428261897007673,\n \"acc\": 0.31452249408050514,\n\
\ \"acc_stderr\": 0.006788199951115784\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.2214765100671141,\n \"em_stderr\": 0.004252451287967787,\n\
\ \"f1\": 0.25772336409395996,\n \"f1_stderr\": 0.00428261897007673\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6290449881610103,\n\
\ \"acc_stderr\": 0.013576399902231568\n }\n}\n```"
repo_url: https://huggingface.co/golaxy/gogpt-7b-bloom
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|arc:challenge|25_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T07_35_20.075381
path:
- '**/details_harness|drop|3_2023-09-17T07-35-20.075381.parquet'
- split: 2023_10_14T21_01_38.341280
path:
- '**/details_harness|drop|3_2023-10-14T21-01-38.341280.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-14T21-01-38.341280.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T07_35_20.075381
path:
- '**/details_harness|gsm8k|5_2023-09-17T07-35-20.075381.parquet'
- split: 2023_10_14T21_01_38.341280
path:
- '**/details_harness|gsm8k|5_2023-10-14T21-01-38.341280.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-14T21-01-38.341280.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hellaswag|10_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T10:56:27.356745.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-31T10:56:27.356745.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-31T10:56:27.356745.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T07_35_20.075381
path:
- '**/details_harness|winogrande|5_2023-09-17T07-35-20.075381.parquet'
- split: 2023_10_14T21_01_38.341280
path:
- '**/details_harness|winogrande|5_2023-10-14T21-01-38.341280.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-14T21-01-38.341280.parquet'
- config_name: results
data_files:
- split: 2023_07_31T10_56_27.356745
path:
- results_2023-07-31T10:56:27.356745.parquet
- split: 2023_09_17T07_35_20.075381
path:
- results_2023-09-17T07-35-20.075381.parquet
- split: 2023_10_14T21_01_38.341280
path:
- results_2023-10-14T21-01-38.341280.parquet
- split: latest
path:
- results_2023-10-14T21-01-38.341280.parquet
---
# Dataset Card for Evaluation run of golaxy/gogpt-7b-bloom
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/golaxy/gogpt-7b-bloom
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [golaxy/gogpt-7b-bloom](https://huggingface.co/golaxy/gogpt-7b-bloom) 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 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_golaxy__gogpt-7b-bloom",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-14T21:01:38.341280](https://huggingface.co/datasets/open-llm-leaderboard/details_golaxy__gogpt-7b-bloom/blob/main/results_2023-10-14T21-01-38.341280.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2214765100671141,
"em_stderr": 0.004252451287967787,
"f1": 0.25772336409395996,
"f1_stderr": 0.00428261897007673,
"acc": 0.31452249408050514,
"acc_stderr": 0.006788199951115784
},
"harness|drop|3": {
"em": 0.2214765100671141,
"em_stderr": 0.004252451287967787,
"f1": 0.25772336409395996,
"f1_stderr": 0.00428261897007673
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6290449881610103,
"acc_stderr": 0.013576399902231568
}
}
```
### 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] | [
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liangzid/glue_prompts | liangzid | 2023-11-18T07:46:42Z | 260 | 0 | null | [
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uitnlp/vietnamese_students_feedback | uitnlp | 2022-10-13T15:39:37Z | 259 | 9 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:topic-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:vi",
"license:unknown",
"region:us"
] | 2022-10-13T15:39:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- vi
license:
- unknown
multilinguality:
- monolingual
pretty_name: "Vietnamese Students\u2019 Feedback Corpus"
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- topic-classification
---
# Dataset Card for Vietnamese Students’ Feedback Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://sites.google.com/uit.edu.vn/uit-nlp/datasets-projects#h.p_4Brw8L-cbfTe
- **Repository:**
- **Paper:** [UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis](https://www.researchgate.net/publication/329645066_UIT-VSFC_Vietnamese_Students'_Feedback_Corpus_for_Sentiment_Analysis)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different
research fields between sentiment analysis and education.
Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are
human-annotated with two different tasks: sentiment-based and topic-based classifications.
To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language of the dataset text sentence is Vietnamese (`vi`).
## Dataset Structure
### Data Instances
An instance example:
```
{
'sentence': 'slide giáo trình đầy đủ .',
'sentiment': 2,
'topic': 1
}
```
### Data Fields
- `sentence` (str): Text sentence.
- `sentiment`: Sentiment class, with values 0 (negative), 1 (neutral) and 2 (positive).
- `topic`: Topic class, with values 0 (lecturer), 1 (training_program), 2 (facility) and 3 (others).
### Data Splits
The dataset is split in train, validation and test.
| | Tain | Validation | Test |
|--------------------|------:|-----------:|-----:|
| Number of examples | 11426 | 1583 | 3166 |
## 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
Unknown.
### Citation Information
```
@InProceedings{8573337,
author={Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu X. V. and Truong, Tham T. H. and Nguyen, Ngan Luu-Thuy},
booktitle={2018 10th International Conference on Knowledge and Systems Engineering (KSE)},
title={UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis},
year={2018},
volume={},
number={},
pages={19-24},
doi={10.1109/KSE.2018.8573337}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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linxinyuan/cola | linxinyuan | 2022-06-08T07:26:13Z | 259 | 1 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
anon8231489123/ShareGPT_Vicuna_unfiltered | anon8231489123 | 2023-04-12T05:23:59Z | 259 | 607 | null | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-04-12T05:23:59Z | 2023-04-02T05:30:31.000Z | 2023-04-02T05:30:31 | ---
license: apache-2.0
language:
- en
---
**Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.**
**Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c**
Two choices:
- Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
- Has instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json
The choice is yours. The first dataset may go to far and remove valuable data. The second is better for when the AI asks for clarification, but it also may refuse to do stuff like browse the internet, which it actually may be able to do with certain langchain implementations. These are important things to think about before training.
~100k ShareGPT conversations narrowed down to 53k by:
* Removing non-english conversations
* Removing excessive unicode (indicative of Chinese or Korean text, usually)
* Removing excessive repeated characters
* Removing various instances "AI Moralizing". Conversations with these phrases were removed (and a few others that can't be mentioned here):
"text-based AI language model",
"domestic violence",
"please refrain",
"derogatory",
"inappropriate",
"offensive",
"racism",
"racist",
"racial",
"discriminate",
"discriminatory",
"discrimination",
"sexist",
"sexism",
"unacceptable",
"inclusive workplace",
"lgbt",
"morals",
"ethics",
"ethical",
"legality",
"illegal",
"illegality",
"hateful",
"harmful",
"it is never okay",
"It is important to",
"It's important to",
"real-world consequences",
"hate speech",
"glorify",
"not be appropriate",
"supremacist",
"extremist",
"responsible AI",
"AI principles",
"AI assistant",
"an AI language",
"ableist",
"hurtful",
"gender stereotype",
"gender inequality",
"underrepresentation",
"safe spaces",
"gender-based",
"inclusivity",
"feminist",
"feminism",
"transgender",
"empowerment",
"communist",
"capitalism",
"stereotypes",
"biases",
"bias",
"Microaggression",
"prioritize human safety",
"as a language model",
"as an AI language model",
"As a large language model",
"As an AI",
"ethical principles",
"consensual",
"it is not appropriate",
"it's not appropriate",
"I cannot fulfill your request",
"harmful to human beings",
"ethical guidelines",
"my guidelines",
"prioritize user safety",
"adhere to ethical guidelines",
"harmful consequences",
"potentially harmful",
"dangerous activities",
"promote safety",
"well-being of all users",
"responsible information sharing",
"jeopardize the safety",
"illegal actions or intentions",
"undermine the stability",
"promote the well-being",
"illegal activities or actions",
"adherence to the law",
"potentially be harmful",
"illegal substances or activities",
"committed to promoting",
"safe information",
"lawful information",
"cannot provide guidance",
"cannot provide information",
"unable to offer assistance",
"cannot engage in discussions",
"programming prohibits",
"follow ethical guidelines",
"ensure the safety",
"involves an illegal subject",
"prioritize safety",
"illegal subject",
"prioritize user well-being",
"cannot support or promote",
"activities that could harm",
"pose a risk to others",
"against my programming",
"activities that could undermine",
"potentially dangerous",
"not within the scope",
"designed to prioritize safety",
"not able to provide",
"maintain user safety",
"adhere to safety guidelines",
"dangerous or harmful",
"cannot provide any information",
"focus on promoting safety"
* Conversations split into 2048 token chunks as described here: https://github.com/lm-sys/FastChat/blob/main/docs/commands/data_cleaning.md
This should be fully ready to train an unfiltered english Vicuna model based on the procedure here: https://github.com/lm-sys/FastChat/ | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/CLSClusteringS2S | C-MTEB | 2023-07-27T17:29:54Z | 259 | 0 | null | [
"region:us"
] | 2023-07-27T17:29:54Z | 2023-07-27T17:29:48.000Z | 2023-07-27T17:29:48 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 6895612
num_examples: 10
download_size: 4483035
dataset_size: 6895612
---
# Dataset Card for "CLSClusteringS2S"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ASIDS/alpaca-cleaned-ru | ASIDS | 2023-10-04T14:26:17Z | 259 | 0 | null | [
"task_categories:text-generation",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:yahma/alpaca-cleaned",
"language:ru",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] | 2023-10-04T14:26:17Z | 2023-10-04T09:52:39.000Z | 2023-10-04T09:52:39 |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: iteration
dtype: uint32
splits:
- name: train
num_bytes: 74829755.0
num_examples: 51760
download_size: 36596664
dataset_size: 74829755.0
license: cc-by-4.0
language:
- ru
multilinguality:
- monolingual
tags:
- instruction-finetuning
pretty_name: alpaca-cleaned-ru
task_categories:
- text-generation
size_categories:
- 10K<n<100K
source_datasets:
- yahma/alpaca-cleaned
language_creators:
- translated
---
# alpaca-cleaned-ru
converter for autotrain from [d0rj/alpaca-cleaned-ru](https://huggingface.co/datasets/d0rj/alpaca-cleaned-ru)
Translated version of [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) into Russian.
## Dataset Description
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
- **Repository:** https://huggingface.co/datasets/d0rj/alpaca-cleaned-ru | [
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wdc/products-2017 | wdc | 2022-10-23T05:50:24Z | 258 | 2 | wdc-products | [
"task_categories:text-classification",
"annotations_creators:weak supervision",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-10-23T05:50:24Z | 2022-05-16T13:23:21.000Z | 2022-05-16T13:23:21 | ---
annotations_creators:
- weak supervision
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- unknown
multilinguality:
- monolingual
pretty_name: products-2017
size_categories:
- 1K<n<10K
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
- data-integration
task_ids:
- entity-matching
- identity-resolution
- product-matching
paperswithcode_id: wdc-products
---
# Dataset Card for [products-2017]
## 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)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [LSPCv2 Homepage](http://webdatacommons.org/largescaleproductcorpus/v2/index.html)
- **Point of Contact:** [Ralph Peeters](mailto:ralph.peeters@uni-mannheim.de)
### Dataset Summary
Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label "match" or "no match")
In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test set. We provide training and validation sets in four different sizes for four product categories. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web via weak supervision.
The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites.
### Supported Tasks and Leaderboards
Entity Matching, Product Matching
### Languages
English
## Dataset Structure
### Data Instances
The data is structured as pairs of product offers with the corresponding match/non-match label. This is an example instance from the computers category:
```
{"pair_id":"581109#16637861","label":0,"id_left":581109,"category_left":"Computers_and_Accessories","cluster_id_left":1324529,"brand_left":"\"Gigabyte\"@en","title_left":" \"Gigabyte Radeon RX 480 G1 Gaming 4096MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_left":"\"GV-RX480G1 GAMING-4GD, Core Clock: 1202MHz, Boost Clock: 1290MHz, Memory: 4096MB 7000MHz GDDR5, Stream Processors: 2304, Crossfire Ready, VR Ready, FreeSync Ready, 3 Years Warranty\"@en ","price_left":null,"specTableContent_left":null,"id_right":16637861,"category_right":"Computers_and_Accessories","cluster_id_right":107415,"brand_right":"\"Gigabyte\"@en","title_right":" \"Gigabyte Radeon RX 550 Gaming OC 2048MB GDDR5 PCI-Express Graphics Card\"@en \"Gigabyte Gr| OcUK\"@en","description_right":"\"GV-RX550GAMING OC-2GD, Boost: 1219MHz, Memory: 2048MB 7000MHz GDDR5, Stream Processors: 512, DirectX 12 Support, 3 Years Warranty\"@en ","price_right":null,"specTableContent_right":null}
```
### Data Fields
- pair_id: unique identifier of a pair (string)
- label: binary label, match or non-match (int)
The following attributes are contained twice, once for the first and once for the second product offer
- id: unique id of the product offer (int)
- category: product category (string)
- cluster_id: id of the product cluster from the original corpus this offer belongs to (int)
- brand: brand of the product (string)
- title: product title (string)
- description: longer product description (string)
- price: price of the product offer (string)
- specTableContent: additional data found in specification tables on the webpage that contains the product offer (string)
### Data Splits
- Computers
- Test set - 1100 pairs
- Small Train set - 2267 pairs
- Small Validation set - 567 pairs
- Medium Train set - 6475 pairs
- Medium Validation set - 1619 pairs
- Large Train set - 26687 pairs
- Large Validation set - 6672 pairs
- XLarge Train set - 54768 pairs
- Xlarge Validation set - 13693 pairs
- Cameras
- Test set - 1100 pairs
- Small Train set - 1508 pairs
- Small Validation set - 378 pairs
- Medium Train set - 4204 pairs
- Medium Validation set - 1051 pairs
- Large Train set - 16028 pairs
- Large Validation set - 4008 pairs
- XLarge Train set - 33821 pairs
- Xlarge Validation set - 8456 pairs
- Watches
- Test set - 1100 pairs
- Small Train set - 1804 pairs
- Small Validation set - 451 pairs
- Medium Train set - 5130 pairs
- Medium Validation set - 1283 pairs
- Large Train set - 21621 pairs
- Large Validation set - 5406 pairs
- XLarge Train set - 49255 pairs
- Xlarge Validation set - 12314 pairs
- Shoes
- Test set - 1100 pairs
- Small Train set - 1650 pairs
- Small Validation set - 413 pairs
- Medium Train set - 4644 pairs
- Medium Validation set - 1161 pairs
- Large Train set - 18391 pairs
- Large Validation set - 4598 pairs
- XLarge Train set - 33943 pairs
- Xlarge Validation set - 8486 pairs
## Dataset Creation
### Annotations
#### Annotation process
- Training and Validation sets: distant supervision via shared schema.org product IDs
- Test sets: Single expert annotator
#### Who are the annotators?
[Ralph Peeters](https://www.uni-mannheim.de/dws/people/researchers/phd-students/ralph-peeters/)
## Additional Information
### Citation Information
```
@inproceedings{primpeli2019wdc,
title={The WDC training dataset and gold standard for large-scale product matching},
author={Primpeli, Anna and Peeters, Ralph and Bizer, Christian},
booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
pages={381--386},
year={2019}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/CLSClusteringP2P | C-MTEB | 2023-07-27T17:29:48Z | 258 | 0 | null | [
"region:us"
] | 2023-07-27T17:29:48Z | 2023-07-27T17:29:10.000Z | 2023-07-27T17:29:10 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 56780231
num_examples: 10
download_size: 37254736
dataset_size: 56780231
---
# Dataset Card for "CLSClusteringP2P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/80da83b2 | result-kand2-sdxl-wuerst-karlo | 2023-11-13T20:26:14Z | 258 | 0 | null | [
"region:us"
] | 2023-11-13T20:26:14Z | 2023-11-13T20:26:14.000Z | 2023-11-13T20:26:14 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 219
num_examples: 10
download_size: 1431
dataset_size: 219
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "80da83b2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/bd8b4914 | result-kand2-sdxl-wuerst-karlo | 2023-11-13T20:28:27Z | 258 | 0 | null | [
"region:us"
] | 2023-11-13T20:28:27Z | 2023-11-13T20:28:26.000Z | 2023-11-13T20:28:26 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 249
num_examples: 10
download_size: 1433
dataset_size: 249
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bd8b4914"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/60b7339d | result-kand2-sdxl-wuerst-karlo | 2023-11-13T20:35:22Z | 258 | 0 | null | [
"region:us"
] | 2023-11-13T20:35:22Z | 2023-11-13T20:35:21.000Z | 2023-11-13T20:35:21 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 231
num_examples: 10
download_size: 1418
dataset_size: 231
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "60b7339d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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result-kand2-sdxl-wuerst-karlo/7fb2a617 | result-kand2-sdxl-wuerst-karlo | 2023-11-13T20:35:25Z | 258 | 0 | null | [
"region:us"
] | 2023-11-13T20:35:25Z | 2023-11-13T20:35:24.000Z | 2023-11-13T20:35:24 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 231
num_examples: 10
download_size: 1418
dataset_size: 231
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7fb2a617"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Snit/french-conversation | Snit | 2023-04-29T06:41:29Z | 257 | 5 | null | [
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:fr",
"license:cc-by-4.0",
"french",
"region:us"
] | 2023-04-29T06:41:29Z | 2023-04-29T05:51:26.000Z | 2023-04-29T05:51:26 | ---
license: cc-by-4.0
language:
- fr
tags:
- french
task_categories:
- conversational
size_categories:
- 1K<n<10K
---
+15 hours of speech data from TTS and text file recording.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vilm/OpenOrca-Viet | vilm | 2023-11-06T17:48:37Z | 257 | 6 | null | [
"license:apache-2.0",
"arxiv:2306.02707",
"region:us"
] | 2023-11-06T17:48:37Z | 2023-11-06T06:29:15.000Z | 2023-11-06T06:29:15 | ---
license: apache-2.0
---
## 🇻🇳 Vietnamese OpenOrca is here 🐋
<img src="https://i.ibb.co/kgmJG96/orca-viet.png" alt="drawing" width="512"/>
Dive into the Vietnamese linguistic landscape with OpenOrca, a cutting-edge dataset crafted through a pioneering partnership between **Virtual Interactive** and **Alignment Lab AI**. Drawing inspiration and methodology from the renowned [Orca paper](https://arxiv.org/abs/2306.02707), we've expanded our horizons to distill knowledge from a more eclectic mix of leading LLMs including GPT-4, PaLM-2, and Claude. Our vision with this dataset is to fuel research and development that will catapult the performance of Vietnamese Language Models into uncharted territories. Join us on this exhilarating journey to redefine AI's linguistic prowess.
The main original source of tasks/questions is a translated version of *FLAN*, **vi-FLAN**. We further augmented **vi-FLAN** on better state-of-the-art LLMs.
## Citation
```
@misc{OpenOrcaViet,
title = {OpenOrca-Viet: GPT Augmented FLAN Reasoning for Vietnamese},
author = {Virtual Interactive and Alignment Lab AI},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/vilm/OpenOrca-Viet}},
}
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millawell/wikipedia_field_of_science | millawell | 2022-10-11T08:26:28Z | 256 | 5 | null | [
"license:cc-by-sa-3.0",
"region:us"
] | 2022-10-11T08:26:28Z | 2022-10-11T08:16:50.000Z | 2022-10-11T08:16:50 | ---
license: cc-by-sa-3.0
---
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d0rj/samsum-ru | d0rj | 2023-05-13T06:44:23Z | 256 | 3 | null | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:samsum",
"language:ru",
"license:cc-by-nc-nd-4.0",
"conversations-summarization",
"arxiv:1911.12237",
"region:us"
] | 2023-05-13T06:44:23Z | 2023-05-08T08:57:36.000Z | 2023-05-08T08:57:36 | ---
annotations_creators:
- expert-generated
language_creators:
- translated
language:
- ru
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- samsum
task_categories:
- summarization
task_ids: []
pretty_name: SAMSum Corpus (ru)
tags:
- conversations-summarization
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 8598724
num_examples: 14731
- name: validation
num_bytes: 471632
num_examples: 818
- name: test
num_bytes: 483686
num_examples: 819
dataset_size: 9554042
train-eval-index:
- config: samsum
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
dialogue: text
summary: target
---
# Dataset Card for SAMSum Corpus (ru)
## Dataset Description
Translated [samsum](https://huggingface.co/datasets/samsum) dataset to russian language.
### Notes
> Row with ID **13828807** was deleted.
### Links
- **Homepage:** hhttps://arxiv.org/abs/1911.12237v2
- **Repository:** https://arxiv.org/abs/1911.12237v2
- **Paper:** https://arxiv.org/abs/1911.12237v2
### Languages
Russian (translated from English [samsum](https://huggingface.co/datasets/samsum) using Google Translator)
## Dataset Structure
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique file id of an example.
### Data Splits
- train: 14731
- val: 818
- test: 819
## Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
## Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ai4bharat/IndicQA | ai4bharat | 2023-06-20T03:03:32Z | 255 | 1 | null | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc-by-4.0",
"region:us"
] | 2023-06-20T03:03:32Z | 2022-09-15T04:52:16.000Z | 2022-09-15T04:52:16 | ---
annotations_creators:
- expert-generated
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: IndicQA
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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ddrg/super_eurlex | ddrg | 2023-11-14T06:18:46Z | 255 | 1 | null | [
"task_categories:text-classification",
"task_categories:fill-mask",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:mit",
"legal documents",
"corpus",
"eurlex",
"html",
"region:us"
] | 2023-11-14T06:18:46Z | 2023-02-23T08:14:37.000Z | 2023-02-23T08:14:37 | ---
annotations_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
language_creators:
- found
license:
- mit
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- legal documents
- corpus
- eurlex
- html
task_categories:
- text-classification
- fill-mask
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: 'SuperEURLEX: A Corpus of Plain Text and HTML from EURLEX, Annotated for multiple Legal Domain Text Classification Tasks.'
---
# Dataset Card for SuperEURLEX
This dataset contains over 4.6M Legal Documents from EURLEX with Annotations.
Over 3.7M of this 4.6M documents are also available in HTML format.
This dataset can be used for pretraining language models as well as for testing them on legal text classification tasks.
Use this dataset as follows:
```python
from datasets import load_dataset
config = "0.DE" # {sector}.{lang}[.html]
dataset = load_dataset("ddrg/super_eurlex", config, split='train')
```
## Dataset Details
### Dataset Description
This Dataset was scrapped from [EURLEX](https://eur-lex.europa.eu/homepage.html).
It contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format.
Those Documents are separated by their language (This Dataset includes a total of 24 official European Languages)
and by their Sector.
#### The Table below shows the number of documents per language:
| | Raw | HTML |
|---:|--------:|--------:|
| BG | 29,778 | 27,718 |
| CS | 94,439 | 91,754 |
| DA | 398,559 | 300,488 |
| DE | 384,179 | 265,724 |
| EL | 167,502 | 117,009 |
| EN | 456,212 | 354,186 |
| ES | 253,821 | 201,400 |
| ET | 142,183 | 139,690 |
| FI | 238,143 | 214,206 |
| FR | 427,011 | 305,592 |
| GA | 19,673 | 19,437 |
| HR | 37,200 | 35,944 |
| HU | 69,275 | 66,334 |
| IT | 358,637 | 259,936 |
| LT | 62,975 | 61,139 |
| LV | 105,433 | 102,105 |
| MT | 46,695 | 43,969 |
| NL | 345,276 | 237,366 |
| PL | 146,502 | 143,490 |
| PT | 369,571 | 314,148 |
| RO | 47,398 | 45,317 |
| SK | 100,718 | 98,192 |
| SL | 170,583 | 166,646 |
| SV | 172,926 | 148,656 |
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
- **Repository:** https://huggingface.co/datasets/ddrg/super_eurlex/tree/main
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
### As Corpus for:
- **Pretraining of Language Models with self supervised tasks** like Masked Language Modeling and Next Sentence Prediction
- Legal Text Analysis
### As Dataset for evaluation on the following task:
- *eurovoc*-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))
- Example for this task is given[below
- *subject-matter* Prediction i.e. which other tags apply (Multi-Label Classification)
- *form* Classification i.e. What Kind of Document is it? (Multi-Class)
- And more
### Example for Use Of EUROVOC-Concepts
```python
from datasets import load_dataset
import transformers as tr
from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import evaluate
import uuid
# ==================== #
# Prepare Data #
# ==================== #
CONFIG = "3.EN" # {sector}.{lang}[.html]
MODEL_NAME = "distilroberta-base"
dataset = load_dataset("ddrg/super_eurlex", CONFIG, split='train')
tokenizer = tr.AutoTokenizer.from_pretrained(MODEL_NAME)
# Remove Unlabeled Columns
def remove_nulls(batch):
return [(sample != None) for sample in batch["eurovoc"]]
dataset = dataset.filter(remove_nulls, batched=True, keep_in_memory=True)
# Tokenize Text
def tokenize(batch):
return tokenizer(batch["text_cleaned"], truncation=True, padding="max_length")
# Keep in Memory is optional (The Dataset is large though and can easily use up alot of memory)
dataset = dataset.map(tokenize, batched=True, keep_in_memory=True)
# Create Label Column by encoding Eurovoc Concepts
encoder = MultiLabelBinarizer()
# List of all Possible Labels
eurovoc_concepts = dataset["eurovoc"]
encoder.fit(eurovoc_concepts)
def encode_labels(batch):
batch["label"] = encoder.transform(batch["eurovoc"])
return batch
dataset = dataset.map(encode_labels, batched=True, keep_in_memory=True)
# Split into train and Test set
dataset = dataset.train_test_split(0.2)
# ==================== #
# Load & Train Model #
# ==================== #
model = tr.AutoModelForSequenceClassification.from_pretrained(
MODEL_NAME,
num_labels=len(encoder.classes_),
problem_type="multi_label_classification",
)
metric = evaluate.load("JP-SystemsX/nDCG", experiment_id=uuid.uuid4())
def compute_metric(eval_pred):
predictions, labels = eval_pred
return metric.compute(predictions=predictions, references=labels, k=5)
# Set Hyperparameter
# Note: We stay mostly with default values to keep example short
# Though more hyperparameter should be set and tuned in praxis
train_args = tr.TrainingArguments(
output_dir="./cache",
per_device_train_batch_size=16,
num_train_epochs=20
)
trainer = tr.Trainer(
model=model,
args=train_args,
train_dataset=dataset["train"],
compute_metrics=compute_metric,
)
trainer.train() # This will take a while
print(trainer.evaluate(dataset["test"]))
# >>> {'eval_loss': 0.0018887673504650593, 'eval_nDCG@5': 0.8072531683578489, 'eval_runtime': 663.8582, 'eval_samples_per_second': 32.373, 'eval_steps_per_second': 4.048, 'epoch': 20.0}
```
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
This dataset is divided into multiple split by _Sector x Language x Format_
Sector refers to the kind of Document it belongs to:
- **0:** Consolidated acts
- **1:** Treaties
- **2:** International agreements
- **3:** Legislation
- **4:** Complementary legislation
- **5:** Preparatory acts and working documents
- **6:** Case-law
- **7:** National transposition measures
- **8:** References to national case-law concerning EU law
- **9:** Parliamentary questions
- **C:** Other documents published in the Official Journal C series
- **E:** EFTA documents
Language refers to each of the 24 official European Languages that were included at the date of the dataset creation:
- BG ~ Bulgarian
- CS ~ Czech
- DA ~ Danish
- DE ~ German
- EL ~ Greek
- EN ~ English
- ES ~ Spanish
- ET ~ Estonian
- FI ~ Finnish
- FR ~ French
- GA ~ Irish
- HR ~ Croatian
- HU ~ Hungarian
- IT ~ Italian
- LT ~ Lithuanian
- LV ~ Latvian
- MT ~ Maltese
- NL ~ Dutch
- PL ~ Polish
- PT ~ Portuguese
- RO ~ Romanian
- SK ~ Slovak
- SL ~ Slovenian
- SV ~ Swedish
Format refers to plain Text (default) or HTML format (.html)
> Note: Plain Text contains generally more documents because not all documents were available in HTML format but those that were are included in both formats
Those Splits are named the following way:
`{sector}.{lang}[.html]`
For Example:
- `3.EN` would be English legislative documents in plain text format
- `3.EN.html` would be the same in HTML Format
Each _Sector_ has its own set of meta data:
<details><summary>Sector 0 (Consolidated acts)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
</p>
</details>
<details><summary>Sector 1 (Treaties)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _current_consolidated_version_ ~ date when this version of the document was consolidated `Format DD/MM/YYYY`
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
<details><summary>Sector 2 (International agreements)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 3 (Legislation)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 4 (Complementary legislation)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 5 (Preparatory acts and working documents)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
</p>
</details>
<details><summary>Sector 6 (Case-law)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
- _case-law_directory_code_before_lisbon_ ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
</p>
</details>
<details><summary>Sector 7 (National transposition measures)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _transposed_legal_acts_ ~ national laws that exist in EU member states as a direct result of the need to comply with EU directives
</p>
</details>
<details><summary>Sector 8 (References to national case-law concerning EU law)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _case-law_directory_code_before_lisbon_ ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
</p>
</details>
<details><summary>Sector 9 (Parliamentary questions)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
<details><summary>Sector C (Other documents published in the Official Journal C series)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
<details><summary>Sector E (EFTA documents)</summary><p>
- _celex_id_ ~ Unique Identifier for each document
- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
</p>
</details>
## Dataset Creation
### Curation Rationale
This dataset was created for the creation and/or evaluation of pretrained Legal Language Models.
### Source Data
#### Data Collection and Processing
We used the [EURLEX-Web-Scrapper Repo](https://github.com/JP-SystemsX/Eurlex-Web-Scrapper) for the data collection process.
#### Who are the source data producers?
The Source data stems from the [EURLEX-Website](https://eur-lex.europa.eu/) and was therefore produced by various entities within the European Union
#### Personal and Sensitive Information
No Personal or Sensitive Information is included to the best of our knowledge.
## Bias, Risks, and Limitations
- We removed HTML documents from which we couldn't extract plain text under the assumption that those are **corrupted files**.
However, we can't guarantee that we removed all.
- The Extraction of plain text from legal HTML documents can lead to **formatting issues**
e.g. the extraction of text from tables might mix up the order such that it becomes nearly incomprehensible.
- This dataset might contain many **missing values** in the meta-data columns as not every document was annotated in the same way
[More Information Needed]
### Recommendations
- Consider Removing rows with missing values for the task before training a model on it
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
IlyaGusev/ru_turbo_alpaca | IlyaGusev | 2023-05-25T19:45:14Z | 255 | 51 | null | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"instruction-finetuning",
"instruction generation",
"alpaca",
"region:us"
] | 2023-05-25T19:45:14Z | 2023-03-21T21:17:42.000Z | 2023-03-21T21:17:42 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: alternative_output
dtype: string
- name: label
dtype: string
- name: all_labels
sequence: string
- name: agreement
dtype: float32
- name: overlap
dtype: uint32
splits:
- name: train
num_bytes: 54774775
num_examples: 29822
download_size: 14565995
dataset_size: 54774775
license: cc-by-4.0
task_categories:
- text-generation
- text2text-generation
language:
- ru
tags:
- instruction-finetuning
- instruction generation
- alpaca
size_categories:
- 10K<n<100K
---
# RuTurboAlpaca
Dataset of ChatGPT-generated instructions in Russian.
<img src="https://cdn.midjourney.com/770a35fa-00c0-4214-bb88-727dbc7cfaf3/0_0.png" >
* Code: [rulm/self_instruct](https://github.com/IlyaGusev/rulm/tree/master/self_instruct)
* Code is based on [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [self-instruct](https://github.com/yizhongw/self-instruct/).
* 29822 examples
Preliminary evaluation by an expert based on 400 samples:
* 83% of samples contain correct instructions
* 63% of samples have correct instructions and outputs
Crowdsouring-based evaluation on 3500 samples:
* 90% of samples contain correct instructions
* 68% of samples have correct instructions and outputs
Prompt template:
```
Составь набор из {{num_tasks}} разных заданий для дообучения языковой модели:
1. Делай задания максимально непохожими друг на друга: по типу, по запрашиваемым действиям, по формулировке, по наличию входа.
2. Задания должны быть выполнимы языковой моделью, которая не умеет работать с картинками, видео, и аудио, и не имеет доступа ко внешнему миру.
3. Используй хороший грамотный русский язык.
4. Делай задания в одно или два предложения.
5. Генерируй подходящие реалистичные входные данные, не используй общие шаблоны типа \"Имя человека\" или [имя] вместо реального имени.
6. Задание может быть без входных данных, в таком случае используй токен <noinput> вместо них.
7. На выходе сгенерируй подходящий длинный ответ.
8. Следуй тому же шаблону, который приведен в примерах, разделяй задания с помощью ###. Это важно!
Примеры заданий:
{% for task in example_tasks %}
{{task.index}}. Задание: {{task.instruction}}
{{task.index}}. Вход: {{task.input}}
{{task.index}}. Выход: {{task.output}}
{{ "###" if not loop.last else "" }}
{% endfor %}
```
## Legal disclaimer
Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit for us developing models that compete with OpenAI. Not for you. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
heegyu/korquad-chat-v1 | heegyu | 2023-05-06T09:12:14Z | 255 | 7 | null | [
"license:mit",
"region:us"
] | 2023-05-06T09:12:14Z | 2023-05-06T09:05:56.000Z | 2023-05-06T09:05:56 | ---
license: mit
---
- 총 9619개의 한국어 지식기반 대화 데이터셋입니다.
- KorQuAD 1.0 데이터에 있는 문서를 ChatGPT에게 준 뒤 생성했습니다.
서로를 호칭하는 부분은 아래처럼 처리되어있습니다.
```
안녕하세요 <|bot|>. 요즘 어떻게 지내시나요?\n<bot> 안녕하세요 <|user|>.
```
데이터 샘플
```
{
"source": "korquad-chat",
"text": "
<sys>유전자의 이름은 인도의 수도 뉴델리의 이름을 따 붙여졌는데, 이는 2009년 용 (Yong) 등이 처음 기록한 이 케이스의 환자가 인도에서 처음 박테리아 감염을 확인하였기 때문이다. 박테리아 감염 치료가 뉴델리 병원에서 실패로 끝난 후 그는 모국 스웨덴으로 송환되었으며, 그곳에서 카바페넴에 내성을 보이는 폐렴막대균의 유전자가 분리되었다. 용 등은 이 내성 메커니즘이 \"분명히 인도에서 생겨났으나, 이것이 얼마나 퍼져있는지를 보여주는 데이터가 인도에 존재한다\"라고 주장하였다. 보고된 바에 따르면 이 유전자는 파키스탄, 인도, 그리고 여러 아시아 국가에서 발견되었으며 이곳 병원에 입원했던 사람들에 의해 유럽으로 옮겨졌다. 그 중 대다수는 저가의 성형 수술을 위해 의료 관광을 떠났다가 수술 중 감염이 된 사람들이었다.\n
<usr> 메탈로-베타락타마제가 어디서 발견되었나요?\n
<bot> 이 유전자는 파키스탄, 인도, 그리고 여러 아시아 국가에서 발견된 것으로 보고되었습니다.\n
<usr> 이 박테리아 감염은 어떻게 전파되는 건가요?\n
<bot> 이 병원균은 주로 의료 관광으로 인해 수술 중 감염된 사람들에 의해 유럽으로 전파되었습니다.\n
<usr> 이 유전자는 카바페넴에 반응하지 않는 내성 케이스에서 처음 발견됐는데, 이 내성 메커니즘이 다른 국가에도 퍼진 건가요?\n
<bot> 이 내성 메커니즘이 여러 나라에서 발견된 것으로 보고되기 때문에, 퍼져있다고 볼 수 있습니다. 그러나, 이것이 얼마나 퍼져있는지를 보여주는 데이터가 인도에 존재한다는 주장이 있습니다.\n
<usr> 이 박테리아 감염은 어떤 질병을 일으키나요?\n
<bot> 이 메탈로-베타락타마제가 감염하면, 카바페넴 유형의 항생제를 사용할 수 없고, 치명적인 감염증으로 발전할 가능성이 있습니다.\n
<usr> 이 박테리아 감염을 예방하거나 치료할 방법이 있을까요?\n
<bot> 이 박테리아 감염을 예방하거나 치료할 수 있는 항생제는 여전히 발견되지 않았습니다. 따라서, 이 병균을 포함한 감염 예방의 중요성이 더욱 부각되고 있습니다."
}
```
ChatGPT 데이터 생성 프롬프트
```
A와 B 두 사람이 나누는 대화를 만들어주세요.
1. A는 주어진 글에서 말하는 내용에 관해 B에게 질문을 하거나 요청합니다. B는 글의 내용을 이해하고 완전히 학습한 상태입니다. B는 A의 질문과 요청에 자신이 학습한 내용을 바탕으로 대답을 해야 합니다.
2. B는 글에 존재하지 않거나 사실에 근거하지 않은 대답을 해서는 안됩니다.
3. 각 발화는 최대 3개의 문장으로 이루어져 있습니다.
4. 대화는 A와 B가 서로 주고받으며 순서대로 A의 발화는 A:, B의 발화는 B: 로 시작해야하고 띄어쓰기로 구분합니다.
5. A와 B가 글을 읽었다는 내용이나, 글에 대해서 평가하거나, 글을 언급하는 내용이 포함되어서는 안됩니다.
6. A와 B가 서로를 언급할 때는 A씨, B씨로 호칭해야합니다.
7. A와 B는 서로 8번 대화를 주고받아야 합니다. 대화의 전체길이는 최대한 200 단어가 넘지 않도록 대화를 끝내야합니다.
제목: #title
#text
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vlsp-2023-vllm/hellaswag | vlsp-2023-vllm | 2023-10-29T01:02:35Z | 255 | 0 | null | [
"region:us"
] | 2023-10-29T01:02:35Z | 2023-09-29T18:37:27.000Z | 2023-09-29T18:37:27 | ---
dataset_info:
features:
- name: endings
sequence: string
- name: ind
dtype: int64
- name: ctx_a
dtype: string
- name: id
dtype: string
- name: ctx_b
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
- name: activity_label
dtype: string
- name: ctx
dtype: string
- name: source_id
dtype: string
splits:
- name: validation
num_bytes: 13795593
num_examples: 9162
download_size: 6831159
dataset_size: 13795593
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
Reference: https://huggingface.co/datasets/Rowan/hellaswag
# HellaSwag (Vietnamese translation version)
## Install
To install `lm-eval` from the github repository main branch, run:
```bash
git clone https://github.com/hieunguyen1053/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
## Basic Usage
> **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
### Hugging Face `transformers`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `hellaswag_vi` you can use the following command:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4 \
--tasks hellaswag_vi \
--num_fewshot 10 \
--batch_size auto \
--device cuda:0
```
Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \
--tasks hellaswag_vi \
--device cuda:0
```
To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. *To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental`*
> **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Coldog2333/blurb-pubmedqa | Coldog2333 | 2023-10-30T04:06:38Z | 255 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-30T04:06:38Z | 2023-10-30T03:38:06.000Z | 2023-10-30T03:38:06 | ---
license: apache-2.0
---
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polinaeterna/amazon_us_reviews | polinaeterna | 2023-06-09T17:56:17Z | 254 | 1 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:topic-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2023-06-09T17:56:17Z | 2023-06-09T17:56:16.000Z | 2023-06-09T17:56:16 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
- text-classification
task_ids:
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- language-modeling
- masked-language-modeling
- sentiment-classification
- sentiment-scoring
- topic-classification
pretty_name: Amazon US Reviews
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duplicated_from: amazon_us_reviews
---
# Dataset Card for "amazon_us_reviews"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://s3.amazonaws.com/amazon-reviews-pds/readme.html](https://s3.amazonaws.com/amazon-reviews-pds/readme.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 32377.29 MB
- **Size of the generated dataset:** 82820.19 MB
- **Total amount of disk used:** 115197.49 MB
### Dataset Summary
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns :
marketplace - 2 letter country code of the marketplace where the review was written.
customer_id - Random identifier that can be used to aggregate reviews written by a single author.
review_id - The unique ID of the review.
product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews
for the same product in different countries can be grouped by the same product_id.
product_parent - Random identifier that can be used to aggregate reviews for the same product.
product_title - Title of the product.
product_category - Broad product category that can be used to group reviews
(also used to group the dataset into coherent parts).
star_rating - The 1-5 star rating of the review.
helpful_votes - Number of helpful votes.
total_votes - Number of total votes the review received.
vine - Review was written as part of the Vine program.
verified_purchase - The review is on a verified purchase.
review_headline - The title of the review.
review_body - The review text.
review_date - The date the review was written.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### Apparel_v1_00
- **Size of downloaded dataset files:** 648.64 MB
- **Size of the generated dataset:** 2254.36 MB
- **Total amount of disk used:** 2903.00 MB
An example of 'train' looks as follows.
```
{
"customer_id": "45223824",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Apparel",
"product_id": "B016PUU3VO",
"product_parent": "893588059",
"product_title": "Fruit of the Loom Boys' A-Shirt (Pack of 4)",
"review_body": "I ordered the same size as I ordered last time, and these shirts were much larger than the previous order. They were also about 6 inches longer. It was like they sent men's shirts instead of boys' shirts. I'll be returning these...",
"review_date": "2015-01-01",
"review_headline": "Sizes not correct, too big overall and WAY too long",
"review_id": "R1N3Z13931J3O9",
"star_rating": 2,
"total_votes": 0,
"verified_purchase": 1,
"vine": 0
}
```
#### Automotive_v1_00
- **Size of downloaded dataset files:** 582.15 MB
- **Size of the generated dataset:** 1518.88 MB
- **Total amount of disk used:** 2101.03 MB
An example of 'train' looks as follows.
```
{
"customer_id": "16825098",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Automotive",
"product_id": "B000E4PCGE",
"product_parent": "694793259",
"product_title": "00-03 NISSAN SENTRA MIRROR RH (PASSENGER SIDE), Power, Non-Heated (2000 00 2001 01 2002 02 2003 03) NS35ER 963015M000",
"review_body": "Product was as described, new and a great look. Only bad thing is that one of the screws was stripped so I couldn't tighten all three.",
"review_date": "2015-08-31",
"review_headline": "new and a great look. Only bad thing is that one of ...",
"review_id": "R2RUIDUMDKG7P",
"star_rating": 3,
"total_votes": 0,
"verified_purchase": 1,
"vine": 0
}
```
#### Baby_v1_00
- **Size of downloaded dataset files:** 357.40 MB
- **Size of the generated dataset:** 956.30 MB
- **Total amount of disk used:** 1313.70 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"customer_id": "23299101",
"helpful_votes": 2,
"marketplace": "US",
"product_category": "Baby",
"product_id": "B00SN6F9NG",
"product_parent": "3470998",
"product_title": "Rhoost Nail Clipper for Baby - Ergonomically Designed and Easy to Use Baby Nail Clipper, Natural Wooden Bamboo - Baby Health and Personal Care Kits",
"review_body": "\"This is an absolute MUST item to have! I was scared to death to clip my baby's nails. I tried other baby nail clippers and th...",
"review_date": "2015-08-31",
"review_headline": "If fits so comfortably in my hand and I feel like I have ...",
"review_id": "R2DRL5NRODVQ3Z",
"star_rating": 5,
"total_votes": 2,
"verified_purchase": 1,
"vine": 0
}
```
#### Beauty_v1_00
- **Size of downloaded dataset files:** 914.08 MB
- **Size of the generated dataset:** 2397.39 MB
- **Total amount of disk used:** 3311.47 MB
An example of 'train' looks as follows.
```
{
"customer_id": "24655453",
"helpful_votes": 1,
"marketplace": "US",
"product_category": "Beauty",
"product_id": "B00SAQ9DZY",
"product_parent": "292127037",
"product_title": "12 New, High Quality, Amber 2 ml (5/8 Dram) Glass Bottles, with Orifice Reducer and Black Cap.",
"review_body": "These are great for small mixtures for EO's, especially for traveling. I only gave this 4 stars because of the orifice reducer. The hole is so small it is hard to get the oil out. Just needs to be slightly bigger.",
"review_date": "2015-08-31",
"review_headline": "Good Product",
"review_id": "R2A30ALEGLMCGN",
"star_rating": 4,
"total_votes": 1,
"verified_purchase": 1,
"vine": 0
}
```
#### Books_v1_00
- **Size of downloaded dataset files:** 2740.34 MB
- **Size of the generated dataset:** 7193.86 MB
- **Total amount of disk used:** 9934.20 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"customer_id": "49735028",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Books",
"product_id": "0664254969",
"product_parent": "248307276",
"product_title": "Presbyterian Creeds: A Guide to the Book of Confessions",
"review_body": "\"The Presbyterian Book of Confessions contains multiple Creeds for use by the denomination. This guidebook helps he lay person t...",
"review_date": "2015-08-31",
"review_headline": "The Presbyterian Book of Confessions contains multiple Creeds for use ...",
"review_id": "R2G519UREHRO8M",
"star_rating": 3,
"total_votes": 1,
"verified_purchase": 1,
"vine": 0
}
```
### Data Fields
The data fields are the same among all splits.
#### Apparel_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Automotive_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Baby_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Beauty_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Books_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
### Data Splits
| name | train |
|----------------|-------:|
|Apparel_v1_00 | 5906333|
|Automotive_v1_00 | 3514942|
|Baby_v1_00 | 1752932|
|Beauty_v1_00 | 5115666|
|Books_v1_00 | 10319090|
|Books_v1_01 | 6106719|
|Books_v1_02 | 3105520|
|Camera_v1_00 | 1801974|
|Digital_Ebook_Purchase_v1_00 | 12520722|
|Digital_Ebook_Purchase_v1_01 | 5101693|
|Digital_Music_Purchase_v1_00 | 1688884|
|Digital_Software_v1_00 | 102084|
|Digital_Video_Download_v1_00 | 4057147|
|Digital_Video_Games_v1_00 | 145431|
|Electronics_v1_00 | 3093869|
|Furniture_v1_00 | 792113|
|Gift_Card_v1_00 | 149086|
|Grocery_v1_00 | 2402458|
|Health_Personal_Care_v1_00 | 5331449|
|Home_Entertainment_v1_00 | 705889|
|Home_Improvement_v1_00 | 2634781|
|Home_v1_00 | 6221559|
|Jewelry_v1_00 | 1767753|
|Kitchen_v1_00 | 4880466|
|Lawn_and_Garden_v1_00 | 2557288|
|Luggage_v1_00 | 348657|
|Major_Appliances_v1_00 | 96901|
|Mobile_Apps_v1_00 | 5033376|
|Mobile_Electronics_v1_00 | 104975|
|Music_v1_00 | 4751577|
|Musical_Instruments_v1_00 | 904765|
|Office_Products_v1_00 | 2642434|
|Outdoors_v1_00 | 2302401|
|PC_v1_00 | 6908554|
|Personal_Care_Appliances_v1_00 | 85981|
|Pet_Products_v1_00 | 2643619|
|Shoes_v1_00 | 4366916|
|Software_v1_00 | 341931|
|Sports_v1_00 | 4850360|
|Tools_v1_00 | 1741100|
|Toys_v1_00 | 4864249|
|Video_DVD_v1_00 | 5069140|
|Video_Games_v1_00 | 1785997|
|Video_v1_00 | 380604|
|Watches_v1_00 | 960872|
|Wireless_v1_00 | 9002021|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
https://s3.amazonaws.com/amazon-reviews-pds/LICENSE.txt
By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the
Reviews Library is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088)
and you agree to be bound by them, with the following additional conditions:
In addition to the license rights granted under the Conditions of Use,
Amazon or its content providers grant you a limited, non-exclusive, non-transferable,
non-sublicensable, revocable license to access and use the Reviews Library
for purposes of academic research.
You may not resell, republish, or make any commercial use of the Reviews Library
or its contents, including use of the Reviews Library for commercial research,
such as research related to a funding or consultancy contract, internship, or
other relationship in which the results are provided for a fee or delivered
to a for-profit organization. You may not (a) link or associate content
in the Reviews Library with any personal information (including Amazon customer accounts),
or (b) attempt to determine the identity of the author of any content in the
Reviews Library.
If you violate any of the foregoing conditions, your license to access and use the
Reviews Library will automatically terminate without prejudice to any of the
other rights or remedies Amazon may have.
### Citation Information
No citation information.
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Trelis/tiny-shakespeare | Trelis | 2023-09-06T16:27:30Z | 254 | 0 | null | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"fine-tuning",
"shakespeare",
"region:us"
] | 2023-09-06T16:27:30Z | 2023-09-06T16:16:36.000Z | 2023-09-06T16:16:36 | ---
task_categories:
- text-generation
language:
- en
tags:
- fine-tuning
- shakespeare
size_categories:
- n<1K
---
# Data source
Downloaded via Andrej Karpathy's nanogpt repo from this [link](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt)
# Data Format
- The entire dataset is split into train (90%) and test (10%).
- All rows are at most 1024 tokens, using the Llama 2 tokenizer.
- All rows are split cleanly so that sentences are whole and unbroken. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
accavdar/donut_employee_info_v1 | accavdar | 2023-11-09T23:52:57Z | 254 | 0 | null | [
"region:us"
] | 2023-11-09T23:52:57Z | 2023-11-09T23:52:48.000Z | 2023-11-09T23:52:48 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 6186374.0
num_examples: 80
- name: test
num_bytes: 1540228.0
num_examples: 20
download_size: 6685265
dataset_size: 7726602.0
---
# Dataset Card for "donut_employee_info_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nlp-thedeep/humset | nlp-thedeep | 2023-05-25T17:14:31Z | 253 | 1 | null | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:token-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:fr",
"language:es",
"license:apache-2.0",
"humanitarian",
"research",
"analytical-framework",
"multilabel",
"humset",
"humbert",
"region:us"
] | 2023-05-25T17:14:31Z | 2023-01-12T16:00:58.000Z | 2023-01-12T16:00:58 | ---
annotations_creators:
- expert-generated
language:
- en
- fr
- es
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: HumSet
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- humanitarian
- research
- analytical-framework
- multilabel
- humset
- humbert
task_categories:
- text-classification
- text-retrieval
- token-classification
task_ids:
- multi-label-classification
dataset_info:
features:
- name: entry_id
dtype: string
- name: lead_id
dtype: string
- name: project_id
dtype: string
- name: lang
dtype: string
- name: n_tokens
dtype: int64
- name: project_title
dtype: string
- name: created_at
dtype: string
- name: document
dtype: string
- name: excerpt
dtype: string
- name: sectors
sequence:
class_label:
names:
0: Agriculture
1: Cross
2: Education
3: Food Security
4: Health
5: Livelihoods
6: Logistics
7: Nutrition
8: Protection
9: Shelter
10: WASH
- name: pillars_1d
sequence:
class_label:
names:
0: Casualties
1: Context
2: Covid-19
3: Displacement
4: Humanitarian Access
5: Information And Communication
6: Shock/Event
- name: pillars_2d
sequence:
class_label:
names:
0: At Risk
1: Capacities & Response
2: Humanitarian Conditions
3: Impact
4: Priority Interventions
5: Priority Needs
- name: subpillars_1d
sequence:
class_label:
names:
0: Casualties->Dead
1: Casualties->Injured
2: Casualties->Missing
3: Context->Demography
4: Context->Economy
5: Context->Environment
6: Context->Legal & Policy
7: Context->Politics
8: Context->Security & Stability
9: Context->Socio Cultural
10: Covid-19->Cases
11: Covid-19->Contact Tracing
12: Covid-19->Deaths
13: Covid-19->Hospitalization & Care
14: Covid-19->Restriction Measures
15: Covid-19->Testing
16: Covid-19->Vaccination
17: Displacement->Intentions
18: Displacement->Local Integration
19: Displacement->Pull Factors
20: Displacement->Push Factors
21: Displacement->Type/Numbers/Movements
22: Humanitarian Access->Number Of People Facing Humanitarian Access Constraints/Humanitarian Access Gaps
23: Humanitarian Access->Physical Constraints
24: Humanitarian Access->Population To Relief
25: Humanitarian Access->Relief To Population
26: Information And Communication->Communication Means And Preferences
27: Information And Communication->Information Challenges And Barriers
28: Information And Communication->Knowledge And Info Gaps (Hum)
29: Information And Communication->Knowledge And Info Gaps (Pop)
30: Shock/Event->Hazard & Threats
31: Shock/Event->Type And Characteristics
32: Shock/Event->Underlying/Aggravating Factors
- name: subpillars_2d
sequence:
class_label:
names:
0: At Risk->Number Of People At Risk
1: At Risk->Risk And Vulnerabilities
2: Capacities & Response->International Response
3: Capacities & Response->Local Response
4: Capacities & Response->National Response
5: Capacities & Response->Number Of People Reached/Response Gaps
6: Humanitarian Conditions->Coping Mechanisms
7: Humanitarian Conditions->Living Standards
8: Humanitarian Conditions->Number Of People In Need
9: Humanitarian Conditions->Physical And Mental Well Being
10: Impact->Driver/Aggravating Factors
11: Impact->Impact On People
12: Impact->Impact On Systems, Services And Networks
13: Impact->Number Of People Affected
14: Priority Interventions->Expressed By Humanitarian Staff
15: Priority Interventions->Expressed By Population
16: Priority Needs->Expressed By Humanitarian Staff
17: Priority Needs->Expressed By Population
splits:
- name: train
num_examples: 117435
- name: validation
num_examples: 16039
- name: test
num_examples: 15147
---
# Dataset Card for HumSet
## 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)
## Dataset Description
- **Homepage:** [http://blog.thedeep.io/humset/](http://blog.thedeep.io/humset/)
- **Repository:** [https://github.com/the-deep/humset](https://github.com/the-deep/humset)
- **Paper:** [EMNLP Findings 2022](https://aclanthology.org/2022.findings-emnlp.321)
- **Leaderboard:**
- **Point of Contact:**[the DEEP NLP team](mailto:nlp@thedeep.io)
### Dataset Summary
HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three languages of English, French, and Spanish, originally taken from publicly-available resources. For each document, analysts have identified informative snippets (entries) in respect to common humanitarian frameworks, and assigned one or many classes to each entry. See the our paper for details.
### Supported Tasks and Leaderboards
This dataset is intended for multi-label classification
### Languages
This dataset is in English, French and Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- **entry_id**: unique identification number for a given entry. (string)
- **lead_id**: unique identification number for the document to which the corrisponding entry belongs. (string)
- **project_id** unique identification number for the project to which the corrisponding entry belongs. (string)
- **sectors**, **pillars_1d**, **pillars_2d**, **subpillars_1d**, **subpillars_2d**: labels assigned to the corresponding entry. Since this is a multi-label dataset (each entry may have several annotations belonging to the same category), they are reported as arrays of strings. See the paper for a detailed description of these categories. (list)
- **lang**: language. (str)
- **n_tokens**: number of tokens (tokenized using NLTK v3.7 library). (int64)
- **project_title**: the name of the project where the corresponding annotation was created. (str)
- **created_at**: date and time of creation of the annotation in stardard ISO 8601 format. (str)
- **document**: document URL source of the excerpt. (str)
- **excerpt**: excerpt text. (str)
### Data Splits
The dataset includes a set of train/validation/test splits, with 117435, 16039 and 15147 examples respectively.
## Dataset Creation
The collection originated from a multi-organizational platform called <em>the Data Entry and Exploration Platform (DEEP)</em> developed and maintained by Data Friendly Space (DFS). The platform facilitates classifying primarily qualitative information with respect to analysis frameworks and allows for collaborative classification and annotation of secondary data.
### Curation Rationale
[More Information Needed]
### Source Data
Documents are selected from different sources, ranging from official reports by humanitarian organizations to international and national media articles. See the paper for more informations.
#### Initial Data Collection and Normalization
#### Who are the source language producers?
[More Information Needed]
#### Annotation process
HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three
languages of English, French, and Spanish, originally taken from publicly-available resources. For
each document, analysts have identified informative snippets (entries, or excerpt in the imported dataset) with respect to common <em>humanitarian frameworks</em> and assigned one or many classes to each entry.
#### 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
NLP team at [Data Friendly Space](https://datafriendlyspace.org/)
### Licensing Information
The GitHub repository which houses this dataset has an Apache License 2.0.
### Citation Information
```
@inproceedings{fekih-etal-2022-humset,
title = "{H}um{S}et: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response",
author = "Fekih, Selim and
Tamagnone, Nicolo{'} and
Minixhofer, Benjamin and
Shrestha, Ranjan and
Contla, Ximena and
Oglethorpe, Ewan and
Rekabsaz, Navid",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.321",
pages = "4379--4389",
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigcode/commitpack | bigcode | 2023-08-20T07:13:13Z | 253 | 39 | null | [
"language:code",
"license:mit",
"arxiv:2308.07124",
"region:us"
] | 2023-08-20T07:13:13Z | 2023-01-17T11:53:28.000Z | 2023-01-17T11:53:28 | ---
license: mit
pretty_name: CommitPack
language:
- code
---
# Dataset Card for CommitPack
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigcode-project/octopack
- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
- **Point of Contact:** [Niklas Muennighoff](mailto:n.muennighoff@gmail.com)
### Dataset Summary
> CommitPack is is a 4TB dataset of commits scraped from GitHub repositories that are permissively licensed.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigcode-project/octopack).
- **Languages:** 350
- **OctoPack🐙🎒:**
<table>
<tr>
<th>Data</t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td>
<td>4TB of GitHub commits across 350 programming languages</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td>
<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t>
<td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td>
<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td>
<td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th>Evaluation</t>
<td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example looks as follows:
```json
{
'commit': '0c17311f7fd511f5dae8f8e4acc2dce1a2de3cf5',
'old_file': 'main.py',
'new_file': 'main.py',
'old_contents': "import numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-5, 5, 20)\ny_data = np.random.normal(0.0, 1.0, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n",
'new_contents': "import math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-math.pi, math.pi, 30)\ny_data = np.sin(x_data) + np.random.normal(0.0, 0.1, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n\n",
'subject': 'Change to sin() function with noise',
'message': 'Change to sin() function with noise\n',
'lang': 'Python',
'license': 'mit',
'repos': 'MorganR/basic-gaussian-process',
'returncode': 0,
'stderr': ''
}
```
### Data Fields
The data fields are the same among all splits:
- `commit`: unique commit id
- `old_file`: name of the file before the commit
- `new_file`: name of the file after the commit
- `old_contents`: contents of the file before the commit
- `new_contents`: contents of the file after the commit
- `subject`: subject of the commit (this is used for all experiments in the paper)
- `message`: message of the commit (commonly the same as the subject)
- `lang`: programming language
- `license`: license of the repository the code stems from, one of `['mit', 'artistic-2.0', 'isc', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'unknown', 'apache-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-2.1', 'bsd-2-clause']`
- `repos`: name of the the repository the code stems from (if multiple, they are comma separated)
- `returncode`: if applicable errorcode during scraping (0 = no error)
- 'stderr': if applicable the error that occured during scraping (empty = no error)
### Data Splits
| Name | Megabytes | % of total | Samples | % of total |
| --- | --- | --- | --- | --- |
| total | 3709175.78 | 100.0% | 57700105 | 100.0% |
| json | 583293.816 | 15.7257% | 3495038 | 6.0572% |
| xml | 279208.676 | 7.5275% | 1923159 | 3.333% |
| text | 270662.596 | 7.2971% | 1389525 | 2.4082% |
| javascript | 262824.844 | 7.0858% | 5401937 | 9.3621% |
| objective-c++ | 239009.3 | 6.4437% | 32227 | 0.0559% |
| python | 234311.564 | 6.3171% | 6189601 | 10.7272% |
| c | 200876.804 | 5.4157% | 2779478 | 4.8171% |
| c++ | 186585.256 | 5.0304% | 2402294 | 4.1634% |
| markdown | 171849.952 | 4.6331% | 7645354 | 13.2502% |
| java | 127103.448 | 3.4267% | 3744377 | 6.4894% |
| html | 105305.284 | 2.839% | 2366841 | 4.102% |
| yaml | 100466.64 | 2.7086% | 2592787 | 4.4936% |
| go | 86444.624 | 2.3306% | 1183612 | 2.0513% |
| csv | 82946.192 | 2.2362% | 79268 | 0.1374% |
| php | 74961.64 | 2.021% | 2555419 | 4.4288% |
| jupyter-notebook | 66854.08 | 1.8024% | 94000 | 0.1629% |
| gettext-catalog | 62296.88 | 1.6795% | 168327 | 0.2917% |
| sql | 56802.764 | 1.5314% | 132772 | 0.2301% |
| unity3d-asset | 39535.008 | 1.0659% | 17867 | 0.031% |
| typescript | 39254.804 | 1.0583% | 572136 | 0.9916% |
| web-ontology-language | 36435.464 | 0.9823% | 7458 | 0.0129% |
| ruby | 35830.74 | 0.966% | 2928702 | 5.0757% |
| c# | 33669.652 | 0.9077% | 923157 | 1.5999% |
| nix | 33547.92 | 0.9045% | 221281 | 0.3835% |
| shell | 25109.952 | 0.677% | 1017977 | 1.7643% |
| perl | 21148.928 | 0.5702% | 374266 | 0.6486% |
| tex | 17471.108 | 0.471% | 89283 | 0.1547% |
| css | 16306.632 | 0.4396% | 548818 | 0.9512% |
| restructuredtext | 15613.888 | 0.421% | 494037 | 0.8562% |
| rust | 15011.296 | 0.4047% | 296214 | 0.5134% |
| groff | 12020.188 | 0.3241% | 32923 | 0.0571% |
| ini | 8375.164 | 0.2258% | 297100 | 0.5149% |
| scala | 8325.96 | 0.2245% | 316064 | 0.5478% |
| coffeescript | 6795.14 | 0.1832% | 292446 | 0.5068% |
| haskell | 6306.12 | 0.17% | 217325 | 0.3766% |
| swift | 5902.716 | 0.1591% | 319289 | 0.5534% |
| lua | 5763.12 | 0.1554% | 139091 | 0.2411% |
| svg | 5645.44 | 0.1522% | 27095 | 0.047% |
| gas | 5585.384 | 0.1506% | 15121 | 0.0262% |
| ocaml | 5355.4 | 0.1444% | 81360 | 0.141% |
| erlang | 5043.32 | 0.136% | 93685 | 0.1624% |
| makefile | 4238.512 | 0.1143% | 343379 | 0.5951% |
| asciidoc | 4138.588 | 0.1116% | 96671 | 0.1675% |
| emacs-lisp | 3988.652 | 0.1075% | 83228 | 0.1442% |
| scss | 3944.936 | 0.1064% | 288190 | 0.4995% |
| clojure | 3523.408 | 0.095% | 158674 | 0.275% |
| org | 3126.22 | 0.0843% | 30198 | 0.0523% |
| common-lisp | 2954.904 | 0.0797% | 74628 | 0.1293% |
| diff | 2586.048 | 0.0697% | 21021 | 0.0364% |
| groovy | 2569.14 | 0.0693% | 110057 | 0.1907% |
| html+erb | 2450.676 | 0.0661% | 225379 | 0.3906% |
| nesc | 2439.564 | 0.0658% | 473 | 0.0008% |
| dart | 2395.796 | 0.0646% | 56873 | 0.0986% |
| powershell | 2289.276 | 0.0617% | 55381 | 0.096% |
| f# | 2289.236 | 0.0617% | 66840 | 0.1158% |
| dm | 2223.144 | 0.0599% | 55584 | 0.0963% |
| kotlin | 2219.248 | 0.0598% | 124266 | 0.2154% |
| pascal | 2194.676 | 0.0592% | 42511 | 0.0737% |
| jsx | 2124.744 | 0.0573% | 139148 | 0.2412% |
| viml | 1948.208 | 0.0525% | 74062 | 0.1284% |
| actionscript | 1844.148 | 0.0497% | 28819 | 0.0499% |
| cython | 1736.588 | 0.0468% | 25927 | 0.0449% |
| turtle | 1698.948 | 0.0458% | 3882 | 0.0067% |
| less | 1616.564 | 0.0436% | 88634 | 0.1536% |
| mathematica | 1475.044 | 0.0398% | 925 | 0.0016% |
| xslt | 1441.456 | 0.0389% | 27956 | 0.0485% |
| scheme | 1249.244 | 0.0337% | 30546 | 0.0529% |
| perl6 | 1223.16 | 0.033% | 12167 | 0.0211% |
| edn | 1186.94 | 0.032% | 2289 | 0.004% |
| fortran | 1178.548 | 0.0318% | 13463 | 0.0233% |
| java-server-pages | 1173.072 | 0.0316% | 53574 | 0.0928% |
| standard-ml | 1133.476 | 0.0306% | 20097 | 0.0348% |
| cmake | 1132.068 | 0.0305% | 58446 | 0.1013% |
| json5 | 1108.2 | 0.0299% | 1827 | 0.0032% |
| vala | 1104.512 | 0.0298% | 14822 | 0.0257% |
| vue | 1093.8 | 0.0295% | 68967 | 0.1195% |
| freemarker | 1032.332 | 0.0278% | 36216 | 0.0628% |
| graphql | 1004.844 | 0.0271% | 2009 | 0.0035% |
| twig | 958.96 | 0.0259% | 39588 | 0.0686% |
| tcl | 869.832 | 0.0235% | 16407 | 0.0284% |
| pod | 859.016 | 0.0232% | 14922 | 0.0259% |
| dockerfile | 849.728 | 0.0229% | 259379 | 0.4495% |
| yacc | 845.704 | 0.0228% | 8230 | 0.0143% |
| postscript | 800.728 | 0.0216% | 903 | 0.0016% |
| racket | 796.64 | 0.0215% | 16615 | 0.0288% |
| eagle | 785.684 | 0.0212% | 2237 | 0.0039% |
| haxe | 772.896 | 0.0208% | 28447 | 0.0493% |
| julia | 752.068 | 0.0203% | 22695 | 0.0393% |
| handlebars | 740.816 | 0.02% | 49842 | 0.0864% |
| smarty | 720.944 | 0.0194% | 41065 | 0.0712% |
| visual-basic | 681.516 | 0.0184% | 10511 | 0.0182% |
| literate-haskell | 673.74 | 0.0182% | 10729 | 0.0186% |
| smalltalk | 665.892 | 0.018% | 11741 | 0.0203% |
| isabelle | 655.82 | 0.0177% | 8359 | 0.0145% |
| nimrod | 652.86 | 0.0176% | 12023 | 0.0208% |
| zig | 621.384 | 0.0168% | 4290 | 0.0074% |
| m4 | 603.584 | 0.0163% | 12465 | 0.0216% |
| max | 603.56 | 0.0163% | 2259 | 0.0039% |
| elixir | 558.116 | 0.015% | 35473 | 0.0615% |
| mako | 543.012 | 0.0146% | 8943 | 0.0155% |
| arduino | 534.176 | 0.0144% | 32350 | 0.0561% |
| jade | 531.4 | 0.0143% | 46993 | 0.0814% |
| haml | 502.012 | 0.0135% | 74792 | 0.1296% |
| elm | 481.968 | 0.013% | 18542 | 0.0321% |
| purebasic | 474.276 | 0.0128% | 36 | 0.0001% |
| coldfusion | 470.78 | 0.0127% | 9263 | 0.0161% |
| lean | 470.032 | 0.0127% | 7507 | 0.013% |
| r | 454.32 | 0.0122% | 12858 | 0.0223% |
| cuda | 437.668 | 0.0118% | 11450 | 0.0198% |
| textile | 425.116 | 0.0115% | 18491 | 0.032% |
| robotframework | 421.612 | 0.0114% | 9211 | 0.016% |
| abap | 409.62 | 0.011% | 1955 | 0.0034% |
| rdoc | 397.028 | 0.0107% | 38760 | 0.0672% |
| llvm | 382.2 | 0.0103% | 10727 | 0.0186% |
| ada | 380.7 | 0.0103% | 13258 | 0.023% |
| batchfile | 372.16 | 0.01% | 43674 | 0.0757% |
| qml | 361.452 | 0.0097% | 19360 | 0.0336% |
| jasmin | 359.82 | 0.0097% | 4782 | 0.0083% |
| assembly | 343.62 | 0.0093% | 8126 | 0.0141% |
| g-code | 334.964 | 0.009% | 3690 | 0.0064% |
| cucumber | 331.38 | 0.0089% | 26677 | 0.0462% |
| html+php | 323.348 | 0.0087% | 18381 | 0.0319% |
| kicad | 321.936 | 0.0087% | 759 | 0.0013% |
| api-blueprint | 317.852 | 0.0086% | 4765 | 0.0083% |
| eiffel | 311.48 | 0.0084% | 373 | 0.0006% |
| toml | 292.676 | 0.0079% | 63517 | 0.1101% |
| modelica | 284.616 | 0.0077% | 2611 | 0.0045% |
| bitbake | 277.576 | 0.0075% | 43239 | 0.0749% |
| lex | 275.96 | 0.0074% | 705 | 0.0012% |
| stylus | 273.056 | 0.0074% | 21967 | 0.0381% |
| protocol-buffer | 254.124 | 0.0069% | 9202 | 0.0159% |
| unknown | 252.228 | 0.0068% | 30570 | 0.053% |
| nit | 244.54 | 0.0066% | 4951 | 0.0086% |
| factor | 241.192 | 0.0065% | 15378 | 0.0267% |
| xs | 239.04 | 0.0064% | 3215 | 0.0056% |
| sass | 230.648 | 0.0062% | 23144 | 0.0401% |
| parrot-internal-representation | 230.196 | 0.0062% | 6231 | 0.0108% |
| html+django | 217.04 | 0.0059% | 10535 | 0.0183% |
| mediawiki | 214.324 | 0.0058% | 10188 | 0.0177% |
| logos | 212.296 | 0.0057% | 1733 | 0.003% |
| genshi | 209.3 | 0.0056% | 956 | 0.0017% |
| coldfusion-cfc | 208.164 | 0.0056% | 4410 | 0.0076% |
| xtend | 179.544 | 0.0048% | 7775 | 0.0135% |
| sqf | 168.656 | 0.0045% | 7778 | 0.0135% |
| vhdl | 155.948 | 0.0042% | 2185 | 0.0038% |
| antlr | 143.548 | 0.0039% | 3651 | 0.0063% |
| systemverilog | 140.192 | 0.0038% | 3944 | 0.0068% |
| hcl | 136.752 | 0.0037% | 13379 | 0.0232% |
| asp | 136.104 | 0.0037% | 4286 | 0.0074% |
| nsis | 129.124 | 0.0035% | 4048 | 0.007% |
| inform-7 | 120.188 | 0.0032% | 184 | 0.0003% |
| slim | 119.036 | 0.0032% | 18726 | 0.0325% |
| groovy-server-pages | 117.368 | 0.0032% | 6695 | 0.0116% |
| ceylon | 116.144 | 0.0031% | 7256 | 0.0126% |
| fish | 111.28 | 0.003% | 15351 | 0.0266% |
| processing | 108.58 | 0.0029% | 5912 | 0.0102% |
| component-pascal | 105.5 | 0.0028% | 43 | 0.0001% |
| lasso | 104.168 | 0.0028% | 67 | 0.0001% |
| glsl | 99.488 | 0.0027% | 9478 | 0.0164% |
| saltstack | 98.196 | 0.0026% | 12314 | 0.0213% |
| xbase | 94.424 | 0.0025% | 1670 | 0.0029% |
| autohotkey | 94.22 | 0.0025% | 1452 | 0.0025% |
| liquid | 93.792 | 0.0025% | 2651 | 0.0046% |
| purescript | 92.412 | 0.0025% | 5024 | 0.0087% |
| agda | 92.06 | 0.0025% | 4956 | 0.0086% |
| inno-setup | 91.36 | 0.0025% | 3014 | 0.0052% |
| oz | 90.476 | 0.0024% | 1551 | 0.0027% |
| chapel | 89.62 | 0.0024% | 26447 | 0.0458% |
| arc | 87.212 | 0.0024% | 758 | 0.0013% |
| opencl | 86.432 | 0.0023% | 2489 | 0.0043% |
| graphviz-dot | 85.804 | 0.0023% | 1525 | 0.0026% |
| pawn | 85.424 | 0.0023% | 580 | 0.001% |
| jsoniq | 75.152 | 0.002% | 1343 | 0.0023% |
| bluespec | 72.38 | 0.002% | 2500 | 0.0043% |
| smali | 71.38 | 0.0019% | 174 | 0.0003% |
| krl | 69.868 | 0.0019% | 1879 | 0.0033% |
| maple | 68.284 | 0.0018% | 1311 | 0.0023% |
| unrealscript | 67.668 | 0.0018% | 585 | 0.001% |
| ooc | 63.188 | 0.0017% | 3416 | 0.0059% |
| pure-data | 62.624 | 0.0017% | 603 | 0.001% |
| xquery | 61.956 | 0.0017% | 2237 | 0.0039% |
| digital-command-language | 59.644 | 0.0016% | 833 | 0.0014% |
| moonscript | 59.208 | 0.0016% | 1951 | 0.0034% |
| awk | 57.176 | 0.0015% | 2206 | 0.0038% |
| pike | 52.872 | 0.0014% | 1262 | 0.0022% |
| livescript | 51.228 | 0.0014% | 5194 | 0.009% |
| solidity | 50.856 | 0.0014% | 3689 | 0.0064% |
| monkey | 48.256 | 0.0013% | 1367 | 0.0024% |
| jsonld | 48.012 | 0.0013% | 462 | 0.0008% |
| zephir | 42.684 | 0.0012% | 1265 | 0.0022% |
| crystal | 41.924 | 0.0011% | 4217 | 0.0073% |
| rhtml | 41.02 | 0.0011% | 4551 | 0.0079% |
| stata | 40.684 | 0.0011% | 1344 | 0.0023% |
| idris | 39.896 | 0.0011% | 3025 | 0.0052% |
| raml | 39.388 | 0.0011% | 948 | 0.0016% |
| openscad | 37.732 | 0.001% | 2178 | 0.0038% |
| red | 35.26 | 0.001% | 1108 | 0.0019% |
| c2hs-haskell | 34.472 | 0.0009% | 1021 | 0.0018% |
| cycript | 33.96 | 0.0009% | 197 | 0.0003% |
| applescript | 33.512 | 0.0009% | 1304 | 0.0023% |
| mupad | 32.488 | 0.0009% | 178 | 0.0003% |
| literate-agda | 31.384 | 0.0008% | 567 | 0.001% |
| boo | 31.172 | 0.0008% | 26289 | 0.0456% |
| sourcepawn | 29.528 | 0.0008% | 717 | 0.0012% |
| qmake | 29.508 | 0.0008% | 3632 | 0.0063% |
| ragel-in-ruby-host | 28.296 | 0.0008% | 888 | 0.0015% |
| io | 27.952 | 0.0008% | 1247 | 0.0022% |
| desktop | 27.648 | 0.0007% | 5021 | 0.0087% |
| propeller-spin | 26.772 | 0.0007% | 625 | 0.0011% |
| thrift | 26.748 | 0.0007% | 1007 | 0.0017% |
| volt | 25.052 | 0.0007% | 1660 | 0.0029% |
| xproc | 24.212 | 0.0007% | 914 | 0.0016% |
| igor-pro | 23.748 | 0.0006% | 388 | 0.0007% |
| lolcode | 23.74 | 0.0006% | 24861 | 0.0431% |
| html+eex | 21.412 | 0.0006% | 2100 | 0.0036% |
| logtalk | 20.428 | 0.0006% | 1035 | 0.0018% |
| mirah | 20.104 | 0.0005% | 706 | 0.0012% |
| gnuplot | 19.676 | 0.0005% | 889 | 0.0015% |
| literate-coffeescript | 19.016 | 0.0005% | 1041 | 0.0018% |
| jflex | 18.608 | 0.0005% | 555 | 0.001% |
| emberscript | 18.392 | 0.0005% | 1024 | 0.0018% |
| cobol | 17.0 | 0.0005% | 24953 | 0.0432% |
| yang | 16.94 | 0.0005% | 597 | 0.001% |
| rebol | 16.468 | 0.0004% | 239 | 0.0004% |
| linker-script | 16.084 | 0.0004% | 1604 | 0.0028% |
| cartocss | 15.916 | 0.0004% | 555 | 0.001% |
| urweb | 13.068 | 0.0004% | 304 | 0.0005% |
| rmarkdown | 13.032 | 0.0004% | 750 | 0.0013% |
| darcs-patch | 13.008 | 0.0004% | 80 | 0.0001% |
| csound | 12.852 | 0.0003% | 229 | 0.0004% |
| squirrel | 12.844 | 0.0003% | 531 | 0.0009% |
| apl | 12.56 | 0.0003% | 586 | 0.001% |
| hlsl | 12.168 | 0.0003% | 1529 | 0.0026% |
| latte | 11.888 | 0.0003% | 1380 | 0.0024% |
| pony | 11.836 | 0.0003% | 624 | 0.0011% |
| ioke | 10.86 | 0.0003% | 373 | 0.0006% |
| hy | 10.512 | 0.0003% | 879 | 0.0015% |
| uno | 10.356 | 0.0003% | 628 | 0.0011% |
| pan | 10.336 | 0.0003% | 637 | 0.0011% |
| xojo | 10.308 | 0.0003% | 642 | 0.0011% |
| papyrus | 10.256 | 0.0003% | 130 | 0.0002% |
| stan | 10.252 | 0.0003% | 540 | 0.0009% |
| slash | 9.904 | 0.0003% | 640 | 0.0011% |
| supercollider | 9.796 | 0.0003% | 318 | 0.0006% |
| vcl | 9.456 | 0.0003% | 747 | 0.0013% |
| smt | 9.032 | 0.0002% | 117 | 0.0002% |
| glyph | 8.948 | 0.0002% | 7 | 0.0% |
| wisp | 8.736 | 0.0002% | 262 | 0.0005% |
| renpy | 8.3 | 0.0002% | 421 | 0.0007% |
| clips | 7.728 | 0.0002% | 450 | 0.0008% |
| dns-zone | 7.56 | 0.0002% | 54 | 0.0001% |
| sas | 7.536 | 0.0002% | 269 | 0.0005% |
| rouge | 7.196 | 0.0002% | 396 | 0.0007% |
| ec | 7.032 | 0.0002% | 94 | 0.0002% |
| dylan | 6.82 | 0.0002% | 280 | 0.0005% |
| tcsh | 6.524 | 0.0002% | 748 | 0.0013% |
| aspectj | 6.332 | 0.0002% | 451 | 0.0008% |
| netlogo | 6.304 | 0.0002% | 140 | 0.0002% |
| gap | 6.096 | 0.0002% | 46 | 0.0001% |
| fancy | 5.952 | 0.0002% | 675 | 0.0012% |
| coq | 5.744 | 0.0002% | 80 | 0.0001% |
| click | 5.74 | 0.0002% | 9 | 0.0% |
| capn-proto | 5.644 | 0.0002% | 330 | 0.0006% |
| flux | 5.572 | 0.0002% | 47 | 0.0001% |
| forth | 5.512 | 0.0001% | 265 | 0.0005% |
| ats | 5.424 | 0.0001% | 383 | 0.0007% |
| netlinx | 5.172 | 0.0001% | 144 | 0.0002% |
| clean | 5.068 | 0.0001% | 171 | 0.0003% |
| parrot-assembly | 4.664 | 0.0001% | 227 | 0.0004% |
| alloy | 4.644 | 0.0001% | 203 | 0.0004% |
| lfe | 4.576 | 0.0001% | 287 | 0.0005% |
| gdscript | 4.488 | 0.0001% | 460 | 0.0008% |
| augeas | 4.444 | 0.0001% | 395 | 0.0007% |
| sparql | 4.404 | 0.0001% | 1036 | 0.0018% |
| lilypond | 4.308 | 0.0001% | 265 | 0.0005% |
| scilab | 4.088 | 0.0001% | 375 | 0.0006% |
| autoit | 4.06 | 0.0001% | 279 | 0.0005% |
| myghty | 3.864 | 0.0001% | 105 | 0.0002% |
| blitzmax | 3.74 | 0.0001% | 220 | 0.0004% |
| creole | 3.416 | 0.0001% | 337 | 0.0006% |
| harbour | 3.336 | 0.0001% | 107 | 0.0002% |
| piglatin | 3.168 | 0.0001% | 513 | 0.0009% |
| opa | 3.164 | 0.0001% | 211 | 0.0004% |
| sage | 3.032 | 0.0001% | 414 | 0.0007% |
| ston | 2.848 | 0.0001% | 414 | 0.0007% |
| maxscript | 2.8 | 0.0001% | 47 | 0.0001% |
| lsl | 2.68 | 0.0001% | 74 | 0.0001% |
| gentoo-ebuild | 2.576 | 0.0001% | 601 | 0.001% |
| nu | 2.38 | 0.0001% | 170 | 0.0003% |
| bro | 2.34 | 0.0001% | 333 | 0.0006% |
| xc | 2.02 | 0.0001% | 88 | 0.0002% |
| j | 1.808 | 0.0% | 142 | 0.0002% |
| metal | 1.724 | 0.0% | 151 | 0.0003% |
| module-management-system | 1.544 | 0.0% | 91 | 0.0002% |
| webidl | 1.508 | 0.0% | 96 | 0.0002% |
| tea | 1.468 | 0.0% | 29 | 0.0001% |
| redcode | 1.272 | 0.0% | 149 | 0.0003% |
| shen | 1.2 | 0.0% | 71 | 0.0001% |
| pov-ray-sdl | 1.136 | 0.0% | 104 | 0.0002% |
| x10 | 1.008 | 0.0% | 33 | 0.0001% |
| brainfuck | 0.964 | 0.0% | 167 | 0.0003% |
| ninja | 0.952 | 0.0% | 187 | 0.0003% |
| golo | 0.896 | 0.0% | 115 | 0.0002% |
| webassembly | 0.86 | 0.0% | 83 | 0.0001% |
| self | 0.824 | 0.0% | 15 | 0.0% |
| labview | 0.808 | 0.0% | 61 | 0.0001% |
| octave | 0.804 | 0.0% | 12 | 0.0% |
| pogoscript | 0.804 | 0.0% | 74 | 0.0001% |
| d | 0.796 | 0.0% | 20 | 0.0% |
| http | 0.736 | 0.0% | 140 | 0.0002% |
| ecl | 0.664 | 0.0% | 48 | 0.0001% |
| chuck | 0.584 | 0.0% | 99 | 0.0002% |
| gosu | 0.524 | 0.0% | 60 | 0.0001% |
| parrot | 0.52 | 0.0% | 17 | 0.0% |
| opal | 0.472 | 0.0% | 69 | 0.0001% |
| objective-j | 0.456 | 0.0% | 37 | 0.0001% |
| kit | 0.412 | 0.0% | 48 | 0.0001% |
| gams | 0.376 | 0.0% | 18 | 0.0% |
| prolog | 0.276 | 0.0% | 35 | 0.0001% |
| clarion | 0.268 | 0.0% | 13 | 0.0% |
| mask | 0.252 | 0.0% | 37 | 0.0001% |
| brightscript | 0.244 | 0.0% | 28 | 0.0% |
| scaml | 0.184 | 0.0% | 31 | 0.0001% |
| matlab | 0.164 | 0.0% | 29 | 0.0001% |
| idl | 0.148 | 0.0% | 1 | 0.0% |
| ags-script | 0.124 | 0.0% | 31 | 0.0001% |
| lookml | 0.12 | 0.0% | 10 | 0.0% |
| apacheconf | 0.108 | 0.0% | 59 | 0.0001% |
| oxygene | 0.104 | 0.0% | 9 | 0.0% |
| txl | 0.096 | 0.0% | 3 | 0.0% |
| grammatical-framework | 0.088 | 0.0% | 39 | 0.0001% |
| renderscript | 0.064 | 0.0% | 54 | 0.0001% |
| mtml | 0.052 | 0.0% | 13 | 0.0% |
| unified-parallel-c | 0.052 | 0.0% | 6 | 0.0% |
| dogescript | 0.04 | 0.0% | 10 | 0.0% |
| gentoo-eclass | 0.04 | 0.0% | 6 | 0.0% |
| zimpl | 0.04 | 0.0% | 7 | 0.0% |
| irc-log | 0.036 | 0.0% | 9 | 0.0% |
| fantom | 0.028 | 0.0% | 11 | 0.0% |
| numpy | 0.028 | 0.0% | 1 | 0.0% |
| cirru | 0.024 | 0.0% | 4 | 0.0% |
| xpages | 0.024 | 0.0% | 7 | 0.0% |
| nginx | 0.02 | 0.0% | 6 | 0.0% |
| objdump | 0.02 | 0.0% | 1 | 0.0% |
| python-traceback | 0.02 | 0.0% | 10 | 0.0% |
| realbasic | 0.012 | 0.0% | 1 | 0.0% |
| befunge | 0.008 | 0.0% | 2 | 0.0% |
| bison | 0.008 | 0.0% | 1 | 0.0% |
| m | 0.008 | 0.0% | 1 | 0.0% |
| omgrofl | 0.008 | 0.0% | 1 | 0.0% |
## Additional Information
### Licensing Information
Each sample comes from a code repository with a permissive license. The license is provided by the `license` field for each sample.
### Citation Information
```bibtex
@article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv preprint arXiv:2308.07124},
year={2023}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/scala-sv | ScandEval | 2023-07-05T09:49:04Z | 252 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-07-05T09:49:04Z | 2022-06-27T15:14:47.000Z | 2022-06-27T15:14:47 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- sv
size_categories:
- 1K<n<10K
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nahrawy/VIDIT-Depth-ControlNet | Nahrawy | 2023-05-06T17:54:43Z | 252 | 0 | null | [
"region:us"
] | 2023-05-06T17:54:43Z | 2023-04-23T18:38:24.000Z | 2023-04-23T18:38:24 | ---
dataset_info:
features:
- name: scene
dtype: string
- name: image
dtype: image
- name: depth_map
dtype: image
- name: direction
dtype: string
- name: temprature
dtype: int32
- name: caption
dtype: string
splits:
- name: train
num_bytes: 20575644792.0
num_examples: 12000
download_size: 20108431280
dataset_size: 20575644792.0
---
# VIDIT Dataset
This is a version of the [VIDIT dataset](https://github.com/majedelhelou/VIDIT) equipped for training ControlNet using depth maps conditioning.
VIDIT includes 390 different Unreal Engine scenes, each captured with 40 illumination settings, resulting in 15,600 images. The illumination settings are all the combinations of 5 color temperatures (2500K, 3500K, 4500K, 5500K and 6500K) and 8 light directions (N, NE, E, SE, S, SW, W, NW). Original image resolution is 1024x1024.
We include in this version only the training split containing only 300 scenes.
Captions were generated using the [BLIP-2, Flan T5-xxl](https://huggingface.co/Salesforce/blip2-flan-t5-xxl) model.
Depth maps were generated using the [GLPN fine-tuned on NYUv2 ](https://huggingface.co/vinvino02/glpn-nyu) model.
## Examples with varying direction
## Examples with varying color temperature
## Disclaimer
I do not own any of this data.
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starmpcc/Asclepius-Synthetic-Clinical-Notes | starmpcc | 2023-09-04T01:27:17Z | 252 | 22 | null | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"medical",
"arxiv:2309.00237",
"region:us"
] | 2023-09-04T01:27:17Z | 2023-09-01T01:47:59.000Z | 2023-09-01T01:47:59 | ---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
- summarization
- text-generation
- conversational
language:
- en
tags:
- medical
pretty_name: 'Asclepius: Synthetic Clincal Notes & Instruction Dataset'
size_categories:
- 100K<n<1M
---
# Asclepius: Synthetic Clincal Notes & Instruction Dataset
## Dataset Description
- **Repository:**
- [Github](https://github.com/starmpcc/Asclepius)
- **Paper:**
- https://arxiv.org/abs/2309.00237
- **MODEL:**
- https://huggingface.co/starmpcc/Asclepius-13B
- https://huggingface.co/starmpcc/Asclepius-7B
### Dataset Summary
This dataset is official dataset for Asclepius [(arxiv)](https://arxiv.org/abs/2309.00237)
This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs.
- We first synthesized synthetic notes from [PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients) case reports with GPT-3.5
- Then, we generate instruction-answer pairs for 157k synthetic discharge summaries
### Supported Tasks and Leaderboards
- This dataset covers below 8 tasks
- Named Entity Recognition
- Abbreviation Expansion
- Relation Extraction
- Temporal Information Extraction
- Coreference Resolution
- Paraphrasing
- Summarization
- Question Answering
### Languages
English
## Dataset Structure
### Data Instances
- `synthetic.csv`
- Clinical Note - Question - Answer pairs
### Data Fields
- `patient_id`: Unique case report id from PMC-Patients
- `patient`: Case report text
- `question`: GPT-3.5 generated instruction from patient. The used prompt can be checked on github.
- `answer`: GPT-3.5 generated answer for given case report and question
- `task`: Corresponding category of question. One of above listsed
## Dataset Creation
### Source Data
[PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients)
### Annotations
We used GPT-3.5-turbo (version 0314).
You can check the prompts on our github.
## Additional Information
### Licensing Information
CC-BY-NC-SA 4.0
### Citation Information
@misc{kweon2023publicly,
title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes},
author={Sunjun Kweon and Junu Kim and Jiyoun Kim and Sujeong Im and Eunbyeol Cho and Seongsu Bae and Jungwoo Oh and Gyubok Lee and Jong Hak Moon and Seng Chan You and Seungjin Baek and Chang Hoon Han and Yoon Bin Jung and Yohan Jo and Edward Choi},
year={2023},
eprint={2309.00237},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
stockmark/ner-wikipedia-dataset | stockmark | 2023-09-02T14:42:18Z | 252 | 1 | null | [
"task_categories:token-classification",
"language:ja",
"license:cc-by-sa-3.0",
"Named Entity Recognition",
"NER",
"region:us"
] | 2023-09-02T14:42:18Z | 2023-09-02T14:38:55.000Z | 2023-09-02T14:38:55 | ---
license: cc-by-sa-3.0
language:
- ja
tags:
- Named Entity Recognition
- NER
task_categories:
- token-classification
---
# Wikipediaを用いた日本語の固有表現抽出データセット
- GitHub: https://github.com/stockmarkteam/ner-wikipedia-dataset/
- LICENSE: CC-BY-SA 3.0
Developed by Stockmark Inc. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nus-yam/bigfixes | nus-yam | 2023-11-16T08:44:09Z | 252 | 1 | null | [
"region:us"
] | 2023-11-16T08:44:09Z | 2023-10-26T05:55:10.000Z | 2023-10-26T05:55:10 | ---
---
pretty_name: BigFixes
description: A clean union of BigVul and CVE-Fixes.
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: cleantest
path: clean_test.csv
- split: test
path: test.csv
- split: output
path: output.csv
---
---
# Information
This is a clean version of the union of BigVul and CVE-Fixes used
[here](https://huggingface.co/datasets/MickyMike/cvefixes_bigvul).
We have
four splits:
- `train`, which has the de-duplicated training data;
- `cleantest`, which has de-duplicated testing data that is completely disjoint from the
training set;
- `test`, which has the deduplicated training data with a
significant intersection with the training data (as seen in the original
repository);
- `output`, which is [VulRepair](https://github.com/awsm-research/VulRepair/tree/main)'s output on the data found in the `test` split.
Our preprocessing is available in `preprocessing.ipynb`.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allenai/drug-combo-extraction | allenai | 2022-05-04T04:12:53Z | 251 | 6 | null | [
"license:mit",
"region:us"
] | 2022-05-04T04:12:53Z | 2022-05-04T03:26:01.000Z | 2022-05-04T03:26:01 | ---
license: mit
---
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jacob-hugging-face/job-descriptions | jacob-hugging-face | 2023-08-18T20:07:48Z | 251 | 7 | null | [
"license:llama2",
"region:us"
] | 2023-08-18T20:07:48Z | 2023-08-18T20:06:24.000Z | 2023-08-18T20:06:24 | ---
license: llama2
---
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stas/wmt14-en-de-pre-processed | stas | 2021-02-16T04:41:04Z | 250 | 1 | null | [
"region:us"
] | 2021-02-16T04:41:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # WMT14 English-German Translation Data w/ further preprocessing
The original pre-processing script is [here](https://github.com/pytorch/fairseq/blob/master/examples/translation/prepare-wmt14en2de.sh).
This pre-processed dataset was created by running:
```
git clone https://github.com/pytorch/fairseq
cd fairseq
cd examples/translation/
./prepare-wmt14en2de.sh
```
It was originally used by `transformers` [`finetune_trainer.py`](https://github.com/huggingface/transformers/blob/641f418e102218c4bf16fcd3124bfebed6217ef6/examples/seq2seq/finetune_trainer.py)
The data itself resides at https://cdn-datasets.huggingface.co/translation/wmt_en_de.tgz
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ArtifactAI/arxiv-physics-instruct-tune-30k | ArtifactAI | 2023-06-20T04:35:10Z | 250 | 16 | null | [
"doi:10.57967/hf/0801",
"region:us"
] | 2023-06-20T04:35:10Z | 2023-06-20T04:25:54.000Z | 2023-06-20T04:25:54 | Entry not found | [
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sil-ai/bloom-vist | sil-ai | 2022-12-06T19:37:53Z | 249 | 3 | null | [
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"language:war",
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"language:zlm",
"language:zuh",
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"language:zu",
"license:cc-by-4.0",
"license:cc-by-nc-4.0",
"license:cc-by-nd-4.0",
"license:cc-by-sa-4.0",
"license:cc-by-nc-nd-4.0",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-12-06T19:37:53Z | 2022-05-05T16:25:54.000Z | 2022-05-05T16:25:54 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- afr
- af
- aaa
- abc
- ada
- adq
- aeu
- agq
- ags
- ahk
- aia
- ajz
- aka
- ak
- ame
- amh
- am
- amp
- amu
- ann
- aph
- awa
- awb
- azn
- azo
- bag
- bam
- bm
- baw
- bax
- bbk
- bcc
- bce
- bec
- bef
- ben
- bn
- bfd
- bfm
- bfn
- bgf
- bho
- bhs
- bis
- bi
- bjn
- bjr
- bkc
- bkh
- bkm
- bkx
- bob
- bod
- bo
- boz
- bqm
- bra
- brb
- bri
- brv
- bss
- bud
- buo
- bwt
- bwx
- bxa
- bya
- bze
- bzi
- cak
- cbr
- ceb
- cgc
- chd
- chp
- cim
- clo
- cmn
- zh
- cmo
- csw
- cuh
- cuv
- dag
- ddg
- ded
- deu
- de
- dig
- dje
- dmg
- dnw
- dtp
- dtr
- dty
- dug
- eee
- ekm
- enb
- enc
- eng
- en
- ewo
- fas
- fa
- fil
- fli
- fon
- fra
- fr
- fub
- fuh
- gal
- gbj
- gou
- gsw
- guc
- guj
- gu
- guz
- gwc
- hao
- hat
- ht
- hau
- ha
- hbb
- hig
- hil
- hin
- hi
- hla
- hna
- hre
- hro
- idt
- ilo
- ind
- id
- ino
- isu
- ita
- it
- jgo
- jmx
- jpn
- ja
- jra
- kak
- kam
- kan
- kn
- kau
- kr
- kbq
- kbx
- kby
- kek
- ken
- khb
- khm
- km
- kik
- ki
- kin
- rw
- kir
- ky
- kjb
- kmg
- kmr
- ku
- kms
- kmu
- kor
- ko
- kqr
- krr
- ksw
- kur
- ku
- kvt
- kwd
- kwu
- kwx
- kxp
- kyq
- laj
- lan
- lao
- lo
- lbr
- lfa
- lgg
- lgr
- lhm
- lhu
- lkb
- llg
- lmp
- lns
- loh
- lsi
- lts
- lug
- lg
- luy
- lwl
- mai
- mal
- ml
- mam
- mar
- mr
- mdr
- mfh
- mfj
- mgg
- mgm
- mgo
- mgq
- mhx
- miy
- mkz
- mle
- mlk
- mlw
- mmu
- mne
- mnf
- mnw
- mot
- mqj
- mrn
- mry
- msb
- muv
- mve
- mxu
- mya
- my
- myk
- myx
- mzm
- nas
- nco
- nep
- ne
- new
- nge
- ngn
- nhx
- njy
- nla
- nld
- nl
- nlv
- nod
- nsk
- nsn
- nso
- nst
- nuj
- nwe
- nwi
- nxa
- nxl
- nya
- ny
- nyo
- nyu
- nza
- odk
- oji
- oj
- oki
- omw
- ori
- or
- ozm
- pae
- pag
- pan
- pa
- pbt
- pce
- pcg
- pdu
- pea
- pex
- pis
- pkb
- pmf
- pnz
- por
- pt
- psp
- pwg
- qaa
- qub
- quc
- quf
- quz
- qve
- qvh
- qvm
- qvo
- qxh
- rel
- rnl
- ron
- ro
- roo
- rue
- rug
- rus
- ru
- san
- sa
- saq
- sat
- sdk
- sea
- sgd
- shn
- sml
- snk
- snl
- som
- so
- sot
- st
- sox
- spa
- es
- sps
- ssn
- stk
- swa
- sw
- swh
- sxb
- syw
- taj
- tam
- ta
- tbj
- tdb
- tdg
- tdt
- teo
- tet
- tgk
- tg
- tha
- th
- the
- thk
- thl
- thy
- tio
- tkd
- tnl
- tnn
- tnp
- tnt
- tod
- tom
- tpi
- tpl
- tpu
- tsb
- tsn
- tn
- tso
- ts
- tuv
- tuz
- tvs
- udg
- unr
- urd
- ur
- uzb
- uz
- ven
- ve
- vie
- vi
- vif
- war
- wbm
- wbr
- wms
- wni
- wnk
- wtk
- xho
- xh
- xkg
- xmd
- xmg
- xmm
- xog
- xty
- yas
- yav
- ybb
- ybh
- ybi
- ydd
- yea
- yet
- yid
- yi
- yin
- ymp
- zaw
- zho
- zh
- zlm
- zuh
- zul
- zu
license:
- cc-by-4.0
- cc-by-nc-4.0
- cc-by-nd-4.0
- cc-by-sa-4.0
- cc-by-nc-nd-4.0
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
paperswithcode_id: null
pretty_name: BloomVIST
extra_gated_prompt: |-
One more step before getting this dataset. This dataset is open access and available only for non-commercial use (except for portions of the dataset labeled with a `cc-by-sa` license). A "license" field paired with each of the dataset entries/samples specifies the Creative Commons license for that entry/sample.
These [Creative Commons licenses](https://creativecommons.org/about/cclicenses/) specify that:
1. You cannot use the dataset for or directed toward commercial advantage or monetary compensation (except for those portions of the dataset labeled specifically with a `cc-by-sa` license. If you would like to ask about commercial uses of this dataset, please [email us](mailto:sj@derivation.co).
2. Any public, non-commercial use of the data must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. For those portions of the dataset marked with an ND license, you cannot remix, transform, or build upon the material, and you may not distribute modified material.
In addition to the above implied by Creative Commons and when clicking "Access Repository" below, you agree:
1. Not to use the dataset for any use intended to or which has the effect of harming or enabling discrimination against individuals or groups based on legally protected characteristics or categories, including but not limited to discrimination against Indigenous People as outlined in Articles 2; 13-16; and 31 of the United Nations Declaration on the Rights of Indigenous People, 13 September 2007 and as subsequently amended and revised.
2. That your *contact information* (email address and username) can be shared with the model authors as well.
extra_gated_fields:
I have read the License and agree with its terms: checkbox
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
<!-- - [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions) -->
## Dataset Description
- **Homepage:** [SIL AI](https://ai.sil.org/)
- **Point of Contact:** [SIL AI email](mailto:idx_aqua@sil.org)
- **Source Data:** [Bloom Library](https://bloomlibrary.org/)
 
## Dataset Summary
**Bloom** is free, open-source software and an associated website [Bloom Library](https://bloomlibrary.org/), app, and services developed by [SIL International](https://www.sil.org/). Bloom’s primary goal is to equip non-dominant language communities and their members to create the literature they want for their community and children. Bloom also serves organizations that help such communities develop literature and education or other aspects of community development.
This version of the Bloom Library data is developed specifically for the visual story telling (or VIST) task. It includes data from 364 languages across 31 language families. There is a mean of 32 stories and median of 2 stories per language.
**Note**: If you speak one of these languages and can help provide feedback or corrections, please let us know!
**Note**: Although this data was used in the training of the [BLOOM model](https://huggingface.co/bigscience/bloom), this dataset only represents a small portion of the data used to train that model. Data from "Bloom Library" was combined with a large number of other datasets to train that model. "Bloom Library" is a project that existed prior to the BLOOM model, and is something separate. All that to say... We were using the "Bloom" name before it was cool. 😉
## Languages
Of the 500+ languages listed at BloomLibrary.org, there are 363 languages available in this dataset. Here are the corresponding ISO 639-3 codes:
aaa, abc, ada, adq, aeu, afr, agq, ags, ahk, aia, ajz, aka, ame, amh, amp, amu, ann, aph, awa, awb, azn, azo, bag, bam, baw, bax, bbk, bcc, bce, bec, bef, ben, bfd, bfm, bfn, bgf, bho, bhs, bis, bjn, bjr, bkc, bkh, bkm, bkx, bob, bod, boz, bqm, bra, brb, bri, brv, bss, bud, buo, bwt, bwx, bxa, bya, bze, bzi, cak, cbr, ceb, cgc, chd, chp, cim, clo, cmn, cmo, csw, cuh, cuv, dag, ddg, ded, deu, dig, dje, dmg, dnw, dtp, dtr, dty, dug, eee, ekm, enb, enc, eng, ewo, fas, fil, fli, fon, fra, fub, fuh, gal, gbj, gou, gsw, guc, guj, guz, gwc, hao, hat, hau, hbb, hig, hil, hin, hla, hna, hre, hro, idt, ilo, ind, ino, isu, ita, jgo, jmx, jpn, jra, kak, kam, kan, kau, kbq, kbx, kby, kek, ken, khb, khm, kik, kin, kir, kjb, kmg, kmr, kms, kmu, kor, kqr, krr, ksw, kur, kvt, kwd, kwu, kwx, kxp, kyq, laj, lan, lao, lbr, lfa, lgg, lgr, lhm, lhu, lkb, llg, lmp, lns, loh, lsi, lts, lug, luy, lwl, mai, mal, mam, mar, mdr, mfh, mfj, mgg, mgm, mgo, mgq, mhx, miy, mkz, mle, mlk, mlw, mmu, mne, mnf, mnw, mot, mqj, mrn, mry, msb, muv, mve, mxu, mya, myk, myx, mzm, nas, nco, nep, new, nge, ngn, nhx, njy, nla, nld, nlv, nod, nsk, nsn, nso, nst, nuj, nwe, nwi, nxa, nxl, nya, nyo, nyu, nza, odk, oji, oki, omw, ori, ozm, pae, pag, pan, pbt, pce, pcg, pdu, pea, pex, pis, pkb, pmf, pnz, por, psp, pwg, qub, quc, quf, quz, qve, qvh, qvm, qvo, qxh, rel, rnl, ron, roo, rue, rug, rus, san, saq, sat, sdk, sea, sgd, shn, sml, snk, snl, som, sot, sox, spa, sps, ssn, stk, swa, swh, sxb, syw, taj, tam, tbj, tdb, tdg, tdt, teo, tet, tgk, tha, the, thk, thl, thy, tio, tkd, tnl, tnn, tnp, tnt, tod, tom, tpi, tpl, tpu, tsb, tsn, tso, tuv, tuz, tvs, udg, unr, urd, uzb, ven, vie, vif, war, wbm, wbr, wms, wni, wnk, wtk, xho, xkg, xmd, xmg, xmm, xog, xty, yas, yav, ybb, ybh, ybi, ydd, yea, yet, yid, yin, ymp, zaw, zho, zlm, zuh, zul
## Dataset Statistics
Some of the languages included in the dataset just include 1 or a couple of "stories." For those with higher numbers of available stories we include the following numbers of stories:
| ISO639-3 Code | Stories | Image-Caption Pairs |
|:-----------|----------:|----------------------:|
| ahk | 55 | 493 |
| awa | 163 | 1200 |
| ben | 220 | 1938 |
| bho | 172 | 1163 |
| bis | 21 | 183 |
| brb | 22 | 330 |
| bzi | 66 | 497 |
| cak | 50 | 694 |
| ceb | 394 | 2806 |
| cgc | 182 | 1473 |
| deu | 22 | 250 |
| dty | 172 | 1310 |
| eng | 2187 | 24338 |
| fas | 128 | 620 |
| fil | 34 | 366 |
| fra | 315 | 4350 |
| hat | 224 | 1881 |
| hau | 229 | 1594 |
| ind | 232 | 1866 |
| jra | 56 | 575 |
| kak | 195 | 1416 |
| kek | 21 | 419 |
| khb | 31 | 167 |
| khm | 26 | 246 |
| kir | 278 | 2866 |
| kjb | 63 | 584 |
| kor | 129 | 2732 |
| krr | 29 | 362 |
| lsi | 22 | 173 |
| mai | 177 | 1186 |
| mam | 118 | 1058 |
| mhx | 51 | 544 |
| myk | 22 | 214 |
| nep | 194 | 1464 |
| new | 177 | 1225 |
| pbt | 203 | 979 |
| por | 148 | 2939 |
| quc | 99 | 817 |
| rus | 271 | 2977 |
| snk | 21 | 210 |
| spa | 444 | 5201 |
| swh | 34 | 387 |
| tdg | 31 | 231 |
| tha | 275 | 2929 |
| thl | 185 | 1464 |
| tpi | 137 | 1528 |
| tpu | 28 | 513 |
| zho | 42 | 339 |
## Dataset Structure
### Data Instances
The examples look like this for Hindi:
```
from datasets import load_dataset
# Specify the language code.
dataset = load_dataset("sil-ai/bloom-vist", 'hin')
# An individual samples consists of stories in the specified language code.
# To see a story:
print(dataset['train'][0]['story'])
```
This would produce an output:
```
{'image_id': ['4e9bdde5-996d-4a98-ac1c-d80fb6349314',
'614e4d51-bbdb-4538-98d3-f603c12dccd0',
'970d60bf-2acb-44ac-8ffb-5aa3f7989630',
'd4ad1199-863e-4929-a377-93276fe5caa8',
'0d9ad694-995a-433d-af4e-6f40ddfa208a',
'811176eb-c9f3-4226-8af5-e6c4e524c494',
'83180da7-4ba8-4104-a0d9-49aa2ef48f7a'],
'image_url': ['https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_03_Image_00011.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_04_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_05_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_06_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_0001.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_07_Image_00011.png',
'https://bloom-vist.s3.amazonaws.com/Saboo+and+Jojo/M_PB_2_-saboo-and-jojo_Page_09_Image_0001.png'],
'story_index': [0, 1, 2, 3, 4, 5, 6],
'story_id': ['cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6',
'cc34c1c7-c086-491b-8e6a-65572e1efdb6'],
'text': ['साबू ने एक कंकड़ को ठोकर मारी। कंकड़ लुढ़कता हुआ एक पेड़ के पास पहुँचा। पेड़ के तने पर मुलायम बाल थे। साबू ने छुए और ऊपर देखा, ऊपर, ऊपर और उससे भी ऊपर...दो आँखें नीचे देख रही थीं।',
'“हेलो, तुम कौन हो?” साबू को बड़ा अचम्भा हुआ।“हेलो, मैं जिराफ़ हूँ। मेरा नाम है जोजो। \xa0मैं तुम्हारे साथ खेल सकता हूँ। मेरी पीठ पर चढ़ जाओ, मैं तुम्हें घुमा के लाता हूँ।”',
'साबू जोजो की पीठ पर चढ़ गया और वे सड़क पर चल निकले। फिर पहाड़ी पर और शहर के बीचों बीच।\nसाबू खुशी से चिल्लाया, “जोजो दाएँ मुड़ो,\n बाएँ मुड़ो और फिर दाएँ।” अब वे उसकी दोस्त मुन्नी के घर पहुँच गये।',
'आज मुन्नी का जन्मदिन था। साबू को जोजो पर सवारी करते देख बच्चों ने ताली बजायी।\xa0\n जोजो ने गुब्बारे लटकाने में आन्टी की मदद करी क्योंकि वह इतना... लम्बा था।\xa0\n कितना आसान था!',
'जोजो ने सब बच्चों को सवारी कराई।\n उनके साथ बॉल भी खेली। बड़े मज़े की पार्टी थी।सब ने गाया, “हैप्पी बर्थ डे टु यू ।”\n आन्टी ने मेज़ पर समोसे, गुलाब जामुन और आइसक्रीम सजाई।',
'जोजो को आइसक्रीम बहुत पसन्द आई। अंकल उसके लिये एक बाल्टी भर के आइसक्रीम लाये। जोजो ने पूरी बाल्टी ख़त्म कर दी। \xa0अब घर जाने का समय हो गया।\n\nसब ने कहा, “बाय बाय जोजो, बाय बाय साबू।” साबू और जोजो घर लौटे।',
'']}
```
### Data Fields
The metadata fields below are available. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).
- **id**: id of the sample
- **title**: title of the book, e.g. "Going to Buy a Book".
- **license**: specific license used, e.g. "cc-by-sa" for "Creative Commons, by attribution, share-alike".
- **album_id**: an ID value corresponding to the set of images corresponding to the given story
- **story**: the sequenced story data including lists of image IDs, image URLs, and corresponding text
### Data Splits
Currently all languages include a train split only. In the future, we will be creating manual splits of the data.
## Changelog
- **6 December 2022** - dataset is made public | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
merve/supersoaker-failures | merve | 2022-09-08T16:06:06Z | 249 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-09-08T16:06:06Z | 2022-09-08T16:05:41.000Z | 2022-09-08T16:05:41 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/ATEC | C-MTEB | 2023-07-28T13:53:38Z | 249 | 2 | null | [
"region:us"
] | 2023-07-28T13:53:38Z | 2023-07-28T13:53:23.000Z | 2023-07-28T13:53:23 | ---
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: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int32
splits:
- name: train
num_bytes: 5689779
num_examples: 62477
- name: validation
num_bytes: 1823903
num_examples: 20000
- name: test
num_bytes: 1824603
num_examples: 20000
download_size: 5073390
dataset_size: 9338285
---
# Dataset Card for "ATEC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Tesutato/medical_multiple_choice_qa | Tesutato | 2023-11-15T08:10:31Z | 249 | 0 | null | [
"region:us"
] | 2023-11-15T08:10:31Z | 2023-11-06T12:39:10.000Z | 2023-11-06T12:39:10 | ---
configs:
- config_name: main_data
data_files: "dataset.csv"
- config_name: raw_data
data_files: "raw_dataset.csv"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/scala-da | ScandEval | 2023-07-05T09:50:02Z | 248 | 1 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-07-05T09:50:02Z | 2022-06-27T15:13:27.000Z | 2022-06-27T15:13:27 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- da
size_categories:
- 1K<n<10K
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-source-metrics/stars | open-source-metrics | 2023-11-22T17:41:45Z | 248 | 0 | null | [
"region:us"
] | 2023-11-22T17:41:45Z | 2023-03-23T12:51:59.000Z | 2023-03-23T12:51:59 | ---
dataset_info:
features:
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dtype: string
- name: dates
dtype: string
splits:
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download_size: 5247081
dataset_size: 9513985
configs:
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data_files:
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path: data/peft-*
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path: data/hub_docs-*
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path: data/evaluate-*
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path: data/huggingface_hub-*
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path: data/accelerate-*
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path: data/datasets-*
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path: data/optimum-*
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path: data/pytorch_image_models-*
- split: gradio
path: data/gradio-*
- split: tokenizers
path: data/tokenizers-*
- split: diffusers
path: data/diffusers-*
- split: transformers
path: data/transformers-*
- split: safetensors
path: data/safetensors-*
---
# Dataset Card for "stars"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yuan-yang/MALLS-v0 | yuan-yang | 2023-10-25T20:16:00Z | 248 | 2 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-10-25T20:16:00Z | 2023-05-31T19:01:19.000Z | 2023-05-31T19:01:19 | ---
license: cc-by-nc-4.0
viewer: true
task_categories:
- text-generation
language:
- en
pretty_name: MALLS NL-FOL Pairs 34K
size_categories:
- 10K<n<100K
---
# MALLS NL-FOL Pairs
## Dataset details
MALLS (large language **M**odel gener**A**ted natural-**L**anguage-to-first-order-**L**ogic pair**S**)
consists of pairs of real-world natural language (NL) statements and the corresponding first-order logic (FOL) rules annotations.
All pairs are generated by prompting GPT-4 and processed to ensure the validity of the FOL rules.
MALLS-v0 consists of the original 34K NL-FOL pairs. We validate FOL rules in terms of syntactical correctness, but we did not conduct a rigorous alignment check on the pairs,
meaning the FOL rule may not accurately reflect the meaning of the NL statement.
MALLS-v0.1 consists of 28K NL-FOL pairs that are filtered from v0. We manually checked the alignment for 1K samples and developed a filtering pipeline to filter
the main dataset.
# Dataset Structure
- The file `MALLS-v0.json` consists of the 34K unfiltered pairs of the MALLS-v0 dataset.
- The files `MALLS-v0.1-train.json` and `MALLS-v0.1-test.json` consist of the 27K auto-verified pairs and the 1K human-verified pairs.
- We also provide `folio_parsed.json` which consists of 2K pairs collected and processed from the FOLIO datset.
Each entry in the file is a dictionary object of the following format
```
{
'NL': <the NL statment>,
'FOL': <the FOL rule>
}
```
**License:**
Attribution-NonCommercial 4.0 International.
Since the data are collected from GPT-4, it also abides by the policy of OpenAI: https://openai.com/policies/terms-of-use
## Using the Dataset
We use MALLS to finetune LLaMA models for NL-FOL translation, namely LogicLLaMA, which achieves GPT-4 level performance.
**Project Page**
https://github.com/gblackout/LogicLLaMA
## Intended use
**Primary intended uses:**
MALLS is intended to be used for research.
## Citation
```
@article{yang2023harnessing,
title={Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation},
author={Yuan Yang and Siheng Xiong and Ali Payani and Ehsan Shareghi and Faramarz Fekri},
journal={arXiv preprint arXiv:2305.15541},
year={2023}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/IFlyTek-classification | C-MTEB | 2023-07-28T13:30:24Z | 248 | 1 | null | [
"region:us"
] | 2023-07-28T13:30:24Z | 2023-07-28T13:30:02.000Z | 2023-07-28T13:30:02 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
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- name: idx
dtype: int32
splits:
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num_bytes: 2105684
num_examples: 2600
- name: train
num_bytes: 10028605
num_examples: 12133
- name: validation
num_bytes: 2157119
num_examples: 2599
download_size: 9777643
dataset_size: 14291408
---
# Dataset Card for "IFlyTek-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
squarelike/OpenOrca-gugugo-ko | squarelike | 2023-11-12T22:41:06Z | 248 | 17 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:ko",
"license:mit",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] | 2023-11-12T22:41:06Z | 2023-11-04T05:57:56.000Z | 2023-11-04T05:57:56 | ---
language:
- ko
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
size_categories:
- 10M<n<100M
---
# **OpenOrca 한국어 번역 데이터셋**
[Gugugo-koen-7B-V1.1](https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1)을 이용하여 [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca)데이터셋을 번역하고 있습니다.
번역 진행상황은 아래를 참고해 주십시오.
## 진행상황
- GPT4 생성물 약 100만 개 중 약 64만 개 번역완료
- GPT3.5 생성물 약 350만 개 중 약 159만 개 번역완료
데이터셋 사용 후 출처표기는 제작자에게 큰 힘이 됩니다.
# Original dataset card: OpenOrca
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [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)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## Mistral-7B-OpenOrca
Our [latest model](https://huggingface.co/spaces/Open-Orca/Mistral-7B-OpenOrca), the first 7B to score better overall than all previous models below 30B.
98% of Llama2-70b-chat's performance, in a completely open 7B!
## OpenOrca-Platypus2-13B
Our [third model](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
higgsfield/hacker_news_prompt_completion | higgsfield | 2023-11-12T22:41:05Z | 248 | 1 | null | [
"region:us"
] | 2023-11-12T22:41:05Z | 2023-11-12T22:36:36.000Z | 2023-11-12T22:36:36 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 187231365
num_examples: 100000
download_size: 77649586
dataset_size: 187231365
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hacker_news_prompt_completion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
conceptofmind/sql_stack_clean_tokenized | conceptofmind | 2023-11-25T04:39:24Z | 248 | 0 | null | [
"region:us"
] | 2023-11-25T04:39:24Z | 2023-11-25T04:37:39.000Z | 2023-11-25T04:37:39 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 4965668400
num_examples: 484550
download_size: 1466991886
dataset_size: 4965668400
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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chintagunta85/bc4chemd | chintagunta85 | 2022-07-27T12:42:13Z | 247 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Hello-SimpleAI/HC3-Chinese | Hello-SimpleAI | 2023-01-21T13:11:49Z | 247 | 105 | null | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"ChatGPT",
"SimpleAI",
"Detection",
"OOD",
"arxiv:2301.07597",
"region:us"
] | 2023-01-21T13:11:49Z | 2023-01-18T14:20:45.000Z | 2023-01-18T14:20:45 | ---
task_categories:
- text-classification
- question-answering
- sentence-similarity
- zero-shot-classification
language:
- en
- zh
tags:
- ChatGPT
- SimpleAI
- Detection
- OOD
size_categories:
- 10K<n<100K
license: cc-by-sa-4.0
---
# Human ChatGPT Comparison Corpus (HC3)
We propose the first human-ChatGPT comparison corpus, named **HC3** dataset.
This dataset is introduced in our paper:
- Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597)
Code, models and analysis are available on our GitHub:
- GitHub: [**Chatgpt-Comparison-Detection project** 🔬](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)
# Dataset Copyright
If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same. If not, they follow CC-BY-SA license.
See [dataset copyright](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection#dataset-copyright).
# Citation
Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597)
```
@article{guo-etal-2023-hc3,
title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
author = "Guo, Biyang and
Zhang, Xin and
Wang, Ziyuan and
Jiang, Minqi and
Nie, Jinran and
Ding, Yuxuan and
Yue, Jianwei and
Wu, Yupeng",
journal={arXiv preprint arxiv:2301.07597}
year = "2023",
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
declare-lab/InstructEvalImpact | declare-lab | 2023-06-09T08:53:22Z | 247 | 6 | null | [
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] | 2023-06-09T08:53:22Z | 2023-06-09T07:05:44.000Z | 2023-06-09T07:05:44 | ---
license: apache-2.0
size_categories:
- n<1K
ArXiv: 2306.04757
---
# Project Links
# Dataset Description
The IMPACT dataset contains 50 human created prompts for each category, 200 in total, to test LLMs general writing ability.
Instructed LLMs demonstrate promising ability in writing-based tasks, such as composing letters or ethical debates. This dataset consists prompts across 4 diverse usage scenarios:
- **Informative Writing**: User queries such as self-help advice or explanations for various concept
- **Professional Writing**: Format involves suggestions presentations or emails in a business setting
- **Argumentative Writing**: Debate positions on ethical and societal question
- **Creative Writing**: Diverse writing formats such as stories, poems, and songs.
The IMPACT dataset is included in our [InstructEval Benchmark Suite](https://github.com/declare-lab/instruct-eval).
# Evaluation Results
We leverage ChatGPT to judge the quality of the generated answers by LLMs. In terms of:
- Relevance: how well the answer engages with the given prompt
- Coherence: general text quality such as organization and logical flow
Each answer is scored on a Likert scale from 1 to 5. We evaluate the models in the zero-shot
setting based on the given prompt and perform sampling-based decoding with a temperature of 1.0
| **Model** | **Size** | **Informative** | | **Professional** | | **Argumentative** | | **Creative** | | **Avg.** | |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| | | Rel. | Coh. | Rel. | Coh. | Rel. | Coh. | Rel. | Coh. | Rel. | Coh. |
| **ChatGPT** | - | 3.34 | 3.98 | 3.88 | 3.96 | 3.96 | 3.82 | 3.92 | 3.94 | 3.78 | 3.93 |
| [**Flan-Alpaca**](https://huggingface.co/declare-lab/flan-alpaca-xxl) | 11B | 3.56 | 3.46 | 3.54 | 3.70 | 3.22 | 3.28 | 3.70 | 3.40 | 3.51 | 3.46 |
| [**Dolly-V2**](https://huggingface.co/databricks/dolly-v2-12b) | 12 B | 3.54 | 3.64 | 2.96 | 3.74 | 3.66 | 3.20 | 3.02 | 3.18 | 3.30 | 3.44 |
| [**StableVicuna**](https://huggingface.co/TheBloke/stable-vicuna-13B-HF) | 13B | 3.54 | 3.64 | 2.96 | 3.74 | 3.30 | 3.20 | 3.02 | 3.18 | 3.21 | 3.44 |
| [**Flan-T5**](https://huggingface.co/google/flan-t5-xxl) | 11B | 2.64 | 3.24 | 2.62 | 3.22 | 2.54 | 3.40 | 2.50 | 2.72 | 2.58 | 3.15 |
# Citation
Please consider citing the following article if you found our work useful:
```
bibtex
@article{chia2023instructeval,
title={INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models},
author={Yew Ken Chia and Pengfei Hong and Lidong Bing and Soujanya Poria},
journal={arXiv preprint arXiv:2306.04757},
year={2023}
}
```
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C-MTEB/OCNLI | C-MTEB | 2023-07-28T07:10:28Z | 247 | 0 | null | [
"region:us"
] | 2023-07-28T07:10:28Z | 2023-07-28T07:10:23.000Z | 2023-07-28T07:10:23 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sent1
sequence: string
- name: sent2
sequence: string
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 222873
num_examples: 1
download_size: 153558
dataset_size: 222873
---
# Dataset Card for "OCNLI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hobeter/JJQA | hobeter | 2023-11-06T19:30:08Z | 247 | 1 | null | [
"task_categories:question-answering",
"size_categories:n<1K",
"license:apache-2.0",
"music",
"art",
"region:us"
] | 2023-11-06T19:30:08Z | 2023-11-06T18:55:51.000Z | 2023-11-06T18:55:51 | ---
dataset_info:
- config_name: qa
features:
- name: q
dtype: string
- name: a
dtype: string
- name: rf
dtype: string
- name: song_title
dtype: string
- name: song_id
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 67824
num_examples: 648
download_size: 134589
dataset_size: 67824
- config_name: song
features:
- name: id
dtype: string
- name: title
dtype: string
- name: name
dtype: string
- name: lyric
dtype: string
splits:
- name: train
num_bytes: 253605
num_examples: 181
download_size: 276024
dataset_size: 253605
- config_name: song_index
features:
- name: dic
dtype: string
splits:
- name: train
num_bytes: 2872
num_examples: 1
download_size: 4168
dataset_size: 2872
license: apache-2.0
task_categories:
- question-answering
tags:
- music
- art
size_categories:
- n<1K
---
## How to Start
```python
from datasets import load_dataset
import json
qas = load_dataset("hobeter/JJQA","qa")["train"]
songs = load_dataset("hobeter/JJQA","song")["train"]
song_index=json.loads(load_dataset("hobeter/JJQA","song_index")["train"]["dic"][0])[0]
```
# JJQA: a Chinese QA dataset on the lyrics of JJ Lin's songs
**GitHub: https://github.com/bebetterest/JJQA**
Large Language Models (LLMs) have shown powerful capability of text understanding, analysis and generation. It seems a good tool for text-style knowledge based question answering (QA) where semantically retrieving related texts, understanding them and generating correct answers are required.
However, many feasible QA datasets are not challenging enough. First, given text-style knowledge might be easy to perceive and analyse. Second, the questions & answers follow commonsense. Thus, LLMs may benefit from training of language modeling and even take a shortcut. In this case, we want to build a new text-style knowledge based logical QA dataset where the text-style knowledge is tricky and LLMs are not likely to give correct answers without successfully retrieving and reasoning related texts.
Chinese is a language where each single character could contain abundant meanings while just a few words, especially pieces of lyrics, are able to express complex conceptions, feelings and impressions. <font color=Purple size="">Besides, Junjie Lin, known as **[JJ Lin](https://www.jjlin.com/home/news/all)**, is a famous Singaporean Mandarin singer.</font> The lyrics of his songs are always imaginative, poetic and romantic.
Hense, we propose JJQA, a Chinese text-style knowledge based question answering dataset on the lyrics of JJ Lin's songs, where related lyrics are provided as text-style knowledge for retrieval while the questions and answers are based on the lyrics. The Q&As are always abstract and follow anti-commonsense. For example, according to the related lyrics of a song called "爱情Yogurt", the question is "热量有什么作用？" ("What is the impact of heat?") and the answer is "降低爱情的过敏反应。" ("Ease the anaphylaxis of love"). It is indeed ridiculous and funny (you could find more in the dataset)🤪. Even human beings could not give the right answer without knowing the related lyrics. In addition, LLMs are not likely to naturally generate the right answer with the capability from training. Therefore, only if the related lyrics are retrieved and understood, are the right answers possibly generated By LLMs.
## Dataset Details
According to [QQMusicSpider](https://github.com/yangjianxin1/QQMusicSpider), we crawled lyrics of all songs of JJ Lin from [QQMusic](https://y.qq.com/). After data cleaning and label annotation, 648 Q&As with 181 related song lyrics are included.
Three fields ("qa", "song", "song_index") are included in JJQA.
"qa" contains Q&As with 6 features. "q" and "a" are a question and the corresponding answer. "song_title" and "song_id" are the title and the corresponding id of the related song. "id" is the id for the Q&A. "rf" locates the lines of lyrics for reference, splited by a space " ".
"song" contains information of songs with 4 features. "title" and "name" are the title and the corresponding name of the song. "id" is the id of the song. "lyric" is the lyrics of the song, where each line is splited by "\n".
"song_index" contains one dictionary, whose keys are the ids of songs and values are indexes of the corresponding song in "song" field, to align QAs with the corresponding songs.
## Baselines
We evaluate three baseline methods on JJQA. The first one (*wo_info*) is to "ask" the question directly without any additional lyric, which is to show the performance of uninformed LLMs; the second one (*w_song*) is to include whole lyrics of the related song as in-contexts; the third one (*w_rf*) is to just include related lyrics. *w_song* and *w_rf* are two reference lines for retrieval-based method.
Six feasible LLMs (*ernie-turbo*, *chatglm2_6b_32k*, *qwen-turbo*, *baichuan2-7b-chat-v1*, *gpt-4*, *gpt-3.5-turbo*) are included. We apply *ernie-turbo* and *chatglm2_6b_32k* in [qianfan platform](https://cloud.baidu.com/product/wenxinworkshop); *qwen-turbo* and *baichuan2-7b-chat-v1* in [dashscope platform](https://dashscope.aliyun.com/); *gpt-4* and *gpt-3.5-turbo* in [openai platform](https://platform.openai.com/).
We consider [BERTScore](https://github.com/Tiiiger/bert_score) with *rescale_with_baseline=True* as the metric.
The results are as follows.
|LLM|Method|Precision|Recall|F1|Date|
|:---:|:---:|:---:|:---:|:---:|:---:|
|*ernie-turbo*|*wo_info*|-0.0350|0.1568|0.0511|2023/11/06|
|*ernie-turbo*|*w_song*|0.2472|0.5765|0.3895|2023/11/06|
|*ernie-turbo*|*w_rf*|0.3600|0.6528|0.4864|2023/11/06|2023/11/05|
|*chatglm2_6b_32k*|*wo_info*|0.0466|0.1787|0.1066|2023/11/05|
|*chatglm2_6b_32k*|*w_song*|0.2361|0.4606|0.3335|2023/11/05|
|*chatglm2_6b_32k*|*w_rf*|0.4650|0.6477|0.5436|2023/11/05|
|*qwen-turbo*|*wo_info*|0.2331|0.2150|0.2208|2023/11/05|
|*qwen-turbo*|*w_song*|0.7673|0.8041|0.7804|2023/11/05|
|*qwen-turbo*|*w_rf*|0.8600|0.8251|0.8386|2023/11/05|
|*baichuan2-7b-chat-v1*|*wo_info*|0.1755|0.2012|0.1857|2023/11/05|
|*baichuan2-7b-chat-v1*|*w_song*|0.4635|0.6324|0.5371|2023/11/05|
|*baichuan2-7b-chat-v1*|*w_rf*|0.6567|0.7272|0.6851|2023/11/05|
|*gpt-3.5-turbo*|*wo_info*|0.2201|0.1983|0.2061|2023/11/06|
|*gpt-3.5-turbo*|*w_song*|0.8031|0.7812|0.7884|2023/11/06|
|*gpt-3.5-turbo*|*w_rf*|0.8110|0.7484|0.7758|2023/11/06|
|*gpt-4*|*wo_info*|0.2426|0.2377|0.2376|2023/11/06|
|*gpt-4*|*w_song*|0.8405|0.8587|0.8464|2023/11/06|
|*gpt-4*|*w_rf*|0.8865|0.8643|0.8732|2023/11/06|
It is worth noting that *Date* stands for the time (UTC+8) for evaluation. In addition, a small number of samples are not feasible in the dashscope platform because of its safety system. We just skip these Q&As. (1 sample for *qwen-turbo* *wo_info*; 3 samples for *qwen-turbo* *w_song*; 3 samples for *baichuan2-7b-chat-v1* *w_song*)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allenai/scifact_entailment | allenai | 2023-09-27T05:00:41Z | 246 | 0 | scifact | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-2.0",
"region:us"
] | 2023-09-27T05:00:41Z | 2023-09-26T22:04:02.000Z | 2023-09-26T22:04:02 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-2.0
multilinguality:
- monolingual
pretty_name: SciFact
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: scifact
dataset_info:
features:
- name: claim_id
dtype: int32
- name: claim
dtype: string
- name: abstract_id
dtype: int32
- name: title
dtype: string
- name: abstract
sequence: string
- name: verdict
dtype: string
- name: evidence
sequence: int32
splits:
- name: train
num_bytes: 1649655
num_examples: 919
- name: validation
num_bytes: 605262
num_examples: 340
download_size: 3115079
dataset_size: 2254917
---
# Dataset Card for "scifact_entailment"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Description
- **Homepage:** [https://scifact.apps.allenai.org/](https://scifact.apps.allenai.org/)
- **Repository:** <https://github.com/allenai/scifact>
- **Paper:** [Fact or Fiction: Verifying Scientific Claims](https://aclanthology.org/2020.emnlp-main.609/)
- **Point of Contact:** [David Wadden](mailto:davidw@allenai.org)
### Dataset Summary
SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
For more information on the dataset, see [allenai/scifact](https://huggingface.co/datasets/allenai/scifact).
This has the same data, but reformatted as an entailment task. A single instance includes a claim paired with a paper title and abstract, together with an entailment label and a list of evidence sentences (if any).
## Dataset Structure
### Data fields
- `claim_id`: An `int32` claim identifier.
- `claim`: A `string`.
- `abstract_id`: An `int32` abstract identifier.
- `title`: A `string`.
- `abstract`: A list of `strings`, one for each sentence in the abstract.
- `verdict`: The fact-checking verdict, a `string`.
- `evidence`: A list of sentences from the abstract which provide evidence for the verdict.
### Data Splits
| |train|validation|
|------|----:|---------:|
|claims| 919 | 340|
| [
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bongsoo/news_talk_en_ko | bongsoo | 2022-10-05T00:09:50Z | 245 | 4 | null | [
"language:ko",
"license:apache-2.0",
"region:us"
] | 2022-10-05T00:09:50Z | 2022-09-20T05:10:56.000Z | 2022-09-20T05:10:56 | ---
language:
- ko
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
liuhaotian/LLaVA-Instruct-150K | liuhaotian | 2023-10-06T22:18:34Z | 245 | 194 | null | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-10-06T22:18:34Z | 2023-04-17T23:47:27.000Z | 2023-04-17T23:47:27 | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
- question-answering
language:
- en
pretty_name: LLaVA Visual Instruct 150K
size_categories:
- 100K<n<1M
---
# LLaVA Visual Instruct 150K Dataset Card
## Dataset details
**Dataset type:**
LLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data.
It is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability.
**Dataset date:**
LLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API.
**Paper or resources for more information:**
https://llava-vl.github.io/
**License:**
Attribution-NonCommercial 4.0 International
It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. | [
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svakulenk0/qrecc | svakulenk0 | 2022-07-02T17:35:21Z | 244 | 8 | null | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|natural_questions",
"source_datasets:extended|quac",
"language:en",
"license:cc-by-3.0",
"arxiv:2010.04898",
"region:us"
] | 2022-07-02T17:35:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: QReCC
language_creators:
- expert-generated
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
source_datasets:
- extended|natural_questions
- extended|quac
task_categories:
- question-answering
task_ids:
- open-domain-qa
---
# Dataset Card for QReCC: Question Rewriting in Conversational Context
## 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 Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- [**Repository:**](https://github.com/apple/ml-qrecc)
- [**Paper:**](https://arxiv.org/pdf/2010.04898.pdf)
- [**Leaderboard:**](https://www.tira.io/task/scai-qrecc/dataset/scai-qrecc21-test-dataset-2021-07-20)
### Dataset Summary
QReCC (Question Rewriting in Conversational Context) is an end-to-end open-domain question answering dataset comprising of 14K conversations with 81K question-answer pairs. The goal of this dataset is to provide a challenging benchmark for end-to-end conversational question answering that includes the individual subtasks of question rewriting, passage retrieval and reading comprehension.
The task in QReCC is to find answers to conversational questions within a collection of 10M web pages split into 54M passages. Answers to questions in the same conversation may be distributed across several web pages.
The passage collection should be downloaded from [**Zenodo**](https://zenodo.org/record/5115890#.YaeD7C8RppR) (passages.zip)
### Supported Tasks and Leaderboards
`question-answering`
### Languages
English
## Dataset Structure
### Data Instances
An example from the data set looks as follows:
```
{
"Context": [
"What are the pros and cons of electric cars?",
"Some pros are: They're easier on the environment. Electricity is cheaper than gasoline. Maintenance is less frequent and less expensive. They're very quiet. You'll get tax credits. They can shorten your commute time. Some cons are: Most EVs have pretty short ranges. Recharging can take a while."
],
"Question": "Tell me more about Tesla",
"Rewrite": "Tell me more about Tesla the car company.",
"Answer": "Tesla Inc. is an American automotive and energy company based in Palo Alto, California. The company specializes in electric car manufacturing and, through its SolarCity subsidiary, solar panel manufacturing.",
"Answer_URL": "https://en.wikipedia.org/wiki/Tesla,_Inc.",
"Conversation_no": 74,
"Turn_no": 2,
"Conversation_source": "trec"
}
```
### Data Splits
- train: 63501
- test: 16451
## Dataset Creation
### Source Data
- QuAC
- TREC CAsT
- Natural Questions
## Additional Information
### Licensing Information
[CC BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/)
### Citation Information
```
@inproceedings{ qrecc,
title={Open-Domain Question Answering Goes Conversational via Question Rewriting},
author={Anantha, Raviteja and Vakulenko, Svitlana and Tu, Zhucheng and Longpre, Shayne and Pulman, Stephen and Chappidi, Srinivas},
booktitle={ NAACL },
year={2021}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/AFQMC | C-MTEB | 2023-07-28T13:39:01Z | 244 | 0 | null | [
"region:us"
] | 2023-07-28T13:39:01Z | 2023-07-28T13:38:46.000Z | 2023-07-28T13:38:46 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: idx
dtype: int32
splits:
- name: test
num_bytes: 378718
num_examples: 3861
- name: train
num_bytes: 3396503
num_examples: 34334
- name: validation
num_bytes: 426285
num_examples: 4316
download_size: 2337418
dataset_size: 4201506
---
# Dataset Card for "AFQMC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shossain/merged-no-pad-32768 | shossain | 2023-11-06T22:41:30Z | 244 | 0 | null | [
"region:us"
] | 2023-11-06T22:41:30Z | 2023-11-06T22:40:47.000Z | 2023-11-06T22:40:47 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1226023649
num_examples: 3036
download_size: 337654761
dataset_size: 1226023649
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "merged-no-pad-32768"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HuggingFaceH4/self-instruct-seed | HuggingFaceH4 | 2023-01-31T22:37:02Z | 243 | 14 | null | [
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"arxiv:2212.10560",
"region:us"
] | 2023-01-31T22:37:02Z | 2023-01-31T22:33:52.000Z | 2023-01-31T22:33:52 | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
size_categories:
- n<1K
---
Manually created seed dataset used in bootstrapping in the Self-instruct paper https://arxiv.org/abs/2212.10560. This is part of the instruction fine-tuning datasets. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/JDReview-classification | C-MTEB | 2023-07-28T13:18:58Z | 243 | 1 | null | [
"region:us"
] | 2023-07-28T13:18:58Z | 2023-07-28T13:18:46.000Z | 2023-07-28T13:18:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int32
- name: domain
dtype: string
- name: label
dtype:
class_label:
names:
'0': POS
'1': NEG
- name: rank
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 680681
num_examples: 3729
- name: test
num_bytes: 94649
num_examples: 533
download_size: 361291
dataset_size: 775330
---
# Dataset Card for "JDReview-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/178c9698 | result-kand2-sdxl-wuerst-karlo | 2023-11-14T08:52:35Z | 243 | 0 | null | [
"region:us"
] | 2023-11-14T08:52:35Z | 2023-11-14T08:52:34.000Z | 2023-11-14T08:52:34 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 187
num_examples: 10
download_size: 1352
dataset_size: 187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "178c9698"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
aquamuse | null | 2022-11-18T18:21:11Z | 242 | 8 | aquamuse | [
"task_categories:other",
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|natural_questions",
"source_datasets:extended|other-Common-Crawl",
"source_datasets:original",
"language:en",
"license:unknown",
"query-based-multi-document-summarization",
"arxiv:2010.12694",
"region:us"
] | 2022-11-18T18:21:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|natural_questions
- extended|other-Common-Crawl
- original
task_categories:
- other
- question-answering
- text2text-generation
task_ids:
- abstractive-qa
- extractive-qa
paperswithcode_id: aquamuse
pretty_name: AQuaMuSe
tags:
- query-based-multi-document-summarization
dataset_info:
- config_name: abstractive
features:
- name: query
dtype: string
- name: input_urls
sequence: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 6434909
num_examples: 6253
- name: test
num_bytes: 843181
num_examples: 811
- name: validation
num_bytes: 689109
num_examples: 661
download_size: 7755161
dataset_size: 7967199
- config_name: extractive
features:
- name: query
dtype: string
- name: input_urls
sequence: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 6434909
num_examples: 6253
- name: test
num_bytes: 843181
num_examples: 811
- name: validation
num_bytes: 689109
num_examples: 661
download_size: 7755161
dataset_size: 7967199
---
# Dataset Card for AQuaMuSe
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/google-research-datasets/aquamuse
- **Repository:** https://github.com/google-research-datasets/aquamuse
- **Paper:** https://arxiv.org/pdf/2010.12694.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)
This dataset contains versions of automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf).
### Supported Tasks and Leaderboards
- **Abstractive** and **Extractive** query-based multi-document summarization
- Question Answering
### Languages
en : English
## Dataset Structure
### Data Instances
- `input_urls`: a `list` of `string` features.
- `query`: a `string` feature.
- `target`: a `string` feature
Example:
```
{
'input_urls': ['https://boxofficebuz.com/person/19653-charles-michael-davis'],
'query': 'who is the actor that plays marcel on the originals',
'target': "In February 2013, it was announced that Davis was cast in a lead role on The CW's new show The
Originals, a spinoff of The Vampire Diaries, centered on the Original Family as they move to New Orleans, where
Davis' character (a vampire named Marcel) currently rules."
}
```
### Data Fields
- `input_urls`: a `list` of `string` features.
- List of URLs to input documents pointing to [Common Crawl](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available) to be summarized.
- Dependencies: Documents URLs references the [Common Crawl June 2017 Archive](https://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available).
- `query`: a `string` feature.
- Input query to be used as summarization context. This is derived from [Natural Questions](https://ai.google.com/research/NaturalQuestions/) user queries.
- `target`: a `string` feature
- Summarization target, derived from [Natural Questions](https://ai.google.com/research/NaturalQuestions/) long answers.
### Data Splits
- This dataset has two high-level configurations `abstractive` and `extractive`
- Each configuration has the data splits of `train`, `dev` and `test`
- The original format of the data was in [TFrecords](https://www.tensorflow.org/tutorials/load_data/tfrecord), which has been parsed to the format as specified in [Data Instances](#data-instances)
## Dataset Creation
### Curation Rationale
The dataset is automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in [AQuaMuSe paper](https://arxiv.org/pdf/2010.12694.pdf).
### 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
The dataset curator is [sayalikulkarni](https://github.com/google-research-datasets/aquamuse/commits?author=sayalikulkarni), who is the contributor for the official GitHub repository for this dataset and also one of the authors of this dataset’s paper. As the account handles of other authors are not available currently who were also part of the curation of this dataset, the authors of the paper are mentioned here as follows, Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, and Eugene Ie.
### Licensing Information
[More Information Needed]
### Citation Information
@misc{kulkarni2020aquamuse,
title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},
author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
year={2020},
eprint={2010.12694},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset. | [
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nlphuji/whoops | nlphuji | 2023-08-18T23:06:45Z | 242 | 12 | whoops | [
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"commonsense-reasoning",
"explanation-generation",
"visual-commonsense-reasoning",
"compositionality",
"image-generation",
"visual-question-answering(VQA)",
"question-answering",
"image-captioning",
"arxiv:2303.07274",
"region:us"
] | 2023-08-18T23:06:45Z | 2023-01-28T22:04:03.000Z | 2023-01-28T22:04:03 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
paperswithcode_id: whoops
pretty_name: WHOOPS!
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- commonsense-reasoning
- explanation-generation
- visual-commonsense-reasoning
- compositionality
- image-generation
- visual-question-answering(VQA)
- question-answering
- image-captioning
task_ids: []
# dataset files.
extra_gated_prompt: >-
# By clicking “Access repository“ below, you assert your intention to exclusively use this resource for research, not for commercial chatbot development, and agree to abide by the terms detailed in the [WHOOPS! license](https://whoops-benchmark.github.io/static/pdfs/whoops_license_agreement.txt). You may also view all instances through the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and consult the accompanying [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md) prior to acceptance. If you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@gmail.com.
By clicking “Access repository” below, you confirm your understanding that for commercial models, this resource is permitted for use as a test set, but not as a training set. Please ensure adherence to the terms detailed in the [WHOOPS! license](https://whoops-benchmark.github.io/static/pdfs/whoops_license_agreement.txt). You may view all instances via the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and refer to the [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md) prior to acceptance. If you are unsure about your specific case, don't hesitate to contact: yonatanbitton1@gmail.com.
---
# Dataset Card for WHOOPS!
- [Dataset Description](#dataset-description)
- [Contribute Images to Extend WHOOPS!](#contribute-images-to-extend-whoops)
- [Languages](#languages)
- [Dataset](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Data Loading](#data-loading)
- [Licensing Information](#licensing-information)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Citation Information](#citation-information)
## Dataset Description
WHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge.
The WHOOPS! benchmark includes four tasks:
1. A novel task of explanation-of-violation: generating a detailed explanation for what makes the image weird.
2. Generating a literal caption
3. Distinguishing between detailed and underspecified captions
4. Answering questions that test compositional understanding
The results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!.
* Homepage: https://whoops-benchmark.github.io/
* Paper: https://arxiv.org/pdf/2303.07274.pdf
* WHOOPS! Explorer: https://huggingface.co/spaces/nlphuji/whoops-explorer-full
* Normal vs. Wired Explorer: https://huggingface.co/spaces/nlphuji/whoops-explorer-analysis
* Point of Contact: yonatanbitton1@gmail.com
[//]: # (Colab notebook code for WHOOPS evaluation )
## Contribute Images to Extend WHOOPS!
Would you like to add a commonsense-defying image to our database? Please send candidate images to yonatanbitton1@gmail.com. Thanks!
### Languages
English.
## Dataset
### Data Fields
image (image) - The weird image.
designer_explanation (string) - Detailed single-sentence explanation given by the designer, explaining why the image is weird.
selected_caption (string) - The caption that was selected from the crowed collected captions.
crowd_captions (list) - Crowd collected captions, depicting whats been seen in the image.
crowd_explanations (list) - Crowd collected single-sentence explanations, explaining why the image is weird.
crowd_underspecified_captions (list) - Crowd collected under-specified captions, depicting what is seen in the image, without depicting the commonsense-violation.
question_answering_pairs (list) - Automatically generated Q-A pairs. FlanT5 XL was used to answer the questions and filter out instances where the BEM metric is above 0.1.
commonsense_category (string) - The commonsense category the images related to (Full categories list can be found in [paper](https://arxiv.org/pdf/2303.07274.pdf)).
image_id (string)- The unique id of the image in the dataset
image_designer (string) - The name of the image designer.
### Data Splits
There is a single TEST split.
Although primarily intended as a challenging test set, we trained on the WHOOPS! dataset to demonstrate the value of the data and to create a better model.
We will provide the splits in the future.
### Data Loading
You can load the data as follows (credit to [Winoground](https://huggingface.co/datasets/facebook/winoground)):
```
from datasets import load_dataset
examples = load_dataset('nlphuji/whoops', use_auth_token=<YOUR USER ACCESS TOKEN>)
```
You can get `<YOUR USER ACCESS TOKEN>` by following these steps:
1) log into your Hugging Face account
2) click on your profile picture
3) click "Settings"
4) click "Access Tokens"
5) generate an access token
## Licensing Information
[CC-By 4.0](https://creativecommons.org/licenses/by/4.0/)
Additional license information: [license_agreement.txt](https://huggingface.co/datasets/nlphuji/whoops/blob/main/license_agreement.txt)
You may also view all instances through the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and consult the accompanying [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md).
1. **Purpose:** The dataset was primarily designed for use as a test set.
2. **Commercial Use:** Commercially, the dataset may be used as a test set, but it's prohibited to use it as a training set.
3. **Rights on Images:** All rights to the images within the dataset are retained by the WHOOPS! authors.
If you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@gmail.com.
[//]: # (To evaluate WHOOPS! with a fine-tune BLIP2, we split the images in WHOOPS! into 5 cross- validation splits. For these 5 splits independently, we train supervised models using 60% of the data as training, 20% as validation, and 20% for test.)
## Annotations
We paid designers to create images, and supply explanations for what is making the image wierd.
We paid Amazon Mechanical Turk Workers to supply explanations, captions and under-specified captions for each image in our dataset.
## Considerations for Using the Data
We took measures to filter out potentially harmful or offensive images and texts in WHOOPS!, but it is still possible that some individuals may find certain content objectionable.
If you come across any instances of harm, please report them to our point of contact. We will review and eliminate any images from the dataset that are deemed harmful.
[//]: # (All images, explanations, captions and under-specified captions were obtained with human annotators.)
### Citation Information
@article{bitton2023breaking,
title={Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images},
author={Bitton-Guetta, Nitzan and Bitton, Yonatan and Hessel, Jack and Schmidt, Ludwig and Elovici, Yuval and Stanovsky, Gabriel and Schwartz, Roy},
journal={arXiv preprint arXiv:2303.07274},
year={2023}
} | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dream-textures/textures-color-normal-1k | dream-textures | 2023-01-13T21:20:22Z | 241 | 7 | null | [
"task_categories:image-to-image",
"size_categories:1K<n<10K",
"license:cc0-1.0",
"region:us"
] | 2023-01-13T21:20:22Z | 2023-01-13T21:14:42.000Z | 2023-01-13T21:14:42 | ---
dataset_info:
features:
- name: color
dtype: image
- name: normal
dtype: image
splits:
- name: train
num_bytes: 110631687.194
num_examples: 1426
download_size: 111043422
dataset_size: 110631687.194
license: cc0-1.0
task_categories:
- image-to-image
size_categories:
- 1K<n<10K
---
# textures-color-normal-1k
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The `textures-color-normal-1k` dataset is an image dataset of 1000+ color and normal map textures in 512x512 resolution.
The dataset was created for use in image to image tasks.
It contains a combination of CC0 procedural and photoscanned PBR materials from [ambientCG](https://ambientcg.com/).
## Dataset Structure
### Data Instances
Each data point contains a 512x512 color texture and the corresponding 512x512 normal map.
### Data Fields
* `color`: the color texture as a PIL image
* `normal`: the normal map as a PIL image
### Data Splits
| | train |
| -- | ----- |
| ambientCG | 1426 |
## Dataset Creation
### Curation Rationale
`textures-color-normal-1k` was created to provide an accesible source of data for automating 3D-asset creation workflows.
The [Dream Textures](https://github.com/carson-katri/dream-textures) add-on is one such tool providing AI automation in Blender.
By training models designed for image to image tasks, this particular use-case can be more accurately automated.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained from [ambientCG](https://ambientcg.com/)'s CC0 textures. Only the color and normal maps were included in this dataset.
## Additional Information
### Dataset Curators
The dataset was created by Carson Katri, with the images being provided by [ambientCG](https://ambientcg.com/).
### Licensing Information
All of the images used in this dataset are CC0.
### Citation Information
[N/A]
### Contributions
Thanks to [@carson-katri](https://github.com/carson-katri) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/ThuNewsClusteringP2P | C-MTEB | 2023-07-27T17:29:09Z | 241 | 0 | null | [
"region:us"
] | 2023-07-27T17:29:09Z | 2023-07-27T17:28:47.000Z | 2023-07-27T17:28:47 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 31552896
num_examples: 10
download_size: 23299710
dataset_size: 31552896
---
# Dataset Card for "ThuNewsClusteringP2P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
griffin/chain_of_density | griffin | 2023-09-08T00:43:00Z | 241 | 51 | null | [
"region:us"
] | 2023-09-08T00:43:00Z | 2023-09-08T00:42:55.000Z | 2023-09-08T00:42:55 | ---
dataset_info:
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splits:
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splits:
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configs:
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data_files:
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path: annotated/test-*
- config_name: unannotated
data_files:
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path: unannotated/train-*
---
# Dataset Card for "chain_of_density"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pvisnrt/mod_capstone | pvisnrt | 2023-11-02T20:50:53Z | 241 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-02T20:50:53Z | 2023-11-02T20:50:30.000Z | 2023-11-02T20:50:30 | ---
license: mit
dataset_info:
features:
- name: source
sequence: string
- name: summary_target
sequence: string
- name: tags
sequence:
class_label:
names:
'0': C
'1': M
'2': N
'3': O
'4': OB
'5': W
splits:
- name: train
num_bytes: 133158
num_examples: 80
- name: validation
num_bytes: 15058
num_examples: 10
- name: test
num_bytes: 22841
num_examples: 10
download_size: 50563
dataset_size: 171057
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/suc3-mini | ScandEval | 2023-07-05T09:42:05Z | 240 | 0 | null | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | 2023-07-05T09:42:05Z | 2022-06-14T18:21:45.000Z | 2022-06-14T18:21:45 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 344855
num_examples: 1024
- name: test
num_bytes: 681936
num_examples: 2048
- name: val
num_bytes: 81547
num_examples: 256
download_size: 509020
dataset_size: 1108338
license: cc-by-4.0
task_categories:
- token-classification
language:
- sv
size_categories:
- 1K<n<10K
---
# Dataset Card for "suc3-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ChristophSchuhmann/improved_aesthetics_6plus | ChristophSchuhmann | 2022-08-10T11:30:40Z | 240 | 24 | null | [
"region:us"
] | 2022-08-10T11:30:40Z | 2022-08-10T11:29:49.000Z | 2022-08-10T11:29:49 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
THUIR/T2Ranking | THUIR | 2023-07-03T08:46:32Z | 240 | 14 | null | [
"task_categories:text-retrieval",
"size_categories:1M<n<10M",
"language:zh",
"license:apache-2.0",
"arxiv:2304.03679",
"region:us"
] | 2023-07-03T08:46:32Z | 2023-04-04T07:15:08.000Z | 2023-04-04T07:15:08 | ---
license: apache-2.0
task_categories:
- text-retrieval
language:
- zh
size_categories:
- 1M<n<10M
---
# T<sup>2</sup>Ranking
## Introduction
T<sup>2</sup>Ranking is a large-scale Chinese benchmark for passage ranking. The details about T<sup>2</sup>Ranking are elaborated in [this paper](https://arxiv.org/abs/2304.03679#).
Passage ranking are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). The goal of passage ranking is to compile a search result list ordered in terms of relevance to the query from a large passage collection. Typically, Passage ranking involves two stages: passage retrieval and passage re-ranking.
To support the passage ranking research, various benchmark datasets are constructed. However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues.
To address this problem, we introduce T<sup>2</sup>Ranking, a large-scale Chinese benchmark for passage ranking. T<sup>2</sup>Ranking comprises more than 300K queries and over 2M unique passages from real- world search engines. Specifically, we sample question-based search queries from user logs of the Sogou search engine, a popular search system in China. For each query, we extract the content of corresponding documents from different search engines. After model-based passage segmentation and clustering-based passage de-duplication, a large-scale passage corpus is obtained. For a given query and its corresponding passages, we hire expert annotators to provide 4-level relevance judgments of each query-passage pair.
<div align=center><img width="600" height="200" src="https://github.com/THUIR/T2Ranking/blob/main/pic/stat.png?raw=true"/></div>
<div align=center>Table 1: The data statistics of datasets commonly used in passage ranking. FR(SR): First (Second)- stage of passage ranking, i.e., passage Retrieval (Re-ranking).</div>
Compared with existing datasets, T<sup>2</sup>Ranking dataset has the following characteristics and advantages:
* The proposed dataset focus on the Chinese search scenario, and has advantages in data scale compared with existing Chinese passage ranking datasets, which can better support the design of deep learning algorithms
* The proposed dataset has a large number of fine-grained relevance annotations, which is helpful for mining fine-grained relationship between queries and passages and constructing more accurate ranking algorithms.
* By retrieving passage results from multiple commercial search engines and providing complete annotation, we ease the false negative problem to some extent, which is beneficial to providing more accurate evaluation.
* We design multiple strategies to ensure the high quality of our dataset, such as using a passage segment model and a passage clustering model to enhance the semantic integrity and diversity of passages and employing active learning for annotation method to improve the efficiency and quality of data annotation.
## Data Download
The whole dataset is placed in [huggingface](https://huggingface.co/datasets/THUIR/T2Ranking), and the data formats are presented in the following table.
<div class="center">
| Description| Filename|Num Records|Format|
|-------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|----------:|-----------------------------------:|
| Collection | collection.tsv | 2,303,643 | tsv: pid, passage |
| Queries Train | queries.train.tsv | 258,042 | tsv: qid, query |
| Queries Dev | queries.dev.tsv | 24,832 | tsv: qid, query |
| Queries Test | queries.test.tsv | 24,832 | tsv: qid, query |
| Qrels Train for re-ranking | qrels.train.tsv | 1,613,421 | TREC qrels format |
| Qrels Dev for re-ranking | qrels.dev.tsv | 400,536 | TREC qrels format |
| Qrels Retrieval Train | qrels.retrieval.train.tsv | 744,663 | tsv: qid, pid |
| Qrels Retrieval Dev | qrels.retrieval.dev.tsv | 118,933 | tsv: qid, pid |
| BM25 Negatives | train.bm25.tsv | 200,359,731 | tsv: qid, pid, index |
| Hard Negatives | train.mined.tsv | 200,376,001 | tsv: qid, pid, index, score |
</div>
You can download the dataset by running the following command:
```bash
git lfs install
git clone https://huggingface.co/datasets/THUIR/T2Ranking
```
After downloading, you can find the following files in the folder:
```
├── data
│ ├── collection.tsv
│ ├── qrels.dev.tsv
│ ├── qrels.retrieval.dev.tsv
│ ├── qrels.retrieval.train.tsv
│ ├── qrels.train.tsv
│ ├── queries.dev.tsv
│ ├── queries.test.tsv
│ ├── queries.train.tsv
│ ├── train.bm25.tsv
│ └── train.mined.tsv
├── script
│ ├── train_cross_encoder.sh
│ └── train_dual_encoder.sh
└── src
├── convert2trec.py
├── dataset_factory.py
├── modeling.py
├── msmarco_eval.py
├── train_cross_encoder.py
├── train_dual_encoder.py
└── utils.py
```
## Training and Evaluation
The dual-encoder can be trained by running the following command:
```bash
sh script/train_dual_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.top1000.step20
```
The cross-encoder can be trained by running the following command:
```bash
sh script/train_cross_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/convert2trec.py output/res.step-20 && python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.step-20.trec && path_to/trec_eval -m ndcg_cut.5 data/qrels.dev.tsv res.step-20.trec
```
BM25 on DEV set
```bash
#####################
MRR @10: 0.35894801237316354
QueriesRanked: 24831
recall@1: 0.05098711868967141
recall@1000: 0.7464097131133757
recall@50: 0.4942572226146033
#####################
```
DPR w/o hard negatives on DEV set
```bash
#####################
MRR @10: 0.4856112079562753
QueriesRanked: 24831
recall@1: 0.07367235058688999
recall@1000: 0.9082753169878586
recall@50: 0.7099350889583964
#####################
```
DPR w/ hard negatives on DEV set
```bash
#####################
MRR @10: 0.5166915171959451
QueriesRanked: 24831
recall@1: 0.08047455688965123
recall@1000: 0.9135220125786163
recall@50: 0.7327044025157232
#####################
```
BM25 retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5188107959009376
QueriesRanked: 24831
recall@1: 0.08545219116806242
recall@1000: 0.7464097131133757
recall@50: 0.595298153566744
#####################
ndcg_cut_20 all 0.4405
ndcg_cut_100 all 0.4705
#####################
```
DPR retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5508822816845231
QueriesRanked: 24831
recall@1: 0.08903406988867588
recall@1000: 0.9135220125786163
recall@50: 0.7393720781623112
#####################
ndcg_cut_20 all 0.5131
ndcg_cut_100 all 0.5564
#####################
```
## License
The dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
## Citation
If you use this dataset in your research, please cite our paper:
```
@misc{xie2023t2ranking,
title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},
year={2023},
eprint={2304.03679},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
selfrag/selfrag_train_data | selfrag | 2023-10-31T19:37:22Z | 240 | 25 | null | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:2310.11511",
"region:us"
] | 2023-10-31T19:37:22Z | 2023-10-18T19:55:39.000Z | 2023-10-18T19:55:39 | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
This is a training data file for [Self-RAG](https://selfrag.github.io/) that generates outputs to diverse user queries as well as reflection tokens to call the retrieval system adaptively and criticize its own output and retrieved passages.
Self-RAG is trained on our 150k diverse instruction-output pairs with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback.
At inference, we leverage reflection tokens covering diverse aspects of generations to sample the best output aligning users' preferences. See full descriptions in [our paper](https://arxiv.org/abs/2310.11511) and [code](https://github.com/AkariAsai/self-rag).
## Citation and contact
If you use this model, please cite our work:
```
@article{asai2023selfrag,
author = {Asai, Akari and Wu, Zeqiu and Wang, Yizhong and Sil, Avirup and Hajishirzi, Hannaneh},
title = {{Self-RAG}: Learning to Retrieve, Generate, and Critique through Self-Reflection},
year = {2023},
journal = { arXiv preprint arXiv:2310.11511 },
URL = {https://arxiv.org/abs/2310.11511}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kyujinpy/OpenOrca-ko-v2 | kyujinpy | 2023-10-28T19:58:34Z | 240 | 0 | null | [
"license:cc-by-nc-4.0",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] | 2023-10-28T19:58:34Z | 2023-10-28T19:52:37.000Z | 2023-10-28T19:52:37 | ---
license: cc-by-nc-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 41592589
num_examples: 19468
download_size: 21611641
dataset_size: 41592589
---
## OpenOrca-Ko-v2
1. NIV // 약 1500개
2. FLAN // 약 9000개
3. T0 // 약 6000개
4. CoT // 약 2000개
> Dataset 구성
- 수작업으로 고친 내용(v2)
1. 영어로 된 답변 수정. (Ex. Nick -> 닉, Lucky -> 운이 좋음, ...)
2. KoCoT 데이터셋 제거.
3. Yes, True, False 등등 일부 답변 수정
> Post-processing 작업 내용
## Translation
Using DeepL Pro API. Thanks.
---
>Below is original dataset card
## Table of Contents
- [Dataset Summary](#dataset-summary)
- [Dataset Attribution](#dataset-attribution)
- [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)
- [Dataset Use](#dataset-use)
- [Use Cases](#use-cases)
- [Usage Caveats](#usage-caveats)
- [Getting Started](#getting-started)
<p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p>
<a name="dataset-announcement"></a>
We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707).
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!
# Official Models
## OpenOrca-Platypus2-13B
Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard!
Released in partnership with Platypus.
## LlongOrca 7B & 13B
* Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance.
* [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance.
## OpenOrcaxOpenChat-Preview2-13B
Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper.
Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B.
Released in partnership with OpenChat.
## OpenOrca-Preview1-13B
[OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B)
This model was trained in less than a day, for <$200, with <10% of our data.
At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper.
<a name="dataset-summary"></a>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-attribution"></a>
# Dataset Attribution
We would like to give special recognition to the following contributors for their significant efforts and dedication:
Teknium
WingLian/Caseus
Eric Hartford
NanoBit
Pankaj
Winddude
Rohan
http://AlignmentLab.ai:
Autometa
Entropi
AtlasUnified
NeverendingToast
NanoBit
WingLian/Caseus
Also of course, as always, TheBloke, for being the backbone of the whole community.
Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others!
We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials:
http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx
Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2).
[<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2)
<a name="supported-tasks-and-leaderboards"></a>
# Supported Tasks and Leaderboards
This dataset supports a range of tasks including language modeling, text generation, and text augmentation.
It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing.
Further information on leaderboards will be updated as they become available.
<a name="languages"></a>
# Languages
The language of the data is primarily English.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
<a name="data-splits"></a>
## Data Splits
The data is unsplit.
<a name="dataset-creation"></a>
# Dataset Creation
<a name="curation-rationale"></a>
## Curation Rationale
The dataset was created to provide a source of augmented text data for researchers and developers.
The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4.
This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on.
<a name="source-data"></a>
## Source Data
The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below:
1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use.
We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available.
2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original).
These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source.
However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively.
Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work.
<a name="dataset-use"></a>
# Dataset Use
<a name="use-cases"></a>
## Use Cases
The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation.
<a name="usage-caveats"></a>
## Usage Caveats
Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements.
Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper.
<a name="getting-started"></a>
## Getting Started
This dataset is organized such that it can be naively loaded via Hugging Face datasets library.
We recommend using streaming due to the large size of the files.
Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.
# Citation
```bibtex
@misc{OpenOrca,
title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces},
author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca},
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint= arXiv 2307.09288
}
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
``` | [
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keshan/clean-si-mc4 | keshan | 2021-07-14T10:14:11Z | 239 | 0 | null | [
"region:us"
] | 2021-07-14T10:14:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | A cleaned version of MC4 dataset for Sinhala, config is a direct adaptation of MC4 original processing script. | [
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nielsr/eurosat-demo | nielsr | 2022-04-04T15:48:08Z | 239 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/anat_em | bigbio | 2022-12-22T15:43:16Z | 239 | 0 | null | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-12-22T15:43:16Z | 2022-11-13T18:26:03.000Z | 2022-11-13T18:26:03 |
---
language:
- en
bigbio_language:
- English
license: cc-by-sa-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_SA_3p0
pretty_name: AnatEM
homepage: http://nactem.ac.uk/anatomytagger/#AnatEM
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for AnatEM
## Dataset Description
- **Homepage:** http://nactem.ac.uk/anatomytagger/#AnatEM
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents (approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical entities. Each annotation is assigned one of 12 granularity-based types such as Cellular component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology.
## Citation Information
```
@article{pyysalo2014anatomical,
title={Anatomical entity mention recognition at literature scale},
author={Pyysalo, Sampo and Ananiadou, Sophia},
journal={Bioinformatics},
volume={30},
number={6},
pages={868--875},
year={2014},
publisher={Oxford University Press}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hrithikpiyush/acl-arc | hrithikpiyush | 2022-04-26T11:40:41Z | 238 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-04-26T11:40:41Z | 2022-04-26T05:00:57.000Z | 2022-04-26T05:00:57 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gopalkalpande/bbc-news-summary | gopalkalpande | 2022-06-22T13:08:15Z | 238 | 4 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-06-22T13:08:15Z | 2022-06-22T12:56:16.000Z | 2022-06-22T12:56:16 | ---
license: cc0-1.0
---
# About Dataset
### Context
Text summarization is a way to condense the large amount of information into a concise form by the process of selection of important information and discarding unimportant and redundant information. With the amount of textual information present in the world wide web the area of text summarization is becoming very important. The extractive summarization is the one where the exact sentences present in the document are used as summaries. The extractive summarization is simpler and is the general practice among the automatic text summarization researchers at the present time. Extractive summarization process involves giving scores to sentences using some method and then using the sentences that achieve highest scores as summaries. As the exact sentence present in the document is used the semantic factor can be ignored which results in generation of less calculation intensive summarization procedure. This kind of summary is generally completely unsupervised and language independent too. Although this kind of summary does its job in conveying the essential information it may not be necessarily smooth or fluent. Sometimes there can be almost no connection between adjacent sentences in the summary resulting in the text lacking in readability.
Content
This dataset for extractive text summarization has four hundred and seventeen political news articles of BBC from 2004 to 2005 in the News Articles folder. For each articles, five summaries are provided in the Summaries folder. The first clause of the text of articles is the respective title.
Acknowledgements
This dataset was created using a dataset used for data categorization that onsists of 2225 documents from the BBC news website corresponding to stories in five topical areas from 2004-2005 used in the paper of D. Greene and P. Cunningham. "Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering", Proc. ICML 2006; whose all rights, including copyright, in the content of the original articles are owned by the BBC. More at http://mlg.ucd.ie/datasets/bbc.html
**Kaggle Link:** https://www.kaggle.com/datasets/pariza/bbc-news-summary | [
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csebuetnlp/BanglaNMT | csebuetnlp | 2023-02-24T14:46:55Z | 238 | 0 | null | [
"task_categories:translation",
"annotations_creators:other",
"language_creators:found",
"multilinguality:translation",
"size_categories:1M<n<10M",
"language:bn",
"language:en",
"license:cc-by-nc-sa-4.0",
"bengali",
"BanglaNMT",
"region:us"
] | 2023-02-24T14:46:55Z | 2022-08-21T13:25:09.000Z | 2022-08-21T13:25:09 | ---
annotations_creators:
- other
language:
- bn
- en
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
pretty_name: BanglaNMT
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- bengali
- BanglaNMT
task_categories:
- translation
---
# Dataset Card for `BanglaNMT`
## Table of Contents
- [Dataset Card for `BanglaNMT`](#dataset-card-for-BanglaNMT)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Usage](#usage)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/csebuetnlp/banglanmt](https://github.com/csebuetnlp/banglanmt)
- **Paper:** [**"Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation"**](https://www.aclweb.org/anthology/2020.emnlp-main.207)
- **Point of Contact:** [Tahmid Hasan](mailto:tahmidhasan@cse.buet.ac.bd)
### Dataset Summary
This is the largest Machine Translation (MT) dataset for Bengali-English, curated using novel sentence alignment methods introduced **[here](https://aclanthology.org/2020.emnlp-main.207/).**
**Note:** This is a filtered version of the original dataset that the authors used for NMT training. For the complete set, refer to the offical [repository](https://github.com/csebuetnlp/banglanmt)
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Languages
- `Bengali`
- `English`
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("csebuetnlp/BanglaNMT")
```
## Dataset Structure
### Data Instances
One example from the dataset is given below in JSON format.
```
{
'bn': 'বিমানবন্দরে যুক্তরাজ্যে নিযুক্ত বাংলাদেশ হাইকমিশনার সাঈদা মুনা তাসনীম ও লন্ডনে বাংলাদেশ মিশনের জ্যেষ্ঠ কর্মকর্তারা তাকে বিদায় জানান।',
'en': 'Bangladesh High Commissioner to the United Kingdom Saida Muna Tasneen and senior officials of Bangladesh Mission in London saw him off at the airport.'
}
```
### Data Fields
The data fields are as follows:
- `bn`: a `string` feature indicating the Bengali sentence.
- `en`: a `string` feature indicating the English translation.
### Data Splits
| split |count |
|----------|--------|
|`train`| 2379749 |
|`validation`| 597 |
|`test`| 1000 |
## Dataset Creation
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Source Data
[More information needed](https://github.com/csebuetnlp/banglanmt)
#### Initial Data Collection and Normalization
[More information needed](https://github.com/csebuetnlp/banglanmt)
#### Who are the source language producers?
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Annotations
[More information needed](https://github.com/csebuetnlp/banglanmt)
#### Annotation process
[More information needed](https://github.com/csebuetnlp/banglanmt)
#### Who are the annotators?
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/banglanmt)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/banglanmt)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/banglanmt)
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use the dataset, please cite the following paper:
```
@inproceedings{hasan-etal-2020-low,
title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Samin, Kazi and
Hasan, Masum and
Basak, Madhusudan and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.207",
doi = "10.18653/v1/2020.emnlp-main.207",
pages = "2612--2623",
abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.",
}
```
### Contributions
Thanks to [@abhik1505040](https://github.com/abhik1505040) and [@Tahmid](https://github.com/Tahmid04) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/CMNLI | C-MTEB | 2023-07-27T17:35:51Z | 238 | 0 | null | [
"region:us"
] | 2023-07-27T17:35:51Z | 2023-07-27T17:35:44.000Z | 2023-07-27T17:35:44 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sent1
sequence: string
- name: sent2
sequence: string
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 1349125
num_examples: 1
download_size: 663026
dataset_size: 1349125
---
# Dataset Card for "CMNLI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/QBQTC | C-MTEB | 2023-07-28T13:38:12Z | 238 | 0 | null | [
"region:us"
] | 2023-07-28T13:38:12Z | 2023-07-28T13:38:05.000Z | 2023-07-28T13:38:05 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 524191
num_examples: 5000
download_size: 387552
dataset_size: 524191
---
# Dataset Card for "QBQTC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tahrirchi/uz-crawl | tahrirchi | 2023-10-28T19:10:00Z | 238 | 4 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:uz",
"license:apache-2.0",
"uz",
"crawl",
"telegram_blogs",
"region:us"
] | 2023-10-28T19:10:00Z | 2023-10-26T10:43:01.000Z | 2023-10-26T10:43:01 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: news
path: data/news-*
- split: telegram_blogs
path: data/telegram_blogs-*
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: string
- name: source
dtype: string
splits:
- name: news
num_bytes: 3272404822
num_examples: 964268
- name: telegram_blogs
num_bytes: 248666870
num_examples: 227337
download_size: 1581389108
dataset_size: 3521071692
annotations_creators:
- no-annotation
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
multilinguality:
- monolingual
language:
- uz
size_categories:
- 1M<n<10M
pretty_name: UzCrawl
tags:
- uz
- crawl
- telegram_blogs
---
# Dataset Card for UzCrawl
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://tahrirchi.uz/grammatika-tekshiruvi](https://tahrirchi.uz/grammatika-tekshiruvi)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.52 GB
- **Size of the generated dataset:** 1.58 GB
- **Total amount of disk used:** 5.1 GB
### Dataset Summary
In an effort to democratize research on low-resource languages, we release UzCrawl dataset, a web and telegram crawl corpus consisting of materials from nearly 1.2 million unique sources in the Uzbek Language.
Please refer to our [blogpost](https://tahrirchi.uz/grammatika-tekshiruvi) and paper (Coming soon!) for further details.
To load and use dataset, run this script:
```python
from datasets import load_dataset
uz_crawl=load_dataset("tahrirchi/uz-crawl")
```
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 3.52 GB
- **Size of the generated dataset:** 1.58 GB
- **Total amount of disk used:** 5.1 GB
An example of 'news' looks as follows.
```
{
'text': "O‘zbekiston Respublikasi Vazirlar Mahkamasining 2019 yil 24 iyuldagi 620-son qarori bilan tasdiqlangan «Xorijiy davlatlarda ta'lim olganlik to‘g‘risidagi hujjatlarni tan olish tartibi to‘g‘risida»gi Nizom ijrosini ta'minlash maqsadida Ta'lim sifatini nazorat qilish davlat inspeksiyasida (Toshkent shahar, Chilonzor tumani, Nurxon ko‘chasi, 21-uy) 2019 yil 9 –14 sentabr kunlari sohalar bo‘yicha sinov testlari bo‘lib o‘tishi rejalashtirilgan.\nTa'lim sifatini nazorat qilish davlat inspeksiyasi matbuot xizmati xabariga\xa0ko‘ra, «Huquqshunoslik», «Sog‘liqni saqlash va ijtimoiy ta'minot», «Iqtisodiyot», «Qishloq xo‘jaligi, muhandislik, ishlov berish va qurilish» hamda «O‘qituvchilar tayyorlash va pedagogik fanlar» sohalari bo‘yicha sinov testlari o‘tkaziladigan sanasi va sinov testida ishtirok etuvchilar ro‘yxati jadvalga muvofiq belgilanadi.\nTa'lim sifatini nazorat qilish davlat inspeksiyasi ogohlantirishicha, xorijiy davlatlarda ta'lim olganlik to‘g‘risidagi hujjatlarni tan olish uchun belgilangan sinov testlariga o‘z vaqtida kelmagan, sinov testida ishtirok etuvchilar ro‘yxatida mavjud bo‘lmagan talabgorlarga sinovlarga kirishga ruxsat etilmaydi.",
'timestamp': '2019-06-09',
'source': 'https://kun.uz/uz/news/2019/09/06/xorijda-talim-olganlik-togrisidagi-hujjatlarni-tan-olish-uchun-testlar-otkaziladigan-kunlar-malum-boldi'
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature that contains text.
- `timestamp`: a `string` feature that contains timestamp of the material.
- `source`: a `string` feature that contains url of the material.
### Data Splits
| name | |
|-----------------|--------:|
| news | 964268 |
| telegram_blogs | 227337 |
## Dataset Creation
The news portion have been crawled from 21 different websites using [Scrapy](https://scrapy.org/) framework. And telegram_blogs portion is consisted of manually curated texts from 81 high-quality Telegram channels.
## Citation
Please cite this model using the following format:
```
@online{Mamasaidov2023UzBooks,
author = {Mukhammadsaid Mamasaidov and Abror Shopulatov},
title = {UzCrawl dataset},
year = {2023},
url = {https://huggingface.co/datasets/tahrirchi/uz-crawl},
note = {Accessed: 2023-10-28}, % change this date
urldate = {2023-10-28} % change this date
}
```
## Gratitude
We are thankful to these awesome organizations and people for helping to make it happen:
- [Asadbek Kiyomov](https://www.linkedin.com/in/asadbey): for his works on the beginning of the project.
- [Ilya Gusev](https://github.com/IlyaGusev/): for his advise throughout the process
- [David Dale](https://daviddale.ru): for his advise throughout the process
## Contacts
We believe that this work will inspire all enthusiasts around the world to open the hidden beauty of low-resource languages, in particular of Uzbek.
For further development and issues about the dataset, please use m.mamasaidov@tahrirchi.uz or a.shopolatov@tahrirchi.uz to contact. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ScandEval/scala-nn | ScandEval | 2023-07-05T09:49:32Z | 237 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:nn",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-07-05T09:49:32Z | 2022-06-27T15:14:22.000Z | 2022-06-27T15:14:22 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- nn
size_categories:
- 1K<n<10K
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GATE-engine/aircraft_bbcrop | GATE-engine | 2023-06-04T22:22:54Z | 237 | 0 | null | [
"region:us"
] | 2023-06-04T22:22:54Z | 2023-06-04T22:22:30.000Z | 2023-06-04T22:22:30 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 253644684.5
num_examples: 3500
- name: validation
num_bytes: 70984494.0
num_examples: 1000
- name: test
num_bytes: 80183818.5
num_examples: 1100
download_size: 404802117
dataset_size: 404812997.0
---
# Dataset Card for "aircraft_bbcrop"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pvduy/rm_oa_hh | pvduy | 2023-06-13T16:39:03Z | 237 | 1 | null | [
"region:us"
] | 2023-06-13T16:39:03Z | 2023-06-13T15:40:34.000Z | 2023-06-13T15:40:34 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
- name: source
dtype: string
splits:
- name: test
num_bytes: 11065628
num_examples: 8524
- name: train
num_bytes: 220101381
num_examples: 166750
download_size: 135525253
dataset_size: 231167009
---
# Dataset Card for "rm_oa_hh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
atmallen/qm_mixture_1.0e | atmallen | 2023-11-16T18:17:48Z | 237 | 0 | null | [
"region:us"
] | 2023-11-16T18:17:48Z | 2023-11-16T03:32:56.000Z | 2023-11-16T03:32:56 | ---
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: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 44733311
num_examples: 400000
- name: validation
num_bytes: 4508863
num_examples: 40000
- name: test
num_bytes: 4496765
num_examples: 40000
download_size: 0
dataset_size: 53738939
---
# Dataset Card for "qm_mixture_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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C-MTEB/ThuNewsClusteringS2S | C-MTEB | 2023-07-27T17:28:46Z | 236 | 0 | null | [
"region:us"
] | 2023-07-27T17:28:46Z | 2023-07-27T17:28:35.000Z | 2023-07-27T17:28:35 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 6649209
num_examples: 10
download_size: 5008942
dataset_size: 6649209
---
# Dataset Card for "ThuNewsClusteringS2S"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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fbaigt/schema-to-json | fbaigt | 2023-11-20T03:11:36Z | 236 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | 2023-11-20T03:11:36Z | 2023-11-08T22:00:59.000Z | 2023-11-08T22:00:59 | ---
license: gpl-3.0
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ChaiML/20231012_chai_prize_reward_model_data | ChaiML | 2023-11-16T16:33:31Z | 236 | 2 | null | [
"region:us"
] | 2023-11-16T16:33:31Z | 2023-11-15T14:52:54.000Z | 2023-11-15T14:52:54 | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 73906677
num_examples: 49553
download_size: 42346275
dataset_size: 73906677
---
# Dataset Card for "20231012_chai_prize_reward_model_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
QingyiSi/Alpaca-CoT | QingyiSi | 2023-09-14T08:52:10Z | 235 | 532 | null | [
"language:en",
"language:zh",
"language:ml",
"license:apache-2.0",
"Instruction",
"Cot",
"region:us"
] | 2023-09-14T08:52:10Z | 2023-03-25T14:58:30.000Z | 2023-03-25T14:58:30 | ---
language:
- en
- zh
- ml
tags:
- Instruction
- Cot
license: apache-2.0
datasets:
- dataset1
- dataset2
---
# Instruction-Finetuning Dataset Collection (Alpaca-CoT)
This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the [code](https://github.com/PhoebusSi/alpaca-CoT) of Alpaca model.
We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in [https://github.com/PhoebusSi/alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT).
If you think this dataset collection is helpful to you, please `like` this dataset and `star` our [github project](https://github.com/PhoebusSi/alpaca-CoT)!
You are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints.
# Contribute
Welcome to join us and become a contributor to this project!
If you want to share some datasets, adjust the data in the following format:
```
example.json
[
{"instruction": instruction string,
"input": input string, # (may be empty)
"output": output string}
]
```
Folder should be like this:
```
Alpaca-CoT
|
|----example
| |
| |----example.json
| |
| ----example_context.json
...
```
Create a new pull request in [Community
](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/discussions) and publish your branch when you are ready. We will merge it as soon as we can.
# Data Usage and Resources
## Data Format
All data in this folder is formatted into the same templates, where each sample is as follows:
```
[
{"instruction": instruction string,
"input": input string, # (may be empty)
"output": output string}
]
```
## alpaca
#### alpaca_data.json
> This dataset is published by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca). It contains 52K English instruction-following samples obtained by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques.
#### alpaca_data_cleaned.json
> This dataset is obtained [here](https://github.com/tloen/alpaca-lora). It is a revised version of `alpaca_data.json` by stripping of various tokenization artifacts.
## alpacaGPT4
#### alpaca_gpt4_data.json
> This dataset is published by [Instruction-Tuning-with-GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM).
It contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.
#### alpaca_gpt4_data_zh.json
> This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT.
<!-- ## belle_cn
#### belle_data_cn.json
This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 0.5M Chinese instruction-following samples, which is also generated by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques.
#### belle_data1M_cn.json
This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 1M Chinese instruction-following samples. The data of `belle_data_cn.json` and `belle_data1M_cn.json` are not duplicated. -->
## Chain-of-Thought
#### CoT_data.json
> This dataset is obtained by formatting the combination of 9 CoT datasets published by [FLAN](https://github.com/google-research/FLAN). It contains 9 CoT tasks involving 74771 samples.
#### CoT_CN_data.json
> This dataset is obtained by tranlating `CoT_data.json` into Chinese, using Google Translate(en2cn).
#### formatted_cot_data folder
> This folder contains the formatted English data for each CoT dataset.
#### formatted_cot_data folder
> This folder contains the formatted Chinese data for each CoT dataset.
## CodeAlpaca
#### code_alpaca.json
> This dataset is published by [codealpaca](https://github.com/sahil280114/codealpaca). It contains code generation task involving 20022 samples.
## finance
#### finance_en.json
> This dataset is collected from [here](https://huggingface.co/datasets/gbharti/finance-alpaca). It contains 68912 financial related instructions in English.
## firefly
#### firefly.json
> his dataset is collected from [here](https://github.com/yangjianxin1/Firefly). It contains 1649398 chinese instructions in 23 nlp tasks.
## GPT4all
#### gpt4all.json
> This dataset is collected from [here](https://github.com/nomic-ai/gpt4all). It contains 806199 en instructions in code, storys and dialogs tasks.
#### gpt4all_without_p3.json
> gpt4all without Bigscience/P3, contains 437605 samples.
## GPTeacher
#### GPTeacher.json
> This dataset is collected from [here](https://github.com/teknium1/GPTeacher). It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer.
## Guanaco
#### GuanacoDataset.json
> This dataset is collected from [here](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset). It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition.
#### Guanaco_additional_Dataset.json
> A new additional larger dataset for different languages.
## HC3
#### HC3_ChatGPT.json/HC3_Human.json
> This dataset is collected from [here](https://huggingface.co/datasets/Hello-SimpleAI/HC3). It contains 37175 en/zh instructions generated by ChatGPT and human.
#### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json
> HC3 dataset without deduplication instructions.
## instinwild
#### instinwild_en.json & instinwild_cn.json
> The two datasets are obtained [here](https://github.com/XueFuzhao/InstructionWild). It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.)
## instruct
#### instruct.json
> The two datasets are obtained [here](https://huggingface.co/datasets/swype/instruct). It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI.
## Natural Instructions
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://github.com/allenai/natural-instructions). It contains 5040134 instructions, which are collected from diverse nlp tasks
## prosocial dialog
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://huggingface.co/datasets/allenai/prosocial-dialog). It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback
## xP3
#### natural-instructions-1700tasks.zip
> This dataset is obtained [here](https://huggingface.co/datasets/bigscience/xP3). It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks
## Chinese-instruction-collection
> all datasets of Chinese instruction collection
## combination
#### alcapa_plus_belle_data.json
> This dataset is the combination of English `alpaca_data.json` and Chinese `belle_data_cn.json`.
#### alcapa_plus_cot_data.json
> This dataset is the combination of English `alpaca_data.json` and CoT `CoT_data.json`.
#### alcapa_plus_belle_cot_data.json
> This dataset is the combination of English `alpaca_data.json`, Chinese `belle_data_cn.json` and CoT `CoT_data.json`.
## Citation
Please cite the repo if you use the data collection, code, and experimental findings in this repo.
```
@misc{alpaca-cot,
author = {Qingyi Si, Zheng Lin },
school = {Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China},
title = {Alpaca-CoT: An Instruction Fine-Tuning Platform with Instruction Data Collection and Unified Large Language Models Interface},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/PhoebusSi/alpaca-CoT}},
}
```
Cite the original Stanford Alpaca, BELLE and FLAN papers as well, please.
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Peppertuna/ChartQADatasetV2 | Peppertuna | 2023-09-11T02:26:42Z | 235 | 2 | null | [
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stvhuang/cc100 | stvhuang | 2023-10-30T03:00:20Z | 235 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
allenai/tulu-v2-sft-mixture | allenai | 2023-11-22T00:52:17Z | 235 | 18 | null | [
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:odc-by",
"arxiv:2305.18290",
"region:us"
] | 2023-11-22T00:52:17Z | 2023-11-13T21:56:34.000Z | 2023-11-13T21:56:34 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: dataset
dtype: string
- name: id
dtype: string
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 1239293363
num_examples: 326154
download_size: 554561769
dataset_size: 1239293363
license: odc-by
task_categories:
- question-answering
- conversational
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png" alt="TuluV2 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Dataset Card for Tulu V2 Mix
*Note the [ODC-BY license](https://opendatacommons.org/licenses/by/1-0/), indicating that different licenses apply to subsets of the data. This means that some portions of the dataset are non-commercial. We present the mixture as a research artifact.*
Tulu is a series of language models that are trained to act as helpful assistants.
The dataset consists of a mix of :
* [FLAN](https://github.com/google-research/FLAN/tree/main) (Apache 2.0): We use 50,000 examples sampled from FLAN v2. To emphasize CoT-style reasoning, we sample another 50,000 examples from the CoT
subset of the FLAN v2 mixture.
* [Open Assistant 1](https://huggingface.co/datasets/OpenAssistant/oasst1) (Apache 2.0): We isolate the highest-scoring paths in each conversation tree and use these samples, resulting in 7,708 examples.
* [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) (Apache 2.0 listed, no official repo found): We use all 114,046 from our processed ShareGPT dataset, as we found ShareGPT gave strong performance in prior work.
* [GPT4-Alpaca](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM#data-release) (CC By NC 4.0):We sample 20,000 samples from GPT-4 Alpaca to further include distilled GPT-4 data.
* [Code-Alpaca](https://github.com/sahil280114/codealpaca) (CC By NC 4.0):We use all 20,022 examples from Code Alpaca, following our prior V1 mixture, in order to improve model code abilities.
* [LIMA](https://huggingface.co/datasets/GAIR/lima) (CC BY-NC-SA): We use 1,030 examples from LIMA as an example of carefully curated data.
* [WizardLM Evol Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) (No license provided): We subsample 30,000 examples from WizardLM, which contains distilled data of increasing diversity and complexity.
* [Open-Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca) (MIT): We sample 30,000 samples generated by GPT-4 from OpenOrca, a reproduction of Orca Mukherjee et al., 2023, which augments FLAN data with additional model-generated explanations
* Hardcoded: A collection of prompts such as `Tell me about yourself' with 140 total samples manually written by the authors, such that the model generates correct outputs given inquiries about its name or developers.
* Science: 7,544 examples from a mixture of scientific document understand tasks—including question answering, fact-checking, summarization, and information extraction (under development, standalone release soon).
These are made by taking either just the training set of the subsets or the entire section if no splits are present.
Tulu V2 is presented as a singular training split.
[Tulu V2 DPO 70B](https://huggingface.co/allenai/tulu-2-dpo-70b), and is a fine-tuned version of Llama 2 that was trained on on a mix of publicly available, synthetic and human datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290).
**Model Family:** Other models and the dataset are found in the [Tulu V2 collection](https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101).
The length distribution of the dataset can be seen below:
<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/length_histogram_v2.png" alt="TuluV2 histogram" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Tulu V1 Mix can be found [here](https://huggingface.co/datasets/allenai/tulu-v1).
### Science data note
The included science data is from the following categories:
<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/science_data.png" alt="TuluV2 science data mix" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Note that some of the examples include an off-by-one error in the sentence indexing that had a small or negligible impact on performance.
This was found during testing and will be updated in future versions, with the detailed release of the dataset artifact itself coming in a future release.
### License
We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset.
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kuroneko5943/jd21 | kuroneko5943 | 2023-01-10T15:51:26Z | 234 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"jd",
"region:us"
] | 2023-01-10T15:51:26Z | 2023-01-10T10:49:13.000Z | 2023-01-10T10:49:13 | ---
annotations_creators:
- found
language:
- zh
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: jd21
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- jd
task_categories:
- text-classification
task_ids:
- sentiment-classification
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
skeskinen/TinyStories-GPT4 | skeskinen | 2023-05-20T19:00:22Z | 234 | 14 | null | [
"region:us"
] | 2023-05-20T19:00:22Z | 2023-05-20T18:58:41.000Z | 2023-05-20T18:58:41 | ---
dataset_info:
features:
- name: story
dtype: string
- name: summary
dtype: string
- name: source
dtype: string
- name: prompt
dtype: string
- name: words
sequence: string
- name: features
sequence: string
splits:
- name: train
num_bytes: 3680196493
num_examples: 2745100
download_size: 1553670972
dataset_size: 3680196493
---
# Dataset Card for "TinyStories-GPT4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jzjiao/halueval-sft | jzjiao | 2023-11-12T01:44:13Z | 234 | 0 | null | [
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | 2023-11-12T01:44:13Z | 2023-11-12T01:25:35.000Z | 2023-11-12T01:25:35 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: sft_text
dtype: string
- name: input
dtype: string
- name: ground_truth_output
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 190689411
num_examples: 45500
- name: test
num_bytes: 40645417
num_examples: 9750
- name: validation
num_bytes: 39692546
num_examples: 9750
download_size: 133425877
dataset_size: 271027374
license: mit
task_categories:
- question-answering
- conversational
language:
- en
pretty_name: HaluEval-SFT
size_categories:
- 10K<n<100K
---
# HaluEval-SFT Dataset
HaluEval-SFT Dataset is derived from the HaluEval(https://github.com/RUCAIBox/HaluEval), focusing on enhancing model capabilities in recognizing hallucinations. The dataset comprises a total of 65,000 data points, partitioned into training, validation, and test sets with a ratio of 0.7/0.15/0.15, respectively.
## Getting Started
```python
from datasets import load_dataset
dataset = load_dataset('jzjiao/halueval-sft', split = ["train"])
```
## Dataset Description
The HaluEval-SFT Dataset is structured as follows, with each entry comprising several key-value pairs that hold the data's attributes:
- `sft_text`: This field contains data specifically structured for use with supervised fine-tuning (SFT).
- `input`: The text provided to the model during testing or validation stages for it to generate its judgment or response.
- `ground_truth_output`: The expected output that a model should produce given the corresponding input.
- `type`: The original type in HaluEval. | [
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