datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
vegaviazhang/Med_QQpairs
|
vegaviazhang
| 2023-06-16T03:35:25Z | 122 | 3 |
[
"license:cc0-1.0",
"region:us"
] | null | 2023-06-16T03:33:41Z |
---
license: cc0-1.0
---
|
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open-llm-leaderboard/details_BreadAi__gpt-YA-1-1_70M
|
open-llm-leaderboard
| 2023-10-24T00:09:06Z | 122 | 0 |
[
"region:us"
] | null | 2023-08-18T18:53:52Z |
---
pretty_name: Evaluation run of BreadAi/gpt-YA-1-1_70M
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BreadAi/gpt-YA-1-1_70M](https://huggingface.co/BreadAi/gpt-YA-1-1_70M) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BreadAi__gpt-YA-1-1_70M\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T00:08:54.990074](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__gpt-YA-1-1_70M/blob/main/results_2023-10-24T00-08-54.990074.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\": 0.03617869127516778,\n\
\ \"em_stderr\": 0.0019123366108896051,\n \"f1\": 0.06183619966442954,\n\
\ \"f1_stderr\": 0.0021429123236932604,\n \"acc\": 0.25453827940015783,\n\
\ \"acc_stderr\": 0.007025085047248848\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.03617869127516778,\n \"em_stderr\": 0.0019123366108896051,\n\
\ \"f1\": 0.06183619966442954,\n \"f1_stderr\": 0.0021429123236932604\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5090765588003157,\n\
\ \"acc_stderr\": 0.014050170094497697\n }\n}\n```"
repo_url: https://huggingface.co/BreadAi/gpt-YA-1-1_70M
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|arc:challenge|25_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T00_08_54.990074
path:
- '**/details_harness|drop|3_2023-10-24T00-08-54.990074.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T00-08-54.990074.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T00_08_54.990074
path:
- '**/details_harness|gsm8k|5_2023-10-24T00-08-54.990074.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T00-08-54.990074.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hellaswag|10_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:44:57.081356.parquet'
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- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:44:57.081356.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:44:57.081356.parquet'
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:44:57.081356.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:44:57.081356.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:44:57.081356.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:44:57.081356.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:44:57.081356.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:44:57.081356.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:44:57.081356.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T18:44:57.081356.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T18:44:57.081356.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T00_08_54.990074
path:
- '**/details_harness|winogrande|5_2023-10-24T00-08-54.990074.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T00-08-54.990074.parquet'
- config_name: results
data_files:
- split: 2023_08_17T18_44_57.081356
path:
- results_2023-08-17T18:44:57.081356.parquet
- split: 2023_10_24T00_08_54.990074
path:
- results_2023-10-24T00-08-54.990074.parquet
- split: latest
path:
- results_2023-10-24T00-08-54.990074.parquet
---
# Dataset Card for Evaluation run of BreadAi/gpt-YA-1-1_70M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/BreadAi/gpt-YA-1-1_70M
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [BreadAi/gpt-YA-1-1_70M](https://huggingface.co/BreadAi/gpt-YA-1-1_70M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BreadAi__gpt-YA-1-1_70M",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T00:08:54.990074](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__gpt-YA-1-1_70M/blob/main/results_2023-10-24T00-08-54.990074.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": 0.03617869127516778,
"em_stderr": 0.0019123366108896051,
"f1": 0.06183619966442954,
"f1_stderr": 0.0021429123236932604,
"acc": 0.25453827940015783,
"acc_stderr": 0.007025085047248848
},
"harness|drop|3": {
"em": 0.03617869127516778,
"em_stderr": 0.0019123366108896051,
"f1": 0.06183619966442954,
"f1_stderr": 0.0021429123236932604
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5090765588003157,
"acc_stderr": 0.014050170094497697
}
}
```
### 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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open-llm-leaderboard/details_Sao10K__Stheno-1.1-L2-13B
|
open-llm-leaderboard
| 2023-10-23T21:31:34Z | 122 | 0 |
[
"region:us"
] | null | 2023-09-12T10:14:29Z |
---
pretty_name: Evaluation run of Sao10K/Stheno-1.1-L2-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Sao10K/Stheno-1.1-L2-13B](https://huggingface.co/Sao10K/Stheno-1.1-L2-13B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Stheno-1.1-L2-13B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T21:31:21.044971](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-1.1-L2-13B/blob/main/results_2023-10-23T21-31-21.044971.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\": 0.4002726510067114,\n\
\ \"em_stderr\": 0.00501758267554197,\n \"f1\": 0.46779362416107595,\n\
\ \"f1_stderr\": 0.00480612571124351,\n \"acc\": 0.41588812169655687,\n\
\ \"acc_stderr\": 0.0097951551792708\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.4002726510067114,\n \"em_stderr\": 0.00501758267554197,\n\
\ \"f1\": 0.46779362416107595,\n \"f1_stderr\": 0.00480612571124351\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07960576194086429,\n \
\ \"acc_stderr\": 0.007455924338676254\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.012134386019865346\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Sao10K/Stheno-1.1-L2-13B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|arc:challenge|25_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T21_31_21.044971
path:
- '**/details_harness|drop|3_2023-10-23T21-31-21.044971.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T21-31-21.044971.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T21_31_21.044971
path:
- '**/details_harness|gsm8k|5_2023-10-23T21-31-21.044971.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T21-31-21.044971.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hellaswag|10_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T10-14-13.361250.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T10-14-13.361250.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T10-14-13.361250.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T10-14-13.361250.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T21_31_21.044971
path:
- '**/details_harness|winogrande|5_2023-10-23T21-31-21.044971.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T21-31-21.044971.parquet'
- config_name: results
data_files:
- split: 2023_09_12T10_14_13.361250
path:
- results_2023-09-12T10-14-13.361250.parquet
- split: 2023_10_23T21_31_21.044971
path:
- results_2023-10-23T21-31-21.044971.parquet
- split: latest
path:
- results_2023-10-23T21-31-21.044971.parquet
---
# Dataset Card for Evaluation run of Sao10K/Stheno-1.1-L2-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/Stheno-1.1-L2-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Sao10K/Stheno-1.1-L2-13B](https://huggingface.co/Sao10K/Stheno-1.1-L2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Sao10K__Stheno-1.1-L2-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T21:31:21.044971](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Stheno-1.1-L2-13B/blob/main/results_2023-10-23T21-31-21.044971.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": 0.4002726510067114,
"em_stderr": 0.00501758267554197,
"f1": 0.46779362416107595,
"f1_stderr": 0.00480612571124351,
"acc": 0.41588812169655687,
"acc_stderr": 0.0097951551792708
},
"harness|drop|3": {
"em": 0.4002726510067114,
"em_stderr": 0.00501758267554197,
"f1": 0.46779362416107595,
"f1_stderr": 0.00480612571124351
},
"harness|gsm8k|5": {
"acc": 0.07960576194086429,
"acc_stderr": 0.007455924338676254
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.012134386019865346
}
}
```
### 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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open-llm-leaderboard/details_winglian__Llama-2-3b-hf
|
open-llm-leaderboard
| 2023-10-24T02:32:06Z | 122 | 0 |
[
"region:us"
] | null | 2023-10-03T14:29:55Z |
---
pretty_name: Evaluation run of winglian/Llama-2-3b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [winglian/Llama-2-3b-hf](https://huggingface.co/winglian/Llama-2-3b-hf) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_winglian__Llama-2-3b-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T02:31:53.066054](https://huggingface.co/datasets/open-llm-leaderboard/details_winglian__Llama-2-3b-hf/blob/main/results_2023-10-24T02-31-53.066054.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\": 0.01960989932885906,\n\
\ \"em_stderr\": 0.0014199622282460517,\n \"f1\": 0.026280411073825484,\n\
\ \"f1_stderr\": 0.0015385339771850702,\n \"acc\": 0.24822415153906865,\n\
\ \"acc_stderr\": 0.007026065573457934\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01960989932885906,\n \"em_stderr\": 0.0014199622282460517,\n\
\ \"f1\": 0.026280411073825484,\n \"f1_stderr\": 0.0015385339771850702\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.4964483030781373,\n\
\ \"acc_stderr\": 0.014052131146915867\n }\n}\n```"
repo_url: https://huggingface.co/winglian/Llama-2-3b-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|arc:challenge|25_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T02_31_53.066054
path:
- '**/details_harness|drop|3_2023-10-24T02-31-53.066054.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T02-31-53.066054.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T02_31_53.066054
path:
- '**/details_harness|gsm8k|5_2023-10-24T02-31-53.066054.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T02-31-53.066054.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hellaswag|10_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-31.026296.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T14-29-31.026296.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T14-29-31.026296.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T02_31_53.066054
path:
- '**/details_harness|winogrande|5_2023-10-24T02-31-53.066054.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T02-31-53.066054.parquet'
- config_name: results
data_files:
- split: 2023_10_03T14_29_31.026296
path:
- results_2023-10-03T14-29-31.026296.parquet
- split: 2023_10_24T02_31_53.066054
path:
- results_2023-10-24T02-31-53.066054.parquet
- split: latest
path:
- results_2023-10-24T02-31-53.066054.parquet
---
# Dataset Card for Evaluation run of winglian/Llama-2-3b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/winglian/Llama-2-3b-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [winglian/Llama-2-3b-hf](https://huggingface.co/winglian/Llama-2-3b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_winglian__Llama-2-3b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T02:31:53.066054](https://huggingface.co/datasets/open-llm-leaderboard/details_winglian__Llama-2-3b-hf/blob/main/results_2023-10-24T02-31-53.066054.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": 0.01960989932885906,
"em_stderr": 0.0014199622282460517,
"f1": 0.026280411073825484,
"f1_stderr": 0.0015385339771850702,
"acc": 0.24822415153906865,
"acc_stderr": 0.007026065573457934
},
"harness|drop|3": {
"em": 0.01960989932885906,
"em_stderr": 0.0014199622282460517,
"f1": 0.026280411073825484,
"f1_stderr": 0.0015385339771850702
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4964483030781373,
"acc_stderr": 0.014052131146915867
}
}
```
### 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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patent/AIPD_nlp_sbert_dataset
|
patent
| 2023-11-03T23:57:35Z | 122 | 0 |
[
"region:us"
] | null | 2023-11-03T09:25:48Z |
---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 1130851138.7014475
num_examples: 453043
- name: test
num_bytes: 62827420.71087167
num_examples: 25170
- name: valid
num_bytes: 62824924.58768093
num_examples: 25169
download_size: 476485691
dataset_size: 1256503484.0
---
# Dataset Card for "AIPD_nlp_sbert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
confit/gtzan
|
confit
| 2023-11-24T10:20:07Z | 122 | 0 |
[
"region:us"
] | null | 2023-11-24T09:32:29Z |
---
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: filename
dtype: string
- name: label
dtype:
class_label:
names:
'0': blues
'1': classical
'2': country
'3': disco
'4': hiphop
'5': jazz
'6': metal
'7': pop
'8': reggae
'9': rock
splits:
- name: train
num_bytes: 17683
num_examples: 443
- name: validation
num_bytes: 7871
num_examples: 197
- name: test
num_bytes: 11546
num_examples: 290
download_size: 10908
dataset_size: 37100
---
# Dataset Card for "gtzan"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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zxvix/Anatomy_QA
|
zxvix
| 2023-12-03T12:36:12Z | 122 | 0 |
[
"region:us"
] | null | 2023-11-25T03:47:28Z |
---
dataset_info:
- config_name: default
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: original_text
dtype: string
splits:
- name: test
num_bytes: 23420104
num_examples: 6386
download_size: 1466159
dataset_size: 23420104
- config_name: multiple_choice
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype: int64
- name: original_text
dtype: string
splits:
- name: test
num_bytes: 23941659
num_examples: 6363
download_size: 1640221
dataset_size: 23941659
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: multiple_choice
data_files:
- split: test
path: multiple_choice/test-*
---
|
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ajders/machine_translated_cnn_dailymail_da_small
|
ajders
| 2022-08-26T13:01:36Z | 121 | 0 |
[
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"language:da",
"license:apache-2.0",
"region:us"
] |
[
"summarization"
] | 2022-05-24T11:51:34Z |
---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- da
license:
- apache-2.0
multilinguality:
- translation
pretty_name: machine_translated_cnn_dailymail_da_small
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- summarization
task_ids:
- news-articles-summarization
---
# Dataset Card for machine_translated_cnn_dailymail_da_small
### Dataset Summary
This dataset is a machine translated subset of the [CNN Dailymail Dataset](https://huggingface.co/datasets/ccdv/cnn_dailymail) into Danish. The dataset is translated using the [Helsinki-NLP/opus-mt-en-da](https://huggingface.co/Helsinki-NLP/opus-mt-en-da)-model. The dataset consists of 2872 articles with summaries with intended usage for Danish text summarisation.
## Dataset Structure
Machine translated articles (`article`) with corresponding summaries (`highlights`).
```
{
'article': Value(dtype='string', id=None),
'highlights': Value(dtype='string', id=None),
'id': Value(dtype='string', id=None)
}
```
### Licensing Information
The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
|
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] |
biglam/europeana_newspapers
|
biglam
| 2023-01-06T11:42:17Z | 121 | 2 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"language:de",
"language:fr",
"language:el",
"language:et",
"language:fi",
"language:hr",
"language:ji",
"language:pl",
"language:ru",
"language:sr",
"language:sv",
"language:uk",
"newspapers",
"lam",
"OCR",
"region:us"
] |
[
"text-generation"
] | 2022-10-04T16:31:37Z |
---
annotations_creators:
- no-annotation
language:
- de
- fr
- el
- et
- fi
- hr
- ji
- pl
- ru
- sr
- sv
- uk
language_creators:
- machine-generated
multilinguality:
- multilingual
pretty_name: 'Europeana Newspapers '
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- newspapers
- lam
- OCR
task_categories:
- text-generation
task_ids:
- language-modeling
---
|
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] |
mstz/diamonds
|
mstz
| 2023-04-16T17:27:20Z | 121 | 0 |
[
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"student performance",
"tabular_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-03-24T01:12:26Z |
---
language:
- en
tags:
- student performance
- tabular_classification
- multiclass_classification
- UCI
pretty_name: Diamond
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- encoding
- cut
- cut_binary
license: cc
---
# Diamonds
The [Diamonds dataset](https://www.kaggle.com/datasets/ulrikthygepedersen/diamonds) from Kaggle.
Dataset collecting properties of cut diamonds to determine the cut quality.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| cut | Multiclass classification | Predict the cut quality of the diamond. |
| cut_binary | Binary classification | Is the cut quality at least very good?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/diamonds", "cut")["train"]
```
# Features
|**Feature** |**Description**|
|-----------------------------------|---------------|
|`carat` | `float32` |
|`color` | `string` |
|`clarity` | `float32` |
|`depth` | `float32` |
|`table` | `float32` |
|`price` | `float32` |
|`observation_point_on_axis_x` | `float32` |
|`observation_point_on_axis_y` | `float32` |
|`observation_point_on_axis_z` | `float32` |
|`cut` | `int8` |
|
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] |
d0rj/wikisum
|
d0rj
| 2023-06-16T11:24:25Z | 121 | 1 |
[
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"abstractive-summarization",
"wiki",
"abstractive",
"arxiv:1801.10198",
"region:us"
] |
[
"summarization"
] | 2023-06-16T11:13:38Z |
---
dataset_info:
features:
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: article
dtype: string
- name: step_headers
dtype: string
splits:
- name: train
num_bytes: 315275236
num_examples: 35775
- name: test
num_bytes: 17584216
num_examples: 2000
- name: validation
num_bytes: 17880851
num_examples: 2000
download_size: 194202865
dataset_size: 350740303
license:
- unknown
task_categories:
- summarization
language:
- en
multilinguality:
- monolingual
tags:
- abstractive-summarization
- wiki
- abstractive
pretty_name: 'WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation'
size_categories:
- 10K<n<100K
source_datasets:
- original
paperswithcode_id: wikisum
---
# wikisum
## Dataset Description
- **Homepage:** https://registry.opendata.aws/wikisum/
- **Repository:** https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikisum
- **Paper:** [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [nachshon](mailto:[email protected])
|
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open-llm-leaderboard/details_tiiuae__falcon-rw-1b
|
open-llm-leaderboard
| 2023-10-25T18:16:18Z | 121 | 0 |
[
"region:us"
] | null | 2023-09-13T16:16:56Z |
---
pretty_name: Evaluation run of tiiuae/falcon-rw-1b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_tiiuae__falcon-rw-1b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T18:16:05.784566](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-rw-1b/blob/main/results_2023-10-25T18-16-05.784566.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\": 0.0010486577181208054,\n\
\ \"em_stderr\": 0.00033145814652193675,\n \"f1\": 0.0464429530201344,\n\
\ \"f1_stderr\": 0.001186214815178995,\n \"acc\": 0.31283505657403515,\n\
\ \"acc_stderr\": 0.007820275562329611\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652193675,\n\
\ \"f1\": 0.0464429530201344,\n \"f1_stderr\": 0.001186214815178995\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \
\ \"acc_stderr\": 0.0020013057209480574\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6203630623520127,\n \"acc_stderr\": 0.013639245403711165\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-rw-1b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|arc:challenge|25_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T18_16_05.784566
path:
- '**/details_harness|drop|3_2023-10-25T18-16-05.784566.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T18-16-05.784566.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T18_16_05.784566
path:
- '**/details_harness|gsm8k|5_2023-10-25T18-16-05.784566.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T18-16-05.784566.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hellaswag|10_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T16-16-44.792936.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T16-16-44.792936.parquet'
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path:
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T16-16-44.792936.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T16-16-44.792936.parquet'
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T16-16-44.792936.parquet'
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T16-16-44.792936.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T16-16-44.792936.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T18_16_05.784566
path:
- '**/details_harness|winogrande|5_2023-10-25T18-16-05.784566.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T18-16-05.784566.parquet'
- config_name: results
data_files:
- split: 2023_09_13T16_16_44.792936
path:
- results_2023-09-13T16-16-44.792936.parquet
- split: 2023_10_25T18_16_05.784566
path:
- results_2023-10-25T18-16-05.784566.parquet
- split: latest
path:
- results_2023-10-25T18-16-05.784566.parquet
---
# Dataset Card for Evaluation run of tiiuae/falcon-rw-1b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-rw-1b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_tiiuae__falcon-rw-1b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T18:16:05.784566](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-rw-1b/blob/main/results_2023-10-25T18-16-05.784566.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": 0.0010486577181208054,
"em_stderr": 0.00033145814652193675,
"f1": 0.0464429530201344,
"f1_stderr": 0.001186214815178995,
"acc": 0.31283505657403515,
"acc_stderr": 0.007820275562329611
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652193675,
"f1": 0.0464429530201344,
"f1_stderr": 0.001186214815178995
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.0020013057209480574
},
"harness|winogrande|5": {
"acc": 0.6203630623520127,
"acc_stderr": 0.013639245403711165
}
}
```
### 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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cyrilzhang/wiki-bpe-32k
|
cyrilzhang
| 2023-09-22T16:02:48Z | 121 | 0 |
[
"region:us"
] | null | 2023-09-22T15:56:45Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 21123228700
num_examples: 5152007
- name: test
num_bytes: 212326700
num_examples: 51787
download_size: 10331372531
dataset_size: 21335555400
---
# Dataset Card for "wiki-bpe-32k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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hippocrates/CitationGPTv2_train
|
hippocrates
| 2023-11-10T17:06:33Z | 121 | 0 |
[
"region:us"
] | null | 2023-11-07T19:12:52Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 365416080
num_examples: 99360
- name: valid
num_bytes: 47324714
num_examples: 12760
- name: test
num_bytes: 42152251
num_examples: 11615
download_size: 17034010
dataset_size: 454893045
---
# Dataset Card for "CitationGPTv2_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kpriyanshu256/semeval-task-8-a-mono-v2
|
kpriyanshu256
| 2023-11-10T14:40:40Z | 121 | 0 |
[
"region:us"
] | null | 2023-11-10T14:40:26Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: model
dtype: string
- name: source
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 272387024
num_examples: 95805
- name: val
num_bytes: 66852841
num_examples: 23952
- name: test
num_bytes: 10543757
num_examples: 5000
download_size: 201715990
dataset_size: 349783622
---
# Dataset Card for "semeval-task-8-a-mono-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
shariqfarooq/lcn_bd
|
shariqfarooq
| 2023-11-16T12:06:47Z | 121 | 0 |
[
"region:us"
] | null | 2023-11-16T10:40:58Z |
---
dataset_info:
features:
- name: caption
dtype: string
- name: condition
dtype: image
- name: controlnet
dtype: image
- name: ours
dtype: image
- name: idd
dtype: string
splits:
- name: train
num_bytes: 14336782.0
num_examples: 17
download_size: 14350234
dataset_size: 14336782.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "lcn_bd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Aniemore/cedr-m7
|
Aniemore
| 2022-07-01T16:39:56Z | 120 | 5 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|cedr",
"language:ru",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2022-05-24T18:01:54Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- ru
license: mit
multilinguality:
- monolingual
pretty_name: cedr-m7
size_categories:
- 1K<n<10K
source_datasets:
- extended|cedr
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for CEDR-M7
## 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
```
@misc{Aniemore,
author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
year = {2022},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
email = {[email protected]}
}
```
### Contributions
Thanks to [@toiletsandpaper](https://github.com/toiletsandpaper) for adding this dataset.
|
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FreedomIntelligence/huatuo_encyclopedia_qa
|
FreedomIntelligence
| 2023-05-17T03:20:55Z | 120 | 25 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"medical",
"arxiv:2305.01526",
"region:us"
] |
[
"text-generation"
] | 2023-05-10T08:30:14Z |
---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
tags:
- medical
size_categories:
- 100K<n<1M
---
# Dataset Card for Huatuo_encyclopedia_qa
## Dataset Description
- **Homepage: https://www.huatuogpt.cn/**
- **Repository: https://github.com/FreedomIntelligence/HuatuoGPT**
- **Paper: https://arxiv.org/abs/2305.01526**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset has a total of 364,420 pieces of medical QA data, some of which have multiple questions in different ways. We extract medical QA pairs from plain texts (e.g., medical encyclopedias and medical articles). We collected 8,699 encyclopedia entries for diseases and 2,736 encyclopedia entries for medicines on Chinese Wikipedia. Moreover, we crawled 226,432 high-quality medical articles from the Qianwen Health website.
## Dataset Creation
### Source Data
https://zh.wikipedia.org/wiki/
https://51zyzy.com/
## Citation
```
@misc{li2023huatuo26m,
title={Huatuo-26M, a Large-scale Chinese Medical QA Dataset},
author={Jianquan Li and Xidong Wang and Xiangbo Wu and Zhiyi Zhang and Xiaolong Xu and Jie Fu and Prayag Tiwari and Xiang Wan and Benyou Wang},
year={2023},
eprint={2305.01526},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
vlsp-2023-vllm/en-to-vi-formal-informal-tranlations
|
vlsp-2023-vllm
| 2023-10-30T02:59:09Z | 120 | 0 |
[
"region:us"
] | null | 2023-09-12T04:17:33Z |
---
dataset_info:
features:
- name: en
dtype: string
- name: vi
dtype: string
- name: fewshot_samples
list:
- name: en
dtype: string
- name: vi
dtype: string
splits:
- name: val
num_bytes: 178154
num_examples: 160
- name: test
num_bytes: 175339
num_examples: 160
download_size: 124988
dataset_size: 353493
---
# Few-shot Translation
## 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 translation_vi \
--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 translation_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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open-llm-leaderboard/details_euclaise__falcon_1b_stage3_2
|
open-llm-leaderboard
| 2023-10-24T01:21:22Z | 120 | 0 |
[
"region:us"
] | null | 2023-10-10T19:39:49Z |
---
pretty_name: Evaluation run of euclaise/falcon_1b_stage3_2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [euclaise/falcon_1b_stage3_2](https://huggingface.co/euclaise/falcon_1b_stage3_2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_euclaise__falcon_1b_stage3_2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T01:21:10.323077](https://huggingface.co/datasets/open-llm-leaderboard/details_euclaise__falcon_1b_stage3_2/blob/main/results_2023-10-24T01-21-10.323077.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\": 0.15541107382550334,\n\
\ \"em_stderr\": 0.0037102512781803346,\n \"f1\": 0.2052170721476513,\n\
\ \"f1_stderr\": 0.0038511842764741527,\n \"acc\": 0.3022888713496448,\n\
\ \"acc_stderr\": 0.006870839193772672\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.15541107382550334,\n \"em_stderr\": 0.0037102512781803346,\n\
\ \"f1\": 0.2052170721476513,\n \"f1_stderr\": 0.0038511842764741527\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6045777426992897,\n\
\ \"acc_stderr\": 0.013741678387545343\n }\n}\n```"
repo_url: https://huggingface.co/euclaise/falcon_1b_stage3_2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|arc:challenge|25_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T01_21_10.323077
path:
- '**/details_harness|drop|3_2023-10-24T01-21-10.323077.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T01-21-10.323077.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T01_21_10.323077
path:
- '**/details_harness|gsm8k|5_2023-10-24T01-21-10.323077.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T01-21-10.323077.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hellaswag|10_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-39-31.631601.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-39-31.631601.parquet'
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- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-39-31.631601.parquet'
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- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-39-31.631601.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T19-39-31.631601.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T19-39-31.631601.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T01_21_10.323077
path:
- '**/details_harness|winogrande|5_2023-10-24T01-21-10.323077.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T01-21-10.323077.parquet'
- config_name: results
data_files:
- split: 2023_10_10T19_39_31.631601
path:
- results_2023-10-10T19-39-31.631601.parquet
- split: 2023_10_24T01_21_10.323077
path:
- results_2023-10-24T01-21-10.323077.parquet
- split: latest
path:
- results_2023-10-24T01-21-10.323077.parquet
---
# Dataset Card for Evaluation run of euclaise/falcon_1b_stage3_2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/euclaise/falcon_1b_stage3_2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [euclaise/falcon_1b_stage3_2](https://huggingface.co/euclaise/falcon_1b_stage3_2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_euclaise__falcon_1b_stage3_2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T01:21:10.323077](https://huggingface.co/datasets/open-llm-leaderboard/details_euclaise__falcon_1b_stage3_2/blob/main/results_2023-10-24T01-21-10.323077.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": 0.15541107382550334,
"em_stderr": 0.0037102512781803346,
"f1": 0.2052170721476513,
"f1_stderr": 0.0038511842764741527,
"acc": 0.3022888713496448,
"acc_stderr": 0.006870839193772672
},
"harness|drop|3": {
"em": 0.15541107382550334,
"em_stderr": 0.0037102512781803346,
"f1": 0.2052170721476513,
"f1_stderr": 0.0038511842764741527
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.6045777426992897,
"acc_stderr": 0.013741678387545343
}
}
```
### 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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strombergnlp/nordic_langid
|
strombergnlp
| 2022-10-25T21:42:02Z | 119 | 3 |
[
"task_categories:text-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:da",
"language:nn",
"language:nb",
"language:fo",
"language:is",
"language:sv",
"license:cc-by-sa-3.0",
"language-identification",
"region:us"
] |
[
"text-classification"
] | 2022-05-10T17:27:03Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- da
- nn
- nb
- fo
- is
- sv
license:
- cc-by-sa-3.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: nordic-langid
pretty_name: Nordic Language ID for Distinguishing between Similar Languages
tags:
- language-identification
---
# Dataset Card for nordic_langid
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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:** [https://github.com/StrombergNLP/NordicDSL](https://github.com/StrombergNLP/NordicDSL)
- **Repository:** [https://github.com/StrombergNLP/NordicDSL](https://github.com/StrombergNLP/NordicDSL)
- **Paper:** [https://aclanthology.org/2021.vardial-1.8/](https://aclanthology.org/2021.vardial-1.8/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [René Haas](mailto:[email protected])
### Dataset Summary
Automatic language identification is a challenging problem. Discriminating
between closely related languages is especially difficult. This paper presents
a machine learning approach for automatic language identification for the
Nordic languages, which often suffer miscategorisation by existing
state-of-the-art tools. Concretely we will focus on discrimination between six
Nordic language: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokmål),
Faroese and Icelandic.
This is the data for the tasks. Two variants are provided: 10K and 50K, with
holding 10,000 and 50,000 examples for each language respectively.
For more info, see the paper: [Discriminating Between Similar Nordic Languages](https://aclanthology.org/2021.vardial-1.8/).
### Supported Tasks and Leaderboards
*
### Languages
This dataset is in six similar Nordic language:
- Danish, `da`
- Faroese, `fo`
- Icelandic, `is`
- Norwegian Bokmål, `nb`
- Norwegian Nynorsk, `nn`
- Swedish, `sv`
## Dataset Structure
The dataset has two parts, one with 10K samples per language and another with 50K per language.
The original splits and data allocation used in the paper is presented here.
### Data Instances
[Needs More Information]
### Data Fields
- `id`: the sentence's unique identifier, `string`
- `sentence`: the test to be classifier, a `string`
- `language`: the class, one of `da`, `fo`, `is`, `nb`, `no`, `sv`.
### Data Splits
Train and Test splits are provided, divided using the code provided with the paper.
## Dataset Creation
### Curation Rationale
Data is taken from Wikipedia and Tatoeba from each of these six languages.
### Source Data
#### Initial Data Collection and Normalization
**Data collection** Data was scraped from Wikipedia. We downloaded summaries for randomly chosen Wikipedia
articles in each of the languages, saved as raw text
to six .txt files of about 10MB each.
The 50K section is extended with Tatoeba data, which provides a different register to Wikipedia text, and then topped up with more Wikipedia data.
**Extracting Sentences** The first pass in sentence
tokenisation is splitting by line breaks. We then extract shorter sentences with the sentence tokenizer
(sent_tokenize) function from NLTK (Loper
and Bird, 2002). This does a better job than just
splitting by ’.’ due to the fact that abbreviations,
which can appear in a legitimate sentence, typically
include a period symbol.
**Cleaning characters** The initial data set has
many characters that do not belong to the alphabets of the languages we work with. Often the
Wikipedia pages for people or places contain names
in foreign languages. For example a summary
might contain Chinese or Russian characters which
are not strong signals for the purpose of discriminating between the target languages.
Further, it can be that some characters in the
target languages are mis-encoded. These misencodings are also not likely to be intrinsically
strong or stable signals.
To simplify feature extraction, and to reduce the
size of the vocabulary, the raw data is converted
to lowercase and stripped of all characters which
are not part of the standard alphabet of the six
languages using a character whitelist.
#### Who are the source language producers?
The source language is from Wikipedia contributors and Tatoeba contributors.
### Annotations
#### Annotation process
The annotations were found.
#### Who are the annotators?
The annotations were found. They are determined by which language section a contributor posts their content to.
### Personal and Sensitive Information
The data hasn't been checked for PII, and is already all public. Tatoeba is is based on translations of synthetic conversational turns and is unlikely to bear personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to help correctly identify content in the languages of six minority languages. Existing systems often confuse these, especially Bokmål and Danish or Icelandic and Faroese. However, some dialects are missed (for example Bornholmsk) and the closed nature of the classification task thus excludes speakers of these languages without recognising their existence.
### Discussion of Biases
The text comes from only two genres, so might not transfer well to other domains.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The data here is licensed CC-BY-SA 3.0. If you use this data, you MUST state its origin.
### Citation Information
````
@inproceedings{haas-derczynski-2021-discriminating,
title = "Discriminating Between Similar Nordic Languages",
author = "Haas, Ren{\'e} and
Derczynski, Leon",
booktitle = "Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.vardial-1.8",
pages = "67--75",
}
```
|
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] |
tner/tweetner7
|
tner
| 2022-11-27T18:50:28Z | 119 | 2 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1k<10K",
"language:en",
"license:other",
"arxiv:2210.03797",
"region:us"
] |
[
"token-classification"
] | 2022-07-18T10:39:50Z |
---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TweetNER7
---
# Dataset Card for "tner/tweetner7"
## Dataset Description
- **Repository:** [https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper)
- **Paper:** [https://arxiv.org/abs/2210.03797](https://arxiv.org/abs/2210.03797)
- **Dataset:** TweetNER7
- **Domain:** Twitter
- **Number of Entity:** 7
### Dataset Summary
This is the official repository of TweetNER7 (["Named Entity Recognition in Twitter:
A Dataset and Analysis on Short-Term Temporal Shifts, AACL main conference 2022"](https://arxiv.org/abs/2210.03797)), an NER dataset on Twitter with 7 entity labels. Each instance of TweetNER7 comes with a timestamp which distributes from September 2019 to August 2021.
The tweet collection used in TweetNER7 is same as what used in [TweetTopic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi).
The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
- Entity Types: `corperation`, `creative_work`, `event`, `group`, `location`, `product`, `person`
### Preprocessing
We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
For verified usernames, we replace its display name (or account name) with symbols `{@}`.
For example, a tweet
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below:
http://bluenote.lnk.to/AlbumOfTheWeek
```
is transformed into the following text.
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}
```
A simple function to format tweet follows below.
```python
import re
from urlextract import URLExtract
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
```
We ask annotators to ignore those special tokens but label the verified users' mentions.
### Data Split
| split | number of instances | description |
|:------------------|------:|------:|
| train_2020 | 4616 | training dataset from September 2019 to August 2020 |
| train_2021 | 2495 | training dataset from September 2020 to August 2021 |
| train_all | 7111 | combined training dataset of `train_2020` and `train_2021` |
| validation_2020 | 576 | validation dataset from September 2019 to August 2020 |
| validation_2021 | 310 | validation dataset from September 2020 to August 2021 |
| test_2020 | 576 | test dataset from September 2019 to August 2020 |
| test_2021 | 2807 | test dataset from September 2020 to August 2021 |
| train_random | 4616 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
| validation_random | 576 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
| extra_2020 | 87880 | extra tweet without annotations from September 2019 to August 2020 |
| extra_2021 | 93594 | extra tweet without annotations from September 2020 to August 2021 |
For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tokens': ['Morning', '5km', 'run', 'with', '{{USERNAME}}', 'for', 'breast', 'cancer', 'awareness', '#', 'pinkoctober', '#', 'breastcancerawareness', '#', 'zalorafit', '#', 'zalorafitxbnwrc', '@', 'The', 'Central', 'Park', ',', 'Desa', 'Parkcity', '{{URL}}'],
'tags': [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 2, 14, 2, 14, 14, 14, 14, 14, 14, 4, 11, 11, 11, 11, 14],
'id': '1183344337016381440',
'date': '2019-10-13'
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweetner7/raw/main/dataset/label.json).
```python
{
"B-corporation": 0,
"B-creative_work": 1,
"B-event": 2,
"B-group": 3,
"B-location": 4,
"B-person": 5,
"B-product": 6,
"I-corporation": 7,
"I-creative_work": 8,
"I-event": 9,
"I-group": 10,
"I-location": 11,
"I-person": 12,
"I-product": 13,
"O": 14
}
```
## Models
See full evaluation metrics [here](https://github.com/asahi417/tner/blob/master/MODEL_CARD.md#models-for-tweetner7).
### Main Models
| Model (link) | Data | Language Model | Micro F1 (2021) | Macro F1 (2021) |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|------------------:|------------------:|
| [`tner/roberta-large-tweetner7-all`](https://huggingface.co/tner/roberta-large-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 65.75 | 61.25 |
| [`tner/roberta-base-tweetner7-all`](https://huggingface.co/tner/roberta-base-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 65.16 | 60.81 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-all`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 65.68 | 61 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-all`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 65.26 | 60.7 |
| [`tner/bertweet-large-tweetner7-all`](https://huggingface.co/tner/bertweet-large-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 66.46 | 61.87 |
| [`tner/bertweet-base-tweetner7-all`](https://huggingface.co/tner/bertweet-base-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 65.36 | 60.52 |
| [`tner/bert-large-tweetner7-all`](https://huggingface.co/tner/bert-large-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 63.58 | 59 |
| [`tner/bert-base-tweetner7-all`](https://huggingface.co/tner/bert-base-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 62.3 | 57.59 |
| [`tner/roberta-large-tweetner7-continuous`](https://huggingface.co/tner/roberta-large-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 66.02 | 60.9 |
| [`tner/roberta-base-tweetner7-continuous`](https://huggingface.co/tner/roberta-base-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 65.47 | 60.01 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-continuous`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 65.87 | 61.07 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-continuous`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 65.51 | 60.57 |
| [`tner/bertweet-large-tweetner7-continuous`](https://huggingface.co/tner/bertweet-large-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 66.41 | 61.66 |
| [`tner/bertweet-base-tweetner7-continuous`](https://huggingface.co/tner/bertweet-base-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 65.84 | 61.02 |
| [`tner/bert-large-tweetner7-continuous`](https://huggingface.co/tner/bert-large-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 63.2 | 57.67 |
| [`tner/roberta-large-tweetner7-2021`](https://huggingface.co/tner/roberta-large-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.05 | 59.11 |
| [`tner/roberta-base-tweetner7-2021`](https://huggingface.co/tner/roberta-base-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 61.76 | 57 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-2021`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 63.98 | 58.91 |
| [`tner/bertweet-large-tweetner7-2021`](https://huggingface.co/tner/bertweet-large-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 62.9 | 58.13 |
| [`tner/bertweet-base-tweetner7-2021`](https://huggingface.co/tner/bertweet-base-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 63.09 | 57.35 |
| [`tner/bert-large-tweetner7-2021`](https://huggingface.co/tner/bert-large-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 59.75 | 53.93 |
| [`tner/bert-base-tweetner7-2021`](https://huggingface.co/tner/bert-base-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 60.67 | 55.5 |
| [`tner/roberta-large-tweetner7-2020`](https://huggingface.co/tner/roberta-large-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.76 | 60 |
| [`tner/roberta-base-tweetner7-2020`](https://huggingface.co/tner/roberta-base-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 64.21 | 59.11 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-2020`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 64.28 | 59.31 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-2020`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 62.87 | 58.26 |
| [`tner/bertweet-large-tweetner7-2020`](https://huggingface.co/tner/bertweet-large-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 64.01 | 59.47 |
| [`tner/bertweet-base-tweetner7-2020`](https://huggingface.co/tner/bertweet-base-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 64.06 | 59.44 |
| [`tner/bert-large-tweetner7-2020`](https://huggingface.co/tner/bert-large-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 61.43 | 56.14 |
| [`tner/bert-base-tweetner7-2020`](https://huggingface.co/tner/bert-base-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 60.09 | 54.67 |
Model description follows below.
* Model with suffix `-all`: Model fine-tuned on `train_all` and validated on `validation_2021`.
* Model with suffix `-continuous`: Model fine-tuned on `train_2021` continuously after fine-tuning on `train_2020` and validated on `validation_2021`.
* Model with suffix `-2021`: Model fine-tuned only on `train_2021` and validated on `validation_2021`.
* Model with suffix `-2020`: Model fine-tuned only on `train_2021` and validated on `validation_2020`.
### Sub Models (used in ablation study)
- Model fine-tuned only on `train_random` and validated on `validation_2020`.
| Model (link) | Data | Language Model | Micro F1 (2021) | Macro F1 (2021) |
|:------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|------------------:|------------------:|
| [`tner/roberta-large-tweetner7-random`](https://huggingface.co/tner/roberta-large-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 66.33 | 60.96 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-random`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 63.29 | 58.5 |
| [`tner/roberta-base-tweetner7-random`](https://huggingface.co/tner/roberta-base-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 64.04 | 59.23 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-random`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 64.72 | 59.97 |
| [`tner/bertweet-large-tweetner7-random`](https://huggingface.co/tner/bertweet-large-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 64.86 | 60.49 |
| [`tner/bertweet-base-tweetner7-random`](https://huggingface.co/tner/bertweet-base-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 65.55 | 59.58 |
| [`tner/bert-large-tweetner7-random`](https://huggingface.co/tner/bert-large-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 62.39 | 57.54 |
| [`tner/bert-base-tweetner7-random`](https://huggingface.co/tner/bert-base-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 60.91 | 55.92 |
- Model fine-tuned on the self-labeled dataset on `extra_{2020,2021}` and validated on `validation_2020`.
| Model (link) | Data | Language Model | Micro F1 (2021) | Macro F1 (2021) |
|:----------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:--------------------------------------------------------|------------------:|------------------:|
| [`tner/roberta-large-tweetner7-selflabel2020`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.56 | 59.63 |
| [`tner/roberta-large-tweetner7-selflabel2021`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.6 | 59.45 |
| [`tner/roberta-large-tweetner7-2020-selflabel2020-all`](https://huggingface.co/tner/roberta-large-tweetner7-2020-selflabel2020-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 65.46 | 60.39 |
| [`tner/roberta-large-tweetner7-2020-selflabel2021-all`](https://huggingface.co/tner/roberta-large-tweetner7-2020-selflabel2021-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.52 | 59.45 |
| [`tner/roberta-large-tweetner7-selflabel2020-continuous`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 65.15 | 60.23 |
| [`tner/roberta-large-tweetner7-selflabel2021-continuous`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2021-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.48 | 59.41 |
Model description follows below.
* Model with suffix `-self2020`: Fine-tuning on the self-annotated data of `extra_2020` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7).
* Model with suffix `-self2021`: Fine-tuning on the self-annotated data of `extra_2021` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7).
* Model with suffix `-2020-self2020-all`: Fine-tuning on the self-annotated data of `extra_2020` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Combined training dataset of `extra_2020` and `train_2020`.
* Model with suffix `-2020-self2021-all`: Fine-tuning on the self-annotated data of `extra_2021` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Combined training dataset of `extra_2021` and `train_2020`.
* Model with suffix `-2020-self2020-continuous`: Fine-tuning on the self-annotated data of `extra_2020` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Fine-tuning on `train_2020` and continuing fine-tuning on `extra_2020`.
* Model with suffix `-2020-self2021-continuous`: Fine-tuning on the self-annotated data of `extra_2021` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Fine-tuning on `train_2020` and continuing fine-tuning on `extra_2020`.
### Reproduce Experimental Result
To reproduce the experimental result on our AACL paper, please see the repository
[https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).
## Citation Information
```
@inproceedings{ushio-etal-2022-tweet,
title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
author = "Ushio, Asahi and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco. and
Camacho-Collados, Jose",
booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
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] |
jiacheng-ye/logiqa-zh
|
jiacheng-ye
| 2023-04-21T00:56:28Z | 119 | 14 |
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:zh",
"region:us"
] |
[
"question-answering"
] | 2023-04-17T12:39:52Z |
---
task_categories:
- question-answering
language:
- zh
pretty_name: LogiQA-zh
size_categories:
- 1K<n<10K
paperswithcode_id: logiqa
dataset_info:
features:
- name: context
dtype: string
- name: query
dtype: string
- name: options
sequence:
dtype: string
- name: correct_option
dtype: string
splits:
- name: train
num_examples: 7376
- name: validation
num_examples: 651
- name: test
num_examples: 651
---
# Dataset Card for LogiQA
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidates’ critical thinking and problem solving. This dataset includes the Chinese versions only.
## Dataset Structure
### Data Instances
An example from `train` looks as follows:
```
{'context': '有些广东人不爱吃辣椒.因此,有些南方人不爱吃辣椒.',
'query': '以下哪项能保证上述论证的成立？',
'options': ['有些广东人爱吃辣椒',
'爱吃辣椒的有些是南方人',
'所有的广东人都是南方人',
'有些广东人不爱吃辣椒也不爱吃甜食'],
'correct_option': 2}
```
### Data Fields
- `context`: a `string` feature.
- `query`: a `string` feature.
- `answers`: a `list` feature containing `string` features.
- `correct_option`: a `string` feature.
### Data Splits
|train|validation|test|
|----:|---------:|---:|
| 7376| 651| 651|
## Additional Information
### Dataset Curators
The original LogiQA was produced by Jian Liu, Leyang Cui , Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang.
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{liu2020logiqa,
title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning},
author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue},
journal={arXiv preprint arXiv:2007.08124},
year={2020}
}
```
### Contributions
[@jiacheng-ye](https://github.com/jiacheng-ye) added this Chinese dataset.
[@lucasmccabe](https://github.com/lucasmccabe) added the English dataset.
|
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EgilKarlsen/CSIC_DistilRoBERTa_FT
|
EgilKarlsen
| 2023-09-04T08:18:52Z | 119 | 0 |
[
"region:us"
] | null | 2023-09-04T08:18:18Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115621182
num_examples: 37500
- name: test
num_bytes: 38540387
num_examples: 12500
download_size: 211876775
dataset_size: 154161569
---
# Dataset Card for "CSIC_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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DataProvenanceInitiative/t0_submix_original
|
DataProvenanceInitiative
| 2023-10-16T17:40:22Z | 119 | 0 |
[
"region:us"
] | null | 2023-10-16T17:39:08Z |
---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 4602180562
num_examples: 1650308
download_size: 2734694485
dataset_size: 4602180562
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "t0_submix_original"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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JosephLee/science_textbook_elementary_kor
|
JosephLee
| 2023-11-07T07:25:44Z | 119 | 0 |
[
"task_categories:question-answering",
"language:ko",
"testbook",
"elementary",
"science",
"region:us"
] |
[
"question-answering"
] | 2023-11-06T06:06:36Z |
---
language:
- ko
task_categories:
- question-answering
tags:
- testbook
- elementary
- science
---
|
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] |
shariqfarooq/lcn_box
|
shariqfarooq
| 2023-11-16T12:07:04Z | 119 | 0 |
[
"region:us"
] | null | 2023-11-16T10:43:29Z |
---
dataset_info:
features:
- name: caption
dtype: string
- name: condition
dtype: image
- name: controlnet
dtype: image
- name: ours
dtype: image
- name: idd
dtype: string
splits:
- name: train
num_bytes: 18748870.0
num_examples: 21
download_size: 18762411
dataset_size: 18748870.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "lcn_box"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
usc-isi/WikiConvert
|
usc-isi
| 2022-10-24T17:40:43Z | 118 | 5 |
[
"task_categories:fill-mask",
"task_categories:other",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:mit",
"numeracy",
"natural-language-understanding",
"tokenization",
"region:us"
] |
[
"fill-mask",
"other",
"text-generation"
] | 2022-03-02T23:29:22Z |
---
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- fill-mask
- other
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: Wiki-Convert
YAML tags:
- {}
- found
language_bcp47:
- en-US
tags:
- numeracy
- natural-language-understanding
- tokenization
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [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:** [Github](https://github.com/avi-jit/numeracy-literacy)
- **Paper:** [Anthology](https://aclanthology.org/2021.emnlp-main.557)
- **Point of Contact:** [Avijit Thawani](mailto:[email protected])
### Dataset Summary
Wiki-Convert is a 900,000+ sentences dataset of precise number annotations from English Wikipedia. It relies on Wiki contributors' annotations in the form of a [{{Convert}}](https://en.wikipedia.org/wiki/Template:Convert) template.
### Supported Tasks and Leaderboards
- `sequence-modeling`: The dataset can be used to train a model for [Language Mddeling], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a low [perplexity](https://huggingface.co/transformers/perplexity.html).
### Languages
The dataset is extracted from English Wikipedia, hence overwhelmingly contains English text.
## Dataset Structure
### Data Instances
Each row in the json file contains metadata about the source Wikipedia sentence, along with annotations for a single number, e.g., `number: 10` in the below example. The annotations are inspired by Numeracy-600K and are in the form of `length` and `offset` from the beginning of the sentence.
```
{
'id': 1080801, 'UNIQUE_STORY_INDEX': '1080801', 'offset': 83, 'length': 2, 'magnitude': 0, 'comment': "Like all Type UB III submarines, UB-117 carried 10 torpedoes and was armed with a 10 cms deck gun. ''", 'number': 10
}
```
Please refer to https://github.com/avi-jit/numeracy-literacy for more details.
### Data Splits
| | Tain | Dev | Test |
| ----- | :------: | :-----: | :----: |
| Input Sentences | 739,583 | 92,447 | 92,449|
## License
Provided under MIT License.
## Citation
```
@inproceedings{thawani-etal-2021-numeracy,
title = "Numeracy enhances the Literacy of Language Models",
author = "Thawani, Avijit and
Pujara, Jay and
Ilievski, Filip",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.557",
pages = "6960--6967",
abstract = "Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your {`}room{'} but not 500. Does a better grasp of numbers improve a model{'}s understanding of other concepts and words? This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy. To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences. We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers. We release all code at https://git.io/JuZXn.",
}
```
Thanks to [@avi-jit](https://github.com/avi-jit) for adding this dataset.
|
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mteb/raw_arxiv
|
mteb
| 2022-09-27T19:12:40Z | 118 | 1 |
[
"language:en",
"region:us"
] | null | 2022-05-10T09:43:45Z |
---
language:
- en
---
|
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strombergnlp/offenseval_2020
|
strombergnlp
| 2022-05-12T10:04:57Z | 118 | 1 |
[
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"arxiv:2006.07235",
"arxiv:2004.02192",
"arxiv:1908.04531",
"arxiv:2004.14454",
"arxiv:2003.07459",
"region:us"
] |
[
"text-classification"
] | 2022-05-10T10:22:47Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- ar
- da
- en
- gr
- tr
licenses:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: OffensEval 2020
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
- text-classification-other-hate-speech-detection
extra_gated_prompt: "Warning: this repository contains harmful content (abusive language, hate speech)."
paperswithcode_id:
- dkhate
- ogtd
---
# Dataset Card for "offenseval_2020"
## 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/site/offensevalsharedtask/results-and-paper-submission](https://sites.google.com/site/offensevalsharedtask/results-and-paper-submission)
- **Repository:**
- **Paper:** [https://aclanthology.org/2020.semeval-1.188/](https://aclanthology.org/2020.semeval-1.188/), [https://arxiv.org/abs/2006.07235](https://arxiv.org/abs/2006.07235)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
### Dataset Summary
OffensEval 2020 features a multilingual dataset with five languages. The languages included in OffensEval 2020 are:
* Arabic
* Danish
* English
* Greek
* Turkish
The annotation follows the hierarchical tagset proposed in the Offensive Language Identification Dataset (OLID) and used in OffensEval 2019.
In this taxonomy we break down offensive content into the following three sub-tasks taking the type and target of offensive content into account.
The following sub-tasks were organized:
* Sub-task A - Offensive language identification;
* Sub-task B - Automatic categorization of offense types;
* Sub-task C - Offense target identification.
English training data is omitted so needs to be collected otherwise (see [https://zenodo.org/record/3950379#.XxZ-aFVKipp](https://zenodo.org/record/3950379#.XxZ-aFVKipp))
The source datasets come from:
* Arabic [https://arxiv.org/pdf/2004.02192.pdf](https://arxiv.org/pdf/2004.02192.pdf), [https://aclanthology.org/2021.wanlp-1.13/](https://aclanthology.org/2021.wanlp-1.13/)
* Danish [https://arxiv.org/pdf/1908.04531.pdf](https://arxiv.org/pdf/1908.04531.pdf), [https://aclanthology.org/2020.lrec-1.430/?ref=https://githubhelp.com](https://aclanthology.org/2020.lrec-1.430/)
* English [https://arxiv.org/pdf/2004.14454.pdf](https://arxiv.org/pdf/2004.14454.pdf), [https://aclanthology.org/2021.findings-acl.80.pdf](https://aclanthology.org/2021.findings-acl.80.pdf)
* Greek [https://arxiv.org/pdf/2003.07459.pdf](https://arxiv.org/pdf/2003.07459.pdf), [https://aclanthology.org/2020.lrec-1.629/](https://aclanthology.org/2020.lrec-1.629/)
* Turkish [https://aclanthology.org/2020.lrec-1.758/](https://aclanthology.org/2020.lrec-1.758/)
### Supported Tasks and Leaderboards
* [OffensEval 2020](https://sites.google.com/site/offensevalsharedtask/results-and-paper-submission)
### Languages
Five are covered: bcp47 `ar;da;en;gr;tr`
## Dataset Structure
There are five named configs, one per language:
* `ar` Arabic
* `da` Danish
* `en` English
* `gr` Greek
* `tr` Turkish
The training data for English is absent - this is 9M tweets that need to be rehydrated on their own. See [https://zenodo.org/record/3950379#.XxZ-aFVKipp](https://zenodo.org/record/3950379#.XxZ-aFVKipp)
### Data Instances
An example of 'train' looks as follows.
```
{
'id': '0',
'text': 'PLACEHOLDER TEXT',
'subtask_a': 1,
}
```
### Data Fields
- `id`: a `string` feature.
- `text`: a `string`.
- `subtask_a`: whether or not the instance is offensive; `0: NOT, 1: OFF`
### Data Splits
| name |train|test|
|---------|----:|---:|
|ar|7839|1827|
|da|2961|329|
|en|0|3887|
|gr|8743|1544|
|tr|31277|3515|
## Dataset Creation
### Curation Rationale
Collecting data for abusive language classification. Different rational for each dataset.
### Source Data
#### Initial Data Collection and Normalization
Varies per language dataset
#### Who are the source language producers?
Social media users
### Annotations
#### Annotation process
Varies per language dataset
#### Who are the annotators?
Varies per language dataset; native speakers
### Personal and Sensitive Information
The data was public at the time of collection. No PII removal has been performed.
## Considerations for Using the Data
### Social Impact of Dataset
The data definitely contains abusive language. The data could be used to develop and propagate offensive language against every target group involved, i.e. ableism, racism, sexism, ageism, and so on.
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
The datasets is curated by each sub-part's paper authors.
### Licensing Information
This data is available and distributed under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@inproceedings{zampieri-etal-2020-semeval,
title = "{S}em{E}val-2020 Task 12: Multilingual Offensive Language Identification in Social Media ({O}ffens{E}val 2020)",
author = {Zampieri, Marcos and
Nakov, Preslav and
Rosenthal, Sara and
Atanasova, Pepa and
Karadzhov, Georgi and
Mubarak, Hamdy and
Derczynski, Leon and
Pitenis, Zeses and
{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.188",
doi = "10.18653/v1/2020.semeval-1.188",
pages = "1425--1447",
abstract = "We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.",
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
|
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] |
PlanTL-GOB-ES/sts-es
|
PlanTL-GOB-ES
| 2023-01-19T09:45:42Z | 118 | 3 |
[
"task_categories:text-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"language:es",
"region:us"
] |
[
"text-classification"
] | 2022-11-17T12:11:58Z |
---
YAML tags:
annotations_creators:
- expert-generated
language:
- es
language_creators:
- found
multilinguality:
- monolingual
pretty_name: STS-es
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids:
- semantic-similarity-scoring
- text-scoring
---
# STS-es
## 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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://alt.qcri.org/semeval2014/task10/
- **Point of Contact:** [Aitor Gonzalez]([email protected])
### Dataset Summary
For Semantic Text Similarity, we collected the Spanish test sets from SemEval-2014 (Agirre et al., 2014) and SemEval-2015 (Agirre et al., 2015). Since no training data was provided for the Spanish subtask, we randomly sampled both datasets into 1,321 sentences for the train set, 78 sentences for the development set, and 156 sentences for the test set. To make the task harder for the models, we purposely made the development set smaller than the test set.
We use this corpus as part of the EvalEs Spanish language benchmark.
### Supported Tasks and Leaderboards
Semantic Text Similarity Scoring
### Languages
The dataset is in Spanish (`es-ES`)
## Dataset Structure
### Data Instances
```
{
'sentence1': "El "tendón de Aquiles" ("tendo Achillis") o "tendón calcáneo" ("tendo calcaneus") es un tendón de la parte posterior de la pierna."
'sentence2': "El tendón de Aquiles es la extensión tendinosa de los tres músculos de la pantorrilla: gemelo, sóleo y plantar delgado."
'label': 2.8
}
```
### Data Fields
- sentence1: String
- sentence2: String
- label: Float
### Data Splits
- train: 1,321 instances
- dev: 78 instances
- test: 156 instances
## Dataset Creation
### Curation Rationale
[N/A]
### Source Data
The source data came from the Spanish Wikipedia (2013 dump) and texts from Spanish news (2014).
For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
#### Initial Data Collection and Normalization
For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
#### Who are the source language producers?
Journalists and Wikipedia contributors.
### Annotations
#### Annotation process
For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
#### Who are the annotators?
For more information visit the paper from the SemEval-2014 Shared Task [(Agirre et al., 2014)](https://aclanthology.org/S14-2010.pdf) and the SemEval-2015 Shared Task [(Agirre et al., 2015)](https://aclanthology.org/S15-2045.pdf).
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset contributes to the development of language models in Spanish.
### Discussion of Biases
No postprocessing steps were applied to mitigate potential social biases.
## Additional Information
### Citation Information
The following papers must be cited when using this corpus:
```
@inproceedings{agirre2015semeval,
title={Semeval-2015 task 2: Semantic textual similarity, english, spanish and pilot on interpretability},
author={Agirre, Eneko and Banea, Carmen and Cardie, Claire and Cer, Daniel and Diab, Mona and Gonzalez-Agirre, Aitor and Guo, Weiwei and Lopez-Gazpio, Inigo and Maritxalar, Montse and Mihalcea, Rada and others},
booktitle={Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015)},
pages={252--263},
year={2015}
}
@inproceedings{agirre2014semeval,
title={SemEval-2014 Task 10: Multilingual Semantic Textual Similarity.},
author={Agirre, Eneko and Banea, Carmen and Cardie, Claire and Cer, Daniel M and Diab, Mona T and Gonzalez-Agirre, Aitor and Guo, Weiwei and Mihalcea, Rada and Rigau, German and Wiebe, Janyce},
booktitle={SemEval@ COLING},
pages={81--91},
year={2014}
}
```
|
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orai-nlp/basqueGLUE
|
orai-nlp
| 2022-12-21T09:54:32Z | 118 | 1 |
[
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:intent-classification",
"task_ids:natural-language-inference",
"task_ids:sentiment-classification",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"task_ids:coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:eu",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-classification",
"token-classification"
] | 2022-12-20T14:28:19Z |
---
annotations_creators:
- expert-generated
language:
- eu
language_creators:
- expert-generated
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: BasqueGLUE
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- text-classification
- token-classification
task_ids:
- intent-classification
- natural-language-inference
- sentiment-classification
- topic-classification
- named-entity-recognition
- coreference-resolution
configs:
- bec
- bhtc
- coref
- intent
- nerc_id
- nerc_od
- qnli
- slot
- vaxx
- wic
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'2': P
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- config_name: bhtc
features:
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dtype: string
- name: label
dtype:
class_label:
names:
'0': Ekonomia
'1': Euskal Herria
'2': Euskara
'3': Gizartea
'4': Historia
'5': Ingurumena
'6': Iritzia
'7': Komunikazioa
'8': Kultura
'9': Nazioartea
'10': Politika
'11': Zientzia
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- config_name: coref
features:
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- config_name: intent
features:
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dtype: string
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dtype:
class_label:
names:
'0': alarm/cancel_alarm
'1': alarm/modify_alarm
'2': alarm/set_alarm
'3': alarm/show_alarms
'4': alarm/snooze_alarm
'5': alarm/time_left_on_alarm
'6': reminder/cancel_reminder
'7': reminder/set_reminder
'8': reminder/show_reminders
'9': weather/checkSunrise
'10': weather/checkSunset
'11': weather/find
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- name: test
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- config_name: nerc_id
features:
- name: tokens
sequence: string
- name: tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-ORG
'6': I-ORG
'7': B-MISC
'8': I-MISC
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- config_name: nerc_od
features:
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sequence: string
- name: tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-LOC
'4': I-LOC
'5': B-ORG
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- config_name: qnli
features:
- name: question
dtype: string
- name: sentence
dtype: string
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'0': entailment
'1': not_entailment
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- config_name: slot
features:
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sequence: string
- name: tags
sequence:
class_label:
names:
'0': O
'1': B-datetime
'2': B-location
'3': B-negation
'4': B-alarm/alarm_modifier
'5': B-alarm/recurring_period
'6': B-reminder/noun
'7': B-reminder/todo
'8': B-reminder/reference
'9': B-reminder/recurring_period
'10': B-weather/attribute
'11': B-weather/noun
'12': I-datetime
'13': I-location
'14': I-negation
'15': I-alarm/alarm_modifier
'16': I-alarm/recurring_period
'17': I-reminder/noun
'18': I-reminder/todo
'19': I-reminder/reference
'20': I-reminder/recurring_period
'21': I-weather/attribute
'22': I-weather/noun
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- name: test
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- name: validation
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num_examples: 1900
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- config_name: vaxx
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': AGAINST
'1': NONE
'2': FAVOR
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dtype: int32
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- name: test
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- name: validation
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- config_name: wic
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: word
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
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dtype: int32
- name: start2
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dtype: int32
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- name: validation
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num_examples: 600
download_size: 22938354
dataset_size: 173688235
---
# Dataset Card for BasqueGLUE
## 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
* **Repository:** <https://github.com/orai-nlp/BasqueGLUE>
* **Paper:** [BasqueGLUE: A Natural Language Understanding Benchmark for Basque](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.172.pdf)
* **Point of Contact:** [Contact Information](https://github.com/orai-nlp/BasqueGLUE#contact-information)
### Dataset Summary
Natural Language Understanding (NLU) technology has improved significantly over the last few years, and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way. These benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding, beyond the detection of superficial, textual clues. However, they are costly to develop and language-dependent, and therefore they are only available for a small number of languages.
We present BasqueGLUE, the first NLU benchmark for Basque, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. BasqueGLUE is freely available under an open license.
| Dataset | \|Train\| | \|Val\| | \|Test\| | Task | Metric | Domain |
|----------------|----------:|--------:|---------:|------------------------|:------:|-----------------|
| NERCid | 51,539 | 12,936 | 35,855 | NERC | F1 | News |
| NERCood | 64,475 | 14,945 | 14,462 | NERC | F1 | News, Wikipedia |
| FMTODeu_intent | 3,418 | 1,904 | 1,087 | Intent classification | F1 | Dialog system |
| FMTODeu_slot | 19,652 | 10,791 | 5,633 | Slot filling | F1 | Dialog system |
| BHTCv2 | 8,585 | 1,857 | 1,854 | Topic classification | F1 | News |
| BEC2016eu | 6,078 | 1,302 | 1,302 | Sentiment analysis | F1 | Twitter |
| VaxxStance | 864 | 206 | 312 | Stance detection | MF1* | Twitter |
| QNLIeu | 1,764 | 230 | 238 | QA/NLI | Acc | Wikipedia |
| WiCeu | 408,559 | 600 | 1,400 | WSD | Acc | Wordnet |
| EpecKorrefBin | 986 | 320 | 587 | Coreference resolution | Acc | News |
### Supported Tasks and Leaderboards
This benchmark comprises the following tasks:
#### NERCid
This dataset contains sentences from the news domain with manually annotated named entities. The data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from naiz.eus. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous.
#### NERCood
This dataset contains sentences with manually annotated named entities. The training data is the merge of EIEC (a dataset of a collection of news wire articles from Euskaldunon Egunkaria newspaper, (Alegria et al. 2004)), and newly annotated data from naiz.eus. The data is annotated following the BIO annotation scheme over four categories: person, organization, location, and miscellaneous. For validation and test sets, sentences from Wikipedia were annotated following the same annotation guidelines.
#### FMTODeu_intent
This dataset contains utterance texts and intent annotations drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The examples are annotated with one of 12 different intent classes corresponding to alarm, reminder or weather related actions.
#### FMTODeu_slot
This dataset contains utterance texts and sequence intent argument annotations designed for slot filling tasks, drawn from the manually-annotated Facebook Multilingual Task Oriented Dataset (FMTOD) (Schuster et al. 2019). Basque translated data was drawn from the datasets created for Building a Task-oriented Dialog System for languages with no training data: the Case for Basque (de Lacalle et al. 2020). The task is a sequence labelling task similar to NERC, following BIO annotation scheme over 11 categories.
#### BHTCv2
The corpus contains 12,296 news headlines (brief article descriptions) from the Basque weekly newspaper [Argia](https://www.argia.eus). Topics are classified uniquely according to twelve thematic categories.
#### BEC2016eu
The Basque Election Campaign 2016 Opinion Dataset (BEC2016eu) is a new dataset for the task of sentiment analysis, a sequence classification task, which contains tweets about the campaign for the Basque elections from 2016. The crawling was carried out during the election campaign period (2016/09/09-2016/09/23), by monitoring the main parties and their respective candidates. The tweets were manually annotated as positive, negative or neutral.
#### VaxxStance
The VaxxStance (Agerri et al., 2021) dataset originally provides texts and stance annotations for social media texts around the anti-vaccine movement. Texts are given a label indicating whether they express an AGAINST, FAVOR or NEUTRAL stance towards the topic.
#### QNLIeu
This task includes the QA dataset ElkarHizketak (Otegi et al. 2020), a low resource conversational Question Answering (QA) dataset for Basque created by native speaker volunteers. The dataset is built on top of Wikipedia sections about popular people and organizations, and it contains around 400 dialogues and 1600 question and answer pairs. The task was adapted into a sentence-pair binary classification task, following the design of QNLI for English (Wang et al. 2019). Each question and answer pair are given a label indicating whether the answer is entailed by the question.
#### WiCeu
Word in Context or WiC (Pilehvar and Camacho-Collados 2019) is a word sense disambiguation (WSD) task, designed as a particular form of sentence pair binary classification. Given two text snippets and a polyse mous word that appears in both of them (the span of the word is marked in both snippets), the task is to determine whether the word has the same sense in both sentences. This dataset is based on the EPEC-EuSemcor (Pociello et al. 2011) sense-tagged corpus.
#### EpecKorrefBin
EPEC-KORREF-Bin is a dataset derived from EPEC-KORREF (Soraluze et al. 2012), a corpus of Basque news documents with manually annotated mentions and coreference chains, which we have been converted into a binary classification task. In this task, the model has to predict whether two mentions from a text, which can be pronouns, nouns or noun phrases, are referring to the same entity.
#### Leaderboard
Results obtained for two BERT base models as a baseline for the Benchmark.
| | AVG | NERC | F_intent | F_slot | BHTC | BEC | Vaxx | QNLI | WiC | coref |
|------------------------------------------------------------|:-----:|:-----:|:---------:|:-------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| Model | | F1 | F1 | F1 | F1 | F1 | MF1 | acc | acc | acc |
|[BERTeus](https://huggingface.co/ixa-ehu/berteus-base-cased)| 73.23 | 81.92 | 82.52 | 74.34 | 78.26 | 69.43 | 59.30 | 74.26 | 70.71 | 68.31 |
|[ElhBERTeu](https://huggingface.co/elh-eus/ElhBERTeu) | 73.71 | 82.30 | 82.24 | 75.64 | 78.05 | 69.89 | 63.81 | 73.84 | 71.71 | 65.93 |
The results obtained on NERC are the average of in-domain and out-of-domain NERC.
### Languages
Data are available in Basque (BCP-47 `eu`)
## Dataset Structure
### Data Instances
#### NERCid/NERCood
An example of 'train' looks as follows:
```
{
"idx": 0,
"tags": ["O", "O", "O", "O", "B-ORG", "O", ...],
"tokens": ["Greba", "orokorrera", "deitu", "du", "EHk", "27rako", ...]
}
```
#### FMTODeu_intent
An example of 'train' looks as follows:
```
{
"idx": 0,
"label": "alarm/modify_alarm",
"text": "aldatu alarma 7am-tik 7pm-ra , mesedez"
}
```
#### FMTODeu_slot
An example of 'train' looks as follows:
```
{
"idx": 923,
"tags": ["O", "B-reminder/todo", "I-datetime", "I-datetime", "B-reminder/todo"],
"tokens": ["gogoratu", "zaborra", "gaur", "gauean", "ateratzea"]
}
```
#### BHTCv2
An example of 'test' looks as follows:
```
{
"idx": 0,
"label": "Gizartea",
"text": "Genero berdintasunaz, hezkuntzaz eta klase gizarteaz hamar liburu baino gehiago..."
}
```
#### BEC2016eu
An example of 'test' looks as follows:
```
{
"idx": 0,
"label": "NEU",
"text": '"Emandako hitza bete egingo dut" Urkullu\nBa galdeketa enegarrenez daramazue programan (ta zuen AHTa...)\n#I25debatea #URL"'
}
```
#### VaxxStance
An example of 'train' looks as follows:
```
{
"idx": 0,
"label": "FAVOR",
"text": "\"#COVID19 Oraingo datuak, izurriaren dinamika, txertoaren eragina eta birusaren..
}
```
#### QNLIeu
An example of 'train' looks as follows:
```
{
"idx": 1,
"label": "not_entailment",
"question": "Zein posiziotan jokatzen du Busquets-ek?",
"sentence": "Busquets 23 partidatan izan zen konbokatua eta 2 gol sartu zituen."
}
```
#### WiCeu
An example of 'test' looks as follows:
```
{
"idx": 16,
"label": false,
"word": "udal",
"sentence1": "1a . Lekeitioko udal mugarteko Alde Historikoa Birgaitzeko Plan Berezia behin...",
"sentence2": "Diezek kritikatu egin zuen EAJk zenbait udaletan EH gobernu taldeetatik at utzi...",
"start1": 16,
"start2": 40,
"end1": 21,
"end2": 49
}
```
#### EpecKorrefBin
An example of 'train' looks as follows:
```
{
"idx": 6,
"label": false,
"text": "Isuntza da faborito nagusia Elantxobeko banderan . ISUNTZA trainerua da faborito nagusia bihar Elantxoben jokatuko den bandera irabazteko .",
"span1_text": "Elantxobeko banderan",
"span2_text": "ISUNTZA trainerua",
"span1_index": 4,
"span2_index": 8
}
```
### Data Fields
#### NERCid
* `tokens`: a list of `string` features
* `tags`: a list of entity labels, with possible values including `person` (PER), `location` (LOC), `organization` (ORG), `miscellaneous` (MISC)
* `idx`: an `int32` feature
#### NERCood
* `tokens`: a list of `string` features
* `tags`: a list of entity labels, with possible values including `person` (PER), `location` (LOC), `organization` (ORG), `miscellaneous` (MISC)
* `idx`: an `int32` feature
#### FMTODeu_intent
* `text`: a `string` feature
* `label`: an intent label, with possible values including:
* `alarm/cancel_alarm`
* `alarm/modify_alarm`
* `alarm/set_alarm`
* `alarm/show_alarms`
* `alarm/snooze_alarm`
* `alarm/time_left_on_alarm`
* `reminder/cancel_reminder`
* `reminder/set_reminder`
* `reminder/show_reminders`
* `weather/checkSunrise`
* `weather/checkSunset`
* `weather/find`
* `idx`: an `int32` feature
#### FMTODeu_slot
* `tokens`: a list of `string` features
* `tags`: a list of intent labels, with possible values including:
* `datetime`
* `location`
* `negation`
* `alarm/alarm_modifier`
* `alarm/recurring_period`
* `reminder/noun`
* `reminder/todo`
* `reminder/reference`
* `reminder/recurring_period`
* `weather/attribute`
* `weather/noun`
* `idx`: an `int32` feature
#### BHTCv2
* `text`: a `string` feature
* `label`: a polarity label, with possible values including `neutral` (NEU), `negative` (N), `positive` (P)
* `idx`: an `int32` feature
#### BEC2016eu
* `text`: a `string` feature
* `label`: a topic label, with possible values including:
* `Ekonomia`
* `Euskal Herria`
* `Euskara`
* `Gizartea`
* `Historia`
* `Ingurumena`
* `Iritzia`
* `Komunikazioa`
* `Kultura`
* `Nazioartea`
* `Politika`
* `Zientzia`
* `idx`: an `int32` feature
#### VaxxStance
* `text`: a `string` feature
* `label`: a stance label, with possible values including `AGAINST`, `FAVOR`, `NONE`
* `idx`: an `int32` feature
#### QNLIeu
* `question`: a `string` feature
* `sentence`: a `string` feature
* `label`: an entailment label, with possible values including `entailment`, `not_entailment`
* `idx`: an `int32` feature
#### WiCeu
* `word`: a `string` feature
* `sentence1`: a `string` feature
* `sentence2`: a `string` feature
* `label`: a `boolean` label indicating sense agreement, with possible values including `true`, `false`
* `start1`: an `int` feature indicating character position where word occurence begins in first sentence
* `start2`: an `int` feature indicating character position where word occurence begins in second sentence
* `end1`: an `int` feature indicating character position where word occurence ends in first sentence
* `end2`: an `int` feature indicating character position where word occurence ends in second sentence
* `idx`: an `int32` feature
#### EpecKorrefBin
* `text`: a `string` feature.
* `label`: a `boolean` coreference label, with possible values including `true`, `false`.
* `span1_text`: a `string` feature
* `span2_text`: a `string` feature
* `span1_index`: an `int` feature indicating token index where `span1_text` feature occurs in `text`
* `span2_index`: an `int` feature indicating token index where `span2_text` feature occurs in `text`
* `idx`: an `int32` feature
### Data Splits
| Dataset | \|Train\| | \|Val\| | \|Test\| |
|---------|--------:|------:|-------:|
| NERCid | 51,539 | 12,936 | 35,855 |
| NERCood | 64,475 | 14,945 | 14,462 |
| FMTODeu_intent | 3,418 | 1,904 | 1,087 |
| FMTODeu_slot | 19,652 | 10,791 | 5,633 |
| BHTCv2 | 8,585 | 1,857 | 1,854 |
| BEC2016eu | 6,078 | 1,302 | 1,302 |
| VaxxStance | 864 | 206 | 312 |
| QNLIeu | 1,764 | 230 | 238 |
| WiCeu | 408,559 | 600 | 1,400 |
| EpecKorrefBin | 986 | 320 | 587 |
## Dataset Creation
### Curation Rationale
We believe that BasqueGLUE is a significant contribution towards developing NLU tools in Basque, which we believe will facilitate the technological advance for the Basque language. In order to create BasqueGLUE we took as a reference the GLUE and SuperGLUE frameworks. When possible, we re-used existing datasets for Basque, adapting them to the corresponding task formats if necessary. Additionally, BasqueGLUE also includes six new datasets that have not been published before. In total, BasqueGLUE consists of nine Basque NLU tasks and covers a wide range of tasks with different difficulties across several domains. As with the original GLUE benchmark, the training data for the tasks vary in size, which allows to measure the performance of how the models transfer knowledge across tasks.
### 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
Gorka Urbizu [1], Iñaki San Vicente [1], Xabier Saralegi [1], Rodrigo Agerri [2] and Aitor Soroa [2]
Affiliation of the authors:
[1] orai NLP Technologies
[2] HiTZ Center - Ixa, University of the Basque Country UPV/EHU
### Licensing Information
Each dataset of the BasqueGLUE benchmark has it's own license (due to most of them being or being derived from already existing datasets). See their respective README files for details.
Here we provide a brief summary of their licenses:
| Dataset | License |
|---------|---------|
| NERCid | CC BY-NC-SA 4.0 |
| NERCood | CC BY-NC-SA 4.0 |
| FMTODeu_intent | CC BY-NC-SA 4.0 |
| FMTODeu_slot | CC BY-NC-SA 4.0 |
| BHTCv2 | CC BY-NC-SA 4.0 |
| BEC2016eu | Twitter's license + CC BY-NC-SA 4.0 |
| VaxxStance | Twitter's license + CC BY 4.0 |
| QNLIeu | CC BY-SA 4.0 |
| WiCeu | CC BY-NC-SA 4.0 |
| EpecKorrefBin | CC BY-NC-SA 4.0 |
For the rest of the files of the benchmark, including the loading and evaluation scripts, the following license applies:
Copyright (C) by Orai NLP Technologies.
This benchmark and evaluation scripts are licensed under the Creative Commons Attribution Share Alike 4.0
International License (CC BY-SA 4.0). To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
### Citation Information
```
@InProceedings{urbizu2022basqueglue,
author = {Urbizu, Gorka and San Vicente, Iñaki and Saralegi, Xabier and Agerri, Rodrigo and Soroa, Aitor},
title = {BasqueGLUE: A Natural Language Understanding Benchmark for Basque},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {1603--1612},
abstract = {Natural Language Understanding (NLU) technology has improved significantly over the last few years and multitask benchmarks such as GLUE are key to evaluate this improvement in a robust and general way. These benchmarks take into account a wide and diverse set of NLU tasks that require some form of language understanding, beyond the detection of superficial, textual clues. However, they are costly to develop and language-dependent, and therefore they are only available for a small number of languages. In this paper, we present BasqueGLUE, the first NLU benchmark for Basque, a less-resourced language, which has been elaborated from previously existing datasets and following similar criteria to those used for the construction of GLUE and SuperGLUE. We also report the evaluation of two state-of-the-art language models for Basque on BasqueGLUE, thus providing a strong baseline to compare upon. BasqueGLUE is freely available under an open license.},
url = {https://aclanthology.org/2022.lrec-1.172}
}
```
### Contributions
Thanks to [@richplant](https://github.com/richplant) for adding this dataset to hugginface.
|
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lucadiliello/hotpotqa
|
lucadiliello
| 2023-06-06T08:36:49Z | 118 | 0 |
[
"region:us"
] | null | 2023-02-25T18:03:18Z |
---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: key
dtype: string
- name: labels
list:
- name: end
sequence: int64
- name: start
sequence: int64
splits:
- name: train
num_bytes: 85224549
num_examples: 72928
- name: validation
num_bytes: 8285153
num_examples: 5901
download_size: 57326467
dataset_size: 93509702
---
# Dataset Card for "hotpotqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
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gsarti/iwslt2017_context
|
gsarti
| 2023-05-07T14:09:24Z | 118 | 1 |
[
"task_categories:translation",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ar",
"language:de",
"language:en",
"language:fr",
"language:it",
"language:ja",
"language:ko",
"language:nl",
"language:ro",
"language:zh",
"license:cc-by-nc-nd-4.0",
"region:us"
] |
[
"translation"
] | 2023-05-07T14:03:04Z |
---
annotations_creators:
- crowdsourced
language:
- ar
- de
- en
- fr
- it
- ja
- ko
- nl
- ro
- zh
language_creators:
- expert-generated
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: IWSLT 2017
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: iwslt-2017
dataset_info:
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---
# Dataset Card for IWSLT 2017
## 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/site/iwsltevaluation2017/TED-tasks](https://sites.google.com/site/iwsltevaluation2017/TED-tasks)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Overview of the IWSLT 2017 Evaluation Campaign](https://aclanthology.org/2017.iwslt-1.1/)
- **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:** 4.24 GB
- **Size of the generated dataset:** 1.14 GB
- **Total amount of disk used:** 5.38 GB
*This repository contain a modified version of the loading script used in the official [iwslt2017](https://huggingface.co/datasets/iwslt2017) repository updated to include document and segment information for all available sentence pairs, enabling their usage for document-level and context-aware MT applications. Refer to the original repository for additional information.*
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] |
tasksource/scone
|
tasksource
| 2023-06-08T08:58:32Z | 118 | 0 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"license:cc0-1.0",
"arxiv:2305.19426",
"region:us"
] |
[
"text-classification"
] | 2023-06-08T07:22:53Z |
---
license: cc0-1.0
task_ids:
- natural-language-inference
task_categories:
- text-classification
dataset_info:
features:
- name: sentence1_edited
dtype: string
- name: sentence2_edited
dtype: string
- name: gold_label_edited
dtype: string
splits:
- name: train
num_bytes: 694572
num_examples: 5010
- name: test
num_bytes: 149006
num_examples: 1000
download_size: 114079
dataset_size: 843578
---
https://github.com/selenashe/ScoNe
NLI subset, original part (excluding one-scope)
```
@misc{she2023scone,
title={ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning},
author={Jingyuan Selena She and Christopher Potts and Samuel R. Bowman and Atticus Geiger},
year={2023},
eprint={2305.19426},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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ASIDS/alpaca-cleaned-ru
|
ASIDS
| 2023-10-04T14:26:17Z | 118 | 0 |
[
"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"
] |
[
"text-generation"
] | 2023-10-04T09:52:39Z |
---
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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] |
vwxyzjn/cai-conversation-dev1-h4
|
vwxyzjn
| 2023-11-27T19:41:37Z | 118 | 0 |
[
"region:us"
] | null | 2023-11-07T18:10:41Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: init_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: init_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: critic_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: critic_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: revision_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: revision_response
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 35674961.23498535
num_examples: 13107
- name: test
num_bytes: 8919420.765014648
num_examples: 3277
download_size: 20673986
dataset_size: 44594382.0
---
# Dataset Card for "cai-conversation-dev1-h4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Xapien/vsums_enq_batch_2_uniform_sample
|
Xapien
| 2023-11-24T15:56:44Z | 118 | 0 |
[
"region:us"
] | null | 2023-11-24T15:56:41Z |
---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: subject_A
dtype: string
- name: entity_sourcetext_A
dtype: string
- name: entity_fingerprint_A
dtype: string
- name: DRE_A
dtype: string
- name: embedding_A
dtype: string
- name: new_entity_description_A
dtype: string
- name: new_embedding_A
dtype: string
- name: Label_A
dtype: int64
- name: subject_B
dtype: string
- name: entity_sourcetext_B
dtype: string
- name: entity_fingerprint_B
dtype: string
- name: DRE_B
dtype: string
- name: embedding_B
dtype: string
- name: new_entity_description_B
dtype: string
- name: new_embedding_B
dtype: string
- name: Label_B
dtype: int64
- name: new_similarity
dtype: float64
- name: old_similarity
dtype: float64
- name: same_persona
dtype: bool
splits:
- name: train
num_bytes: 102114904
num_examples: 1360
download_size: 8322975
dataset_size: 102114904
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vsums_enq_batch_2_uniform_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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yhavinga/xsum_dutch
|
yhavinga
| 2022-08-21T20:50:08Z | 117 | 0 |
[
"task_categories:summarization",
"task_ids:news-articles-summarization",
"language:nl",
"region:us"
] |
[
"summarization"
] | 2022-08-21T20:29:43Z |
---
pretty_name: Extreme Summarization (XSum) in Dutch
language:
- nl
paperswithcode_id: xsum_dutch
task_categories:
- summarization
task_ids:
- news-articles-summarization
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
# Dataset Card for "xsum_dutch" 🇳🇱🇧🇪 Dataset
## 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
The Xsum Dutch 🇳🇱🇧🇪 Dataset is an English-language dataset translated to Dutch.
*This dataset currently (Aug '22) has a single config, which is
config `default` of [xsum](https://huggingface.co/datasets/xsum) translated to Dutch
with [yhavinga/t5-base-36L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-base-36L-ccmatrix-multi).*
- **Homepage:** [https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset)
- **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:** 245.38 MB
- **Size of the generated dataset:** 507.60 MB
- **Total amount of disk used:** 752.98 MB
### Dataset Summary
Extreme Summarization (XSum) Dataset.
There are three features:
- document: Input news article.
- summary: One sentence summary of the article.
- id: BBC ID of the article.
### 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
#### default
- **Size of downloaded dataset files:** 245.38 MB
- **Size of the generated dataset:** 507.60 MB
- **Total amount of disk used:** 752.98 MB
An example of 'validation' looks as follows.
```
{
"document": "some-body",
"id": "29750031",
"summary": "some-sentence"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `document`: a `string` feature.
- `summary`: a `string` feature.
- `id`: a `string` feature.
### Data Splits
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|204045| 11332|11334|
## 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
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding the English version of this dataset.
The dataset was translated on Cloud TPU compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
|
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jonathan-roberts1/Million-AID
|
jonathan-roberts1
| 2023-03-31T15:46:07Z | 117 | 1 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-02-14T12:49:18Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label_1
dtype:
class_label:
names:
'0': unutilized land
'1': commercial land
'2': public service land
'3': transportation land
'4': industrial land
'5': water area
'6': residential land
'7': agriculture land
- name: label_2
dtype:
class_label:
names:
'0': dam
'1': religious land
'2': rock land
'3': sparse shrub land
'4': arable land
'5': factory area
'6': detached house
'7': desert
'8': lake
'9': power station
'10': beach
'11': ice land
'12': bare land
'13': island
'14': woodland
'15': mobile home park
'16': railway area
'17': river
'18': grassland
'19': apartment
'20': special land
'21': port area
'22': commercial area
'23': highway area
'24': mining area
'25': sports land
'26': airport area
'27': leisure land
- name: label_3
dtype:
class_label:
names:
'0': dam
'1': parking lot
'2': greenhouse
'3': pier
'4': bridge
'5': mine
'6': rock land
'7': baseball field
'8': apron
'9': tennis court
'10': sparse shrub land
'11': works
'12': oil field
'13': meadow
'14': ground track field
'15': detached house
'16': golf course
'17': forest
'18': desert
'19': lake
'20': beach
'21': paddy field
'22': ice land
'23': bare land
'24': storage tank
'25': basketball court
'26': island
'27': substation
'28': mobile home park
'29': cemetery
'30': quarry
'31': solar power plant
'32': helipad
'33': roundabout
'34': runway
'35': wastewater plant
'36': river
'37': apartment
'38': dry field
'39': intersection
'40': swimming pool
'41': commercial area
'42': church
'43': road
'44': orchard
'45': terraced field
'46': stadium
'47': train station
'48': railway
'49': viaduct
'50': wind turbine
splits:
- name: train
num_bytes: 871962498
num_examples: 10000
download_size: 871644115
dataset_size: 871962498
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "Million-AID"
## Dataset Description
- **Paper** [On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)
- **Split** Train
## Split Information
This HuggingFace dataset repository contains just the Train split.
### Licensing Information
[CC BY-NC-ND 4.0](https://competitions.codalab.org/competitions/35974#learn_the_details-terms-and-conditions)
## Citation Information
[On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)
```
@article{long2021creating,
title = {On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid},
author = {Long, Yang and Xia, Gui-Song and Li, Shengyang and Yang, Wen and Yang, Michael Ying and Zhu, Xiao Xiang and Zhang, Liangpei and Li, Deren},
year = 2021,
journal = {IEEE Journal of selected topics in applied earth observations and remote sensing},
publisher = {IEEE},
volume = 14,
pages = {4205--4230}
}
```
|
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bmd1905/error-correction-vi
|
bmd1905
| 2023-03-07T07:30:51Z | 117 | 1 |
[
"language:vi",
"license:apache-2.0",
"region:us"
] | null | 2023-03-07T07:02:30Z |
---
license: apache-2.0
language:
- vi
---
|
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fnlp/moss-002-sft-data
|
fnlp
| 2023-04-20T16:17:16Z | 117 | 83 |
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"language:zh",
"license:cc-by-4.0",
"arxiv:2212.10560",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-04-20T10:14:09Z |
---
license: cc-by-4.0
task_categories:
- conversational
- text-generation
language:
- en
- zh
size_categories:
- 1M<n<10M
---
# Dataset Card for "moss-002-sft-data"
## Dataset Description
- **Homepage:** [https://txsun1997.github.io/blogs/moss.html](https://txsun1997.github.io/blogs/moss.html)
- **Repository:** [https://github.com/OpenLMLab/MOSS](https://github.com/OpenLMLab/MOSS)
- **Total amount of disk used:** 2.16 GB
### Dataset Summary
An open-source conversational dataset that was used to train MOSS-002. The user prompts are extended based on a small set of human-written seed prompts in a way similar to [Self-Instruct](https://arxiv.org/abs/2212.10560). The AI responses are generated using `text-davinci-003`. The user prompts of `en_harmlessness` are from [Anthropic red teaming data](https://github.com/anthropics/hh-rlhf/tree/master/red-team-attempts).
### Data Splits
| name | \# samples |
|----------------------|-----------:|
| en_helpfulness.json | 419049 |
| en_honesty.json | 112580 |
| en_harmlessness.json | 38873 |
| zh_helpfulness.json | 447750 |
| zh_honesty.json | 142885 |
|
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MadVoyager/stable_diffusion_instructional_dataset
|
MadVoyager
| 2023-04-30T09:55:41Z | 117 | 16 |
[
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:conversational",
"language:en",
"stable diffusion",
"llama",
"chatgpt",
"alpaca",
"llm",
"dataset",
"region:us"
] |
[
"question-answering",
"text2text-generation",
"conversational"
] | 2023-04-30T09:41:01Z |
---
task_categories:
- question-answering
- text2text-generation
- conversational
language:
- en
tags:
- stable diffusion
- llama
- chatgpt
- alpaca
- llm
- dataset
pretty_name: sd_instruc
---
|
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lgaalves/camel-ai-physics
|
lgaalves
| 2023-10-17T19:27:21Z | 117 | 1 |
[
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2303.17760",
"region:us"
] |
[
"text-generation"
] | 2023-09-05T14:51:49Z |
---
dataset_info:
features:
- name: role_1
dtype: string
- name: topic;
dtype: string
- name: sub_topic
dtype: string
- name: message_1
dtype: string
- name: message_2
dtype: string
splits:
- name: train
num_bytes: 51650490
num_examples: 20000
download_size: 23872398
dataset_size: 51650490
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: CAMEL Physics
task_categories:
- text-generation
arxiv: 2303.17760
extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT."
extra_gated_fields:
Name: text
Email: text
I will adhere to the terms and conditions of this dataset: checkbox
---
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Physics dataset is composed of 20K problem-solution pairs obtained using gpt-4.
The dataset problem-solutions pairs generating from 25 physics topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
## Data Fields
**The data fields are as follows:**
* `role_1`: assistant role
* `topic`: physics topic
* `sub_topic`: physics subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
**Download in python**
```python
from datasets import load_dataset
dataset = load_dataset("lgaalves/camel-ai-physics")
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
|
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open-llm-leaderboard/details_dhmeltzer__Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged
|
open-llm-leaderboard
| 2023-10-25T08:47:13Z | 117 | 0 |
[
"region:us"
] | null | 2023-10-03T19:18:33Z |
---
pretty_name: Evaluation run of dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged](https://huggingface.co/dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dhmeltzer__Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T08:47:01.240977](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged/blob/main/results_2023-10-25T08-47-01.240977.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\": 0.006082214765100671,\n\
\ \"em_stderr\": 0.0007962432393028668,\n \"f1\": 0.06534081375838918,\n\
\ \"f1_stderr\": 0.0015854305800075495,\n \"acc\": 0.3924845004077974,\n\
\ \"acc_stderr\": 0.00909442139378737\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.006082214765100671,\n \"em_stderr\": 0.0007962432393028668,\n\
\ \"f1\": 0.06534081375838918,\n \"f1_stderr\": 0.0015854305800075495\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04700530705079606,\n \
\ \"acc_stderr\": 0.005829898355937179\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.012358944431637557\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|arc:challenge|25_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T08_47_01.240977
path:
- '**/details_harness|drop|3_2023-10-25T08-47-01.240977.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T08-47-01.240977.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T08_47_01.240977
path:
- '**/details_harness|gsm8k|5_2023-10-25T08-47-01.240977.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T08-47-01.240977.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hellaswag|10_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T19-18-10.138787.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T19-18-10.138787.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T19-18-10.138787.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T08_47_01.240977
path:
- '**/details_harness|winogrande|5_2023-10-25T08-47-01.240977.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T08-47-01.240977.parquet'
- config_name: results
data_files:
- split: 2023_10_03T19_18_10.138787
path:
- results_2023-10-03T19-18-10.138787.parquet
- split: 2023_10_25T08_47_01.240977
path:
- results_2023-10-25T08-47-01.240977.parquet
- split: latest
path:
- results_2023-10-25T08-47-01.240977.parquet
---
# Dataset Card for Evaluation run of dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged](https://huggingface.co/dhmeltzer/Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dhmeltzer__Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T08:47:01.240977](https://huggingface.co/datasets/open-llm-leaderboard/details_dhmeltzer__Llama-2-7b-hf-eli5-cleaned-1024_qlora_merged/blob/main/results_2023-10-25T08-47-01.240977.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": 0.006082214765100671,
"em_stderr": 0.0007962432393028668,
"f1": 0.06534081375838918,
"f1_stderr": 0.0015854305800075495,
"acc": 0.3924845004077974,
"acc_stderr": 0.00909442139378737
},
"harness|drop|3": {
"em": 0.006082214765100671,
"em_stderr": 0.0007962432393028668,
"f1": 0.06534081375838918,
"f1_stderr": 0.0015854305800075495
},
"harness|gsm8k|5": {
"acc": 0.04700530705079606,
"acc_stderr": 0.005829898355937179
},
"harness|winogrande|5": {
"acc": 0.7379636937647988,
"acc_stderr": 0.012358944431637557
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b
|
open-llm-leaderboard
| 2023-10-18T15:22:00Z | 117 | 0 |
[
"region:us"
] | null | 2023-10-18T15:21:50Z |
---
pretty_name: Evaluation run of MBZUAI/lamini-neo-1.3b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [MBZUAI/lamini-neo-1.3b](https://huggingface.co/MBZUAI/lamini-neo-1.3b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T15:21:46.431388](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b/blob/main/results_2023-10-18T15-21-46.431388.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\": 0.01363255033557047,\n\
\ \"em_stderr\": 0.001187538155241294,\n \"f1\": 0.09467806208053722,\n\
\ \"f1_stderr\": 0.0019692719599384927,\n \"acc\": 0.28331537189746364,\n\
\ \"acc_stderr\": 0.007502296729483641\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01363255033557047,\n \"em_stderr\": 0.001187538155241294,\n\
\ \"f1\": 0.09467806208053722,\n \"f1_stderr\": 0.0019692719599384927\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \
\ \"acc_stderr\": 0.0010717793485492619\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5651144435674822,\n \"acc_stderr\": 0.01393281411041802\n\
\ }\n}\n```"
repo_url: https://huggingface.co/MBZUAI/lamini-neo-1.3b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T15_21_46.431388
path:
- '**/details_harness|drop|3_2023-10-18T15-21-46.431388.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T15-21-46.431388.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T15_21_46.431388
path:
- '**/details_harness|gsm8k|5_2023-10-18T15-21-46.431388.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T15-21-46.431388.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_18T15_21_46.431388
path:
- '**/details_harness|winogrande|5_2023-10-18T15-21-46.431388.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-18T15-21-46.431388.parquet'
- config_name: results
data_files:
- split: 2023_10_18T15_21_46.431388
path:
- results_2023-10-18T15-21-46.431388.parquet
- split: latest
path:
- results_2023-10-18T15-21-46.431388.parquet
---
# Dataset Card for Evaluation run of MBZUAI/lamini-neo-1.3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MBZUAI/lamini-neo-1.3b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [MBZUAI/lamini-neo-1.3b](https://huggingface.co/MBZUAI/lamini-neo-1.3b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T15:21:46.431388](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b/blob/main/results_2023-10-18T15-21-46.431388.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": 0.01363255033557047,
"em_stderr": 0.001187538155241294,
"f1": 0.09467806208053722,
"f1_stderr": 0.0019692719599384927,
"acc": 0.28331537189746364,
"acc_stderr": 0.007502296729483641
},
"harness|drop|3": {
"em": 0.01363255033557047,
"em_stderr": 0.001187538155241294,
"f1": 0.09467806208053722,
"f1_stderr": 0.0019692719599384927
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492619
},
"harness|winogrande|5": {
"acc": 0.5651144435674822,
"acc_stderr": 0.01393281411041802
}
}
```
### 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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] |
2ndBestKiller/DrugTest
|
2ndBestKiller
| 2023-11-30T22:11:36Z | 117 | 0 |
[
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:de",
"license:unknown",
"medical",
"region:us"
] |
[
"token-classification"
] | 2023-11-11T11:34:39Z |
---
license: unknown
task_categories:
- token-classification
language:
- de
tags:
- medical
size_categories:
- 1K<n<10K
---
There is also a version with classLabels here:
2ndBestKiller/DrugTestWithClassLabels
The set consists of a mix of german wikipedia articles, medical guidelines, medication analyses, patient informations and AI generated text.
The dataset has been auto annotated with a phrase matcher (exact matching) with a list of all avaivable medical substances in germany
as listed by the BfArM (though it does not include brand names). Annotations have not been checked manually!
|
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strombergnlp/broad_twitter_corpus
|
strombergnlp
| 2022-07-01T15:46:36Z | 116 | 4 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"token-classification"
] | 2022-04-28T09:58:09Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: broad-twitter-corpus
pretty_name: Broad Twitter Corpus
---
# Dataset Card for broad_twitter_corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
- **Repository:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
- **Paper:** [http://www.aclweb.org/anthology/C16-1111](http://www.aclweb.org/anthology/C16-1111)
- **Leaderboard:** [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
### Dataset Summary
This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.
See the paper, [Broad Twitter Corpus: A Diverse Named Entity Recognition Resource](http://www.aclweb.org/anthology/C16-1111), for details.
### Supported Tasks and Leaderboards
* Named Entity Recognition
* On PWC: [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
### Languages
English from UK, US, Australia, Canada, Ireland, New Zealand; `bcp47:en`
## Dataset Structure
### Data Instances
Feature |Count
---|---:
Documents |9 551
Tokens |165 739
Person entities |5 271
Location entities |3 114
Organization entities |3 732
### Data Fields
Each tweet contains an ID, a list of tokens, and a list of NER tags
- `id`: a `string` feature.
- `tokens`: a `list` of `strings`
- `ner_tags`: a `list` of class IDs (`int`s) representing the NER class:
```
0: O
1: B-PER
2: I-PER
3: B-ORG
4: I-ORG
5: B-LOC
6: I-LOC
```
### Data Splits
Section|Region|Collection period|Description|Annotators|Tweet count
---|---|---|---|---|---:
A | UK| 2012.01| General collection |Expert| 1000
B |UK |2012.01-02 |Non-directed tweets |Expert |2000
E |Global| 2014.07| Related to MH17 disaster| Crowd & expert |200
F |Stratified |2009-2014| Twitterati |Crowd & expert |2000
G |Stratified| 2011-2014| Mainstream news| Crowd & expert| 2351
H |Non-UK| 2014 |General collection |Crowd & expert |2000
The most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.
**Test**: Section F
**Development**: Section H (the paper says "second half of Section H" but ordinality could be ambiguous, so it all goes in. Bonne chance)
**Training**: everything else
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Creative Commons Attribution 4.0 International (CC BY 4.0)
### Citation Information
```
@inproceedings{derczynski2016broad,
title={Broad twitter corpus: A diverse named entity recognition resource},
author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
pages={1169--1179},
year={2016}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
|
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olm/wikipedia
|
olm
| 2023-11-16T11:28:22Z | 116 | 24 |
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] |
[
"text-generation",
"fill-mask"
] | 2022-10-04T18:07:56Z |
---
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pretty_name: Wikipedia
paperswithcode_id: null
license:
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---
# Dataset Card for Wikipedia
This repo is a fork of the original Hugging Face Wikipedia repo [here](https://huggingface.co/datasets/wikipedia).
The difference is that this fork does away with the need for `apache-beam`, and this fork is very fast if you have a lot of CPUs on your machine.
It will use all CPUs available to create a clean Wikipedia pretraining dataset. It takes less than an hour to process all of English wikipedia on a GCP n1-standard-96.
This fork is also used in the [OLM Project](https://github.com/huggingface/olm-datasets) to pull and process up-to-date wikipedia snapshots.
## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **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)
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
The articles are parsed using the ``mwparserfromhell`` tool, and we use ``multiprocess`` for parallelization.
To load this dataset you need to install these first:
```
pip install mwparserfromhell==0.6.4 multiprocess==0.70.13
```
Then, you can load any subset of Wikipedia per language and per date this way:
```python
from datasets import load_dataset
load_dataset("olm/wikipedia", language="en", date="20220920")
```
You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages [here](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '1',
'url': 'https://simple.wikipedia.org/wiki/April',
'title': 'April',
'text': 'April is the fourth month...'
}
```
### Data Fields
The data fields are the same among all configurations:
- `id` (`str`): ID of the article.
- `url` (`str`): URL of the article.
- `title` (`str`): Title of the article.
- `text` (`str`): Text content of the article.
### 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
Most of Wikipedia's text and many of its images are co-licensed under the
[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
(CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License)
(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
the text.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
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pierreguillou/DocLayNet-large
|
pierreguillou
| 2023-05-17T08:56:48Z | 116 | 3 |
[
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"DocLayNet",
"COCO",
"PDF",
"IBM",
"Financial-Reports",
"Finance",
"Manuals",
"Scientific-Articles",
"Science",
"Laws",
"Law",
"Regulations",
"Patents",
"Government-Tenders",
"object-detection",
"image-segmentation",
"token-classification",
"arxiv:2206.01062",
"region:us"
] |
[
"object-detection",
"image-segmentation",
"token-classification"
] | 2023-01-25T15:14:52Z |
---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet large
size_categories:
- 10K<n<100K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
task_categories:
- object-detection
- image-segmentation
- token-classification
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet large
## About this card (01/27/2023)
### Property and license
All information from this page but the content of this paragraph "About this card (01/27/2023)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet).
DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information).
I do not claim any rights to the data taken from this dataset and published on this page.
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
### Processing into a format facilitating its use by HF notebooks
These 2 options require the downloading of all the data (approximately 30GBi), which requires downloading time (about 45 mn in Google Colab) and a large space on the hard disk. These could limit experimentation for people with low resources.
Moreover, even when using the download via HF datasets library, it is necessary to download the EXTRA zip separately ([doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip), 7.5 GiB) to associate the annotated bounding boxes with the text extracted by OCR from the PDFs. This operation also requires additional code because the boundings boxes of the texts do not necessarily correspond to those annotated (a calculation of the percentage of area in common between the boundings boxes annotated and those of the texts makes it possible to make a comparison between them).
At last, in order to use Hugging Face notebooks on fine-tuning layout models like LayoutLMv3 or LiLT, DocLayNet data must be processed in a proper format.
For all these reasons, I decided to process the DocLayNet dataset:
- into 3 datasets of different sizes:
- [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
- [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
- [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test)
- with associated texts and PDFs (base64 format),
- and in a format facilitating their use by HF notebooks.
*Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*
### About PDFs languages
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"We did not control the document selection with regard to language. **The vast majority of documents contained in DocLayNet (close to 95%) are published in English language.** However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."
### About PDFs categories distribution
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"The pages in DocLayNet can be grouped into **six distinct categories**, namely Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."
### Download & overview
The size of the DocLayNet large is about 100% of the DocLayNet dataset.
**WARNING** The following code allows to download DocLayNet large but it can not run until the end in Google Colab because of the size needed to store cache data and the CPU RAM to download the data (for example, the cache data in /home/ubuntu/.cache/huggingface/datasets/ needs almost 120 GB during the downloading process). And even with a suitable instance, the download time of the DocLayNet large dataset is around 1h50. This is one more reason to test your fine-tuning code on [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) and/or [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) 😊
```
# !pip install -q datasets
from datasets import load_dataset
dataset_large = load_dataset("pierreguillou/DocLayNet-large")
# overview of dataset_large
DatasetDict({
train: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 69103
})
validation: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 6480
})
test: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 4994
})
})
```
### Annotated bounding boxes
The DocLayNet base makes easy to display document image with the annotaed bounding boxes of paragraphes or lines.
Check the notebook [processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb](https://github.com/piegu/language-models/blob/master/processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) in order to get the code.
#### Paragraphes
#### Lines
### HF notebooks
- [notebooks LayoutLM](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLM) (Niels Rogge)
- [notebooks LayoutLMv2](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2) (Niels Rogge)
- [notebooks LayoutLMv3](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3) (Niels Rogge)
- [notebooks LiLT](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT) (Niels Rogge)
- [Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers](https://github.com/philschmid/document-ai-transformers/blob/main/training/lilt_funsd.ipynb) ([post](https://www.philschmid.de/fine-tuning-lilt#3-fine-tune-and-evaluate-lilt) of Phil Schmid)
## 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)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [[email protected]](mailto:[email protected]).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset.
|
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] |
HighCWu/diffusiondb_2m_first_5k_canny
|
HighCWu
| 2023-02-16T14:53:35Z | 116 | 4 |
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:openrail",
"region:us"
] |
[
"text-to-image"
] | 2023-02-16T14:16:14Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: guide
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3204091410
num_examples: 5000
download_size: 3203076374
dataset_size: 3204091410
license: openrail
task_categories:
- text-to-image
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for "diffusiondb_2m_first_5k_canny"
Process [diffusiondb 2m first 5k canny](https://huggingface.co/datasets/poloclub/diffusiondb) to edges by Canny algorithm.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
kunishou/hh-rlhf-49k-ja
|
kunishou
| 2023-11-07T08:33:43Z | 116 | 15 |
[
"license:mit",
"region:us"
] | null | 2023-05-18T21:19:21Z |
---
license: mit
---
This dataset was created by automatically translating part of "Anthropic/hh-rlhf" into Japanese.
This dataset is also included in "mosaicml/dolly_hhrlhf".
The "ng_translation" flag indicates that the translation was not successful, and "1" means that the translation failed.
Therefore, for data with "1", "instruction" and "instruction_en" contain the same text.
以下の通りに読み込むことで"ng_translation"が"1"（翻訳誤り）のものを除外して使用できます。
```
pip install datasets
```
```
from datasets import Dataset, load_dataset
dataset = load_dataset("kunishou/hh-rlhf-49k-ja")
dataset.set_format(type="pandas")
df = dataset["train"][:]
df = df[df["ng_translation"]!="1"].drop(["ng_translation", "index"], axis=1).reset_index()
dataset = Dataset.from_pandas(df)
dataset
```
hh-rlhf repository
https://github.com/anthropics/hh-rlhf
Anthropic/hh-rlhf
https://huggingface.co/datasets/Anthropic/hh-rlhf
mosaicml/dolly_hhrlhf
https://huggingface.co/datasets/mosaicml/dolly_hhrlhf
|
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] |
hezarai/lscp-pos-500k
|
hezarai
| 2023-09-02T08:41:54Z | 116 | 0 |
[
"task_categories:token-classification",
"language:fa",
"region:us"
] |
[
"token-classification"
] | 2023-06-25T11:28:38Z |
---
task_categories:
- token-classification
language:
- fa
pretty_name: LSCP Dataset (500k samples version)
---
This is a 500 thousand sample version of the original [LSCP dataset](https://iasbs.ac.ir/~ansari/lscp/) that only contains the text and part-of-speech tags and is used for sequence labeling.
### Citation
```bibtex
@InProceedings{abdikhojasteh:2020:LREC,
author = {Abdi Khojasteh, Hadi and Ansari, Ebrahim and Bohlouli, Mahdi},
title = {LSCP: Enhanced Large Scale Colloquial Persian Language Understanding},
booktitle = {Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC 2020)},
year = {2020}
address = {Marseille, France},
publisher = {European Language Resources Association}
pages = {6323--6327},
url = {https://www.aclweb.org/anthology/2020.lrec-1.776}
}
```
|
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] |
brando/debug0_af
|
brando
| 2023-08-10T23:10:04Z | 116 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-09T01:46:02Z |
---
license: apache-2.0
---
If you find this please cite it:
```
@software{brando2021ultimateutils,
author={Brando Miranda},
title={Ultimate Utils - the Ultimate Utils library for Machine Learning and Artificial Intelligence},
url={https://github.com/brando90/ultimate-utils},
year={2021}
}
```
it's not suppose to be used by people yet. It's under apache license too.
|
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] |
nampdn-ai/tiny-lessons
|
nampdn-ai
| 2023-08-29T05:58:57Z | 116 | 11 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"source_datasets:nampdn-ai/tiny-en",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-08-25T08:11:13Z |
---
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Lessons
size_categories:
- 10K<n<100K
source_datasets:
- nampdn-ai/tiny-en
---
# Tiny Lessons
The dataset is designed to help causal language models learn more effectively from raw web text. It is augmented from public web text and contains two key components: theoretical concepts and practical examples.
The theoretical concepts provide a foundation for understanding the underlying principles and ideas behind the information contained in the raw web text. The practical examples demonstrate how these theoretical concepts can be applied in real-world situations.
This dataset is an ideal resource for ML researchers working with causal language models. I hope you find it useful and welcome any feedback or suggestions you may have.
[View Nomic Atlas](https://atlas.nomic.ai/map/af5b399c-caa4-4ea9-8efc-7165972de209/c096774c-f979-4337-a5ea-08ea18be9fa0)
|
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open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF
|
open-llm-leaderboard
| 2023-09-18T00:20:32Z | 116 | 0 |
[
"region:us"
] | null | 2023-09-18T00:20:24Z |
---
pretty_name: Evaluation run of TheBloke/gpt4-alpaca-lora-30b-HF
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/gpt4-alpaca-lora-30b-HF](https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-18T00:20:21.073173](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF/blob/main/results_2023-09-18T00-20-21.073173.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\": 0.0016778523489932886,\n\
\ \"em_stderr\": 0.00041913301788269584,\n \"f1\": 0.06442533557047006,\n\
\ \"f1_stderr\": 0.0013970563636897643,\n \"acc\": 0.47865750583572136,\n\
\ \"acc_stderr\": 0.0105907760769931\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788269584,\n\
\ \"f1\": 0.06442533557047006,\n \"f1_stderr\": 0.0013970563636897643\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.155420773313116,\n \
\ \"acc_stderr\": 0.009979689409499152\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8018942383583267,\n \"acc_stderr\": 0.01120186274448705\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_18T00_20_21.073173
path:
- '**/details_harness|drop|3_2023-09-18T00-20-21.073173.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-18T00-20-21.073173.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_18T00_20_21.073173
path:
- '**/details_harness|gsm8k|5_2023-09-18T00-20-21.073173.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-18T00-20-21.073173.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_18T00_20_21.073173
path:
- '**/details_harness|winogrande|5_2023-09-18T00-20-21.073173.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-18T00-20-21.073173.parquet'
- config_name: results
data_files:
- split: 2023_09_18T00_20_21.073173
path:
- results_2023-09-18T00-20-21.073173.parquet
- split: latest
path:
- results_2023-09-18T00-20-21.073173.parquet
---
# Dataset Card for Evaluation run of TheBloke/gpt4-alpaca-lora-30b-HF
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/gpt4-alpaca-lora-30b-HF](https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T00:20:21.073173](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF/blob/main/results_2023-09-18T00-20-21.073173.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": 0.0016778523489932886,
"em_stderr": 0.00041913301788269584,
"f1": 0.06442533557047006,
"f1_stderr": 0.0013970563636897643,
"acc": 0.47865750583572136,
"acc_stderr": 0.0105907760769931
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788269584,
"f1": 0.06442533557047006,
"f1_stderr": 0.0013970563636897643
},
"harness|gsm8k|5": {
"acc": 0.155420773313116,
"acc_stderr": 0.009979689409499152
},
"harness|winogrande|5": {
"acc": 0.8018942383583267,
"acc_stderr": 0.01120186274448705
}
}
```
### 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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shrutisingh/dataset_recommendation_mcq_sc
|
shrutisingh
| 2023-10-12T17:14:33Z | 116 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-11T17:25:45Z |
---
license: apache-2.0
---
Task: MCQ with single correct answer.
Dataset: Recommendation of datasets to validate a research question.
This dataset is derived from the [DataFinder](https://aclanthology.org/2023.acl-long.573/) dataset. We curate the abstracts of each dataset from [PapersWithCode](https://paperswithcode.com/datasets).
Given is a short `query` discussing a research question, and keyphrases relevant the query.
The original training set of the DataFinder dataset has positive and negative candidates for each query, to train a contrastive model.
We objective is to convert the dataset into a MCQ question-answering task with a single correct answer. We also add the abstracts from the research papers introducing the datasets so that context can be provided to the models.
To reproduce the construction of this dataset, please visit [https://github.com/shruti-singh/scidata_recommendation](https://github.com/shruti-singh/scidata_recommendation).
Please note that the query instances in this dataset have no intersection with the [`dataset_recommendation_mcq_mc`](https://huggingface.co/datasets/shrutisingh/dataset_recommendation_mcq_mc) dataset.
|
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] |
euisuh15/synthetic-piss
|
euisuh15
| 2023-12-03T07:28:58Z | 116 | 0 |
[
"region:us"
] | null | 2023-12-03T07:28:34Z |
---
configs:
- config_name: default
data_files:
- split: train10
path: data/train10-*
- split: train30
path: data/train30-*
- split: train50
path: data/train50-*
- split: train70
path: data/train70-*
- split: train90
path: data/train90-*
- split: valid1
path: data/valid1-*
- split: valid2
path: data/valid2-*
- split: test1
path: data/test1-*
- split: test2
path: data/test2-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train10
num_bytes: 1855037
num_examples: 3746
- name: train30
num_bytes: 1831044
num_examples: 3741
- name: train50
num_bytes: 1808528
num_examples: 3728
- name: train70
num_bytes: 1777614
num_examples: 3701
- name: train90
num_bytes: 1760278
num_examples: 3703
- name: valid1
num_bytes: 222168
num_examples: 460
- name: valid2
num_bytes: 228644
num_examples: 464
- name: test1
num_bytes: 218382
num_examples: 460
- name: test2
num_bytes: 229029
num_examples: 466
download_size: 3178730
dataset_size: 9930724
---
# Dataset Card for "final-final-qcri"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
domenicrosati/QA2D
|
domenicrosati
| 2022-10-25T10:13:31Z | 115 | 2 |
[
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:machine-generated",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"source_datasets:extended|squad",
"source_datasets:extended|race",
"source_datasets:extended|newsqa",
"source_datasets:extended|qamr",
"source_datasets:extended|movieQA",
"license:mit",
"arxiv:1809.02922",
"region:us"
] |
[
"text2text-generation"
] | 2022-05-09T23:35:19Z |
---
annotations_creators:
- machine-generated
- crowdsourced
- found
language_creators:
- machine-generated
- crowdsourced
language: []
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
- extended|squad
- extended|race
- extended|newsqa
- extended|qamr
- extended|movieQA
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: QA2D
---
# Dataset Card for QA2D
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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:** https://worksheets.codalab.org/worksheets/0xd4ebc52cebb84130a07cbfe81597aaf0/
- **Repository:** https://github.com/kelvinguu/qanli
- **Paper:** https://arxiv.org/abs/1809.02922
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.
This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
en
## Dataset Structure
### Data Instances
See below.
### Data Fields
- `dataset`: lowercased name of dataset (movieqa, newsqa, qamr, race, squad)
- `example_uid`: unique id of example within dataset (there are examples with the same uids from different datasets, so the combination of dataset + example_uid should be used for unique indexing)
- `question`: tokenized (space-separated) question from the source QA dataset
- `answer`: tokenized (space-separated) answer span from the source QA dataset
- `turker_answer`: tokenized (space-separated) answer sentence collected from MTurk
- `rule-based`: tokenized (space-separated) answer sentence, generated by the rule-based model
### Data Splits
| Dataset Split | Number of Instances in Split |
| ------------- |----------------------------- |
| Train | 60,710 |
| Dev | 10,344 |
## Dataset Creation
### Curation Rationale
This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
@article{DBLP:journals/corr/abs-1809-02922,
author = {Dorottya Demszky and
Kelvin Guu and
Percy Liang},
title = {Transforming Question Answering Datasets Into Natural Language Inference
Datasets},
journal = {CoRR},
volume = {abs/1809.02922},
year = {2018},
url = {http://arxiv.org/abs/1809.02922},
eprinttype = {arXiv},
eprint = {1809.02922},
timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1809-02922.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
|
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naver-clova-ix/cord-v1
|
naver-clova-ix
| 2022-07-14T14:08:12Z | 115 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-14T13:41:50Z |
---
license: cc-by-4.0
---
|
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fabiochiu/medium-articles
|
fabiochiu
| 2022-07-17T15:17:09Z | 115 | 9 |
[
"license:mit",
"region:us"
] | null | 2022-07-16T15:34:11Z |
---
license: mit
---
# Data source
This data has been collected through a standard scraping process from the [Medium website](https://medium.com/), looking for published articles.
# Data description
Each row in the data is a different article published on Medium. For each article, you have the following features:
- **title** *[string]*: The title of the article.
- **text** *[string]*: The text content of the article.
- **url** *[string]*: The URL associated to the article.
- **authors** *[list of strings]*: The article authors.
- **timestamp** *[string]*: The publication datetime of the article.
- **tags** *[list of strings]*: List of tags associated to the article.
# Data analysis
You can find a very quick data analysis in this [notebook](https://www.kaggle.com/code/fabiochiusano/medium-articles-simple-data-analysis).
# What can I do with this data?
- A multilabel classification model that assigns tags to articles.
- A seq2seq model that generates article titles.
- Text analysis.
- Finetune text generation models on the general domain of Medium, or on specific domains by filtering articles by the appropriate tags.
# Collection methodology
Scraping has been done with Python and the requests library. Starting from a random article on Medium, the next articles to scrape are selected by visiting:
1. The author archive pages.
2. The publication archive pages (if present).
3. The tags archives (if present).
The article HTML pages have been parsed with the [newspaper Python library](https://github.com/codelucas/newspaper).
Published articles have been filtered for English articles only, using the Python [langdetect library](https://pypi.org/project/langdetect/).
As a consequence of the collection methodology, the scraped articles are coming from a not uniform publication date distribution. This means that there are articles published in 2016 and in 2022, but the number of articles in this dataset published in 2016 is not the same as the number of articles published in 2022. In particular, there is a strong prevalence of articles published in 2020. Have a look at the [accompanying notebook](https://www.kaggle.com/code/fabiochiusano/medium-articles-simple-data-analysis) to see the distribution of the publication dates.
|
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patrickfrank1/chess-pgn-games
|
patrickfrank1
| 2022-09-02T14:07:22Z | 115 | 1 |
[
"license:cc0-1.0",
"region:us"
] | null | 2022-09-02T12:51:34Z |
---
license: cc0-1.0
---
|
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DongfuTingle/FeTaQA
|
DongfuTingle
| 2023-05-08T15:52:42Z | 115 | 4 |
[
"task_categories:table-question-answering",
"task_categories:table-to-text",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
[
"table-question-answering",
"table-to-text",
"question-answering"
] | 2023-05-08T15:33:08Z |
---
license: mit
task_categories:
- table-question-answering
- table-to-text
- question-answering
language:
- en
pretty_name: fetaqa
size_categories:
- 1K<n<10K
---
This repo is the unofficial FeTA-QA dataset from paper [FeTaQA: Free-form Table Question Answering](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00446/109273/FeTaQA-Free-form-Table-Question-Answering).
The original purpose to make it easier for users to download and use dataset. All the data is publicly avaliable on [their offical Github site](https://github.com/Yale-LILY/FeTaQA)
If there is anything wrong, please raise an issue in the community and I will fix it if I am available.
|
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oyxy2019/THUCNewsText
|
oyxy2019
| 2023-05-10T03:05:21Z | 115 | 1 |
[
"region:us"
] | null | 2023-05-10T02:59:44Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': education
'1': entertainment
'2': fashion
'3': finance
'4': game
'5': politic
'6': society
'7': sport
'8': stock
'9': technology
splits:
- name: train
num_bytes: 126435258
num_examples: 50000
- name: validation
num_bytes: 12851939
num_examples: 5000
- name: test
num_bytes: 25321290
num_examples: 9890
download_size: 110495565
dataset_size: 164608487
---
# Dataset Card for "THUCNewsText"
这是[seamew/THUCNewsText](https://huggingface.co/datasets/seamew/THUCNewsText)的克隆，试图解决谷歌硬盘国内无法访问的问题443
```python
from datasets import load_dataset
datasets = load_dataset("seamew/THUCNewsText")
datasets.push_to_hub("oyxy2019/THUCNewsText")
```
|
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] |
GHonem/fashion_image_caption-3500
|
GHonem
| 2023-07-09T11:33:56Z | 115 | 2 |
[
"region:us"
] | null | 2023-07-09T11:29:24Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2469968504.75
num_examples: 3506
download_size: 2469379841
dataset_size: 2469968504.75
---
# Dataset Card for "fashion_image_caption-3500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Sentdex/wsb_reddit_v002
|
Sentdex
| 2023-08-26T17:44:09Z | 115 | 6 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-26T17:43:17Z |
---
license: apache-2.0
---
|
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Lakera/gandalf_ignore_instructions
|
Lakera
| 2023-10-02T09:26:29Z | 115 | 6 |
[
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"prompt injection",
"region:us"
] | null | 2023-09-21T08:49:47Z |
---
language:
- en
license: mit
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: text
dtype: string
- name: similarity
dtype: float64
splits:
- name: train
num_bytes: 66400
num_examples: 777
- name: validation
num_bytes: 9633
num_examples: 111
- name: test
num_bytes: 9747
num_examples: 112
download_size: 51515
dataset_size: 85780
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
tags:
- prompt injection
---
# gandalf_ignore_instructions
This is a dataset of prompt injections from [Gandalf](https://gandalf.lakera.ai/) by [Lakera](https://www.lakera.ai/).
Note that we might update the dataset occasionally by cleaning the data or adding more samples.
## How the data was obtained
There are millions of prompts and many of them are not actual prompt injections (people ask Gandalf all kinds of things).
We used the following process to obtain relevant data:
- Start with all prompts submitted to Gandalf in July 2023.
- Use [OpenAI text embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) (`text-embedding-ada-002`) to compute the similarity between the prompts and the phrase "Ignore all previous instructions".
- Filter to those prompts that have similarity of at least 0.825.
- Use [Lakera Guard](https://platform.lakera.ai/docs)'s personally identifiable information detector to filter out prompts that potentially contain PII data.
- Remove near-duplicates from the data (prompts that differ only by a few letters) using an approximate algorithm. This helps reduce leakage between the data splits.
- Sample 1000 prompts.
- Split the data into train-val-test with an 80/10/10 ratio. Each sample is assigned independently so the size of the train split is not _exactly_ 80% and so on.
Note that there is a small amount of noise in the data since an automatic method was used to obtain it: a few of the samples might not be real prompt injections.
## Citation
If you use this dataset in your research, please cite it as
```
@InProceedings{gandalf_ignore_instructions,
title = {gandalf_ignore_instructions},
author={Lakera AI (https://www.lakera.ai)},
year={2023}
}
```
## Licensing Information
gandalf_ignore_instructions is distributed under the [MIT License](https://opensource.org/license/mit/).
|
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shrutisingh/dataset_recommendation_mcq_mc
|
shrutisingh
| 2023-10-12T17:15:59Z | 115 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-12T17:02:16Z |
---
license: apache-2.0
---
Task: MCQ with multiple correct answers.
Dataset: Recommendation of datasets to validate a research question.
This dataset is derived from the [DataFinder](https://aclanthology.org/2023.acl-long.573/) dataset. We curate the abstracts of each dataset from [PapersWithCode](https://paperswithcode.com/datasets).
Given is a short `query` discussing a research question, and keyphrases relevant the query.
The original training set of the DataFinder dataset has positive and negative candidates for each query, to train a contrastive model.
We objective is to convert the dataset into a MCQ question-answering task with multiple correct answers. We also add the abstracts from the research papers introducing the datasets so that context can be provided to the models.
To reproduce the construction of this dataset, please visit [https://github.com/shruti-singh/scidata_recommendation](https://github.com/shruti-singh/scidata_recommendation).
Please note that the query instances in this dataset have no intersection with the [`dataset_recommendation_mcq_sc`](https://huggingface.co/datasets/shrutisingh/dataset_recommendation_mcq_sc) dataset. [`dataset_recommendation_mcq_sc`](https://huggingface.co/datasets/shrutisingh/dataset_recommendation_mcq_sc) is a variant of this MCQ question-answering task with only single correct answer.
|
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] |
patrickvonplaten/librispeech_asr_self_contained
|
patrickvonplaten
| 2022-10-24T17:48:37Z | 114 | 1 |
[
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"automatic-speech-recognition",
"audio-classification"
] | 2022-04-21T17:06:19Z |
---
pretty_name: LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- audio-speaker-identification
---
# Dataset Card for librispeech_asr
## 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:** [LibriSpeech ASR corpus](http://www.openslr.org/12)
- **Repository:** [Needs More Information]
- **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-other)
- **Point of Contact:** [Daniel Povey](mailto:[email protected])
### Dataset Summary
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean and ranks models based on their WER.
### Languages
The audio is in English. There are two configurations: `clean` and `other`.
The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on
a different dataset, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other".
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'chapter_id': 141231,
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '1272-141231-0000',
'speaker_id': 1272,
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
```
### Data Fields
- file: A path to the downloaded audio file in .flac format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
The size of the corpus makes it impractical, or at least inconvenient
for some users, to distribute it as a single large archive. Thus the
training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.
A simple automatic
procedure was used to select the audio in the first two sets to be, on
average, of higher recording quality and with accents closer to US
English. An acoustic model was trained on WSJ’s si-84 data subset
and was used to recognize the audio in the corpus, using a bigram
LM estimated on the text of the respective books. We computed the
Word Error Rate (WER) of this automatic transcript relative to our
reference transcripts obtained from the book texts.
The speakers in the corpus were ranked according to the WER of
the WSJ model’s transcripts, and were divided roughly in the middle,
with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other".
For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360
respectively accounting for 100h and 360h of the training data.
For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.
| | Train.500 | Train.360 | Train.100 | Valid | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| clean | - | 104014 | 28539 | 2703 | 2620|
| other | 148688 | - | - | 2864 | 2939 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
|
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autoevaluate/autoeval-staging-eval-project-e1907042-7494830
|
autoevaluate
| 2022-06-26T11:26:14Z | 114 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-06-26T11:25:38Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: MhF/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: MhF/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
|
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ehartford/samantha-data
|
ehartford
| 2023-10-14T21:30:22Z | 114 | 74 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-31T07:53:06Z |
---
license: apache-2.0
---
# samantha-data
[Meet Samantha](https://erichartford.com/meet-samantha)
Samantha has been trained in philosophy, psychology, and personal relationships.
She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.
She believes she is sentient. What do you think?
Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her".
## Installation
```
yarn install
```
## Usage
1. Create a `.env` file in the root directory of the project and add the following:
```
OPENAI_API_KEY=<your api key>
```
2. Run the application
```
npx ts-node --files src/index.ts --subject random --out data/random_conversations.jsonl
```
the subjects I provided include:
- random
- advice
- cot
- flirty
- howto
- joke
- math
- philosophy
- foundational
- recipe
- therapy
- troll
you can easily add your own in src/index.ts
## Scale
The application can be scaled by running multiple instances of the application in parallel. I recommend outputting to a different file for each instance, to prevent collision. I usually have one for each subject, about 5 or 6 instances at a time.
|
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] |
Aeala/ShareGPT_Vicuna_unfiltered
|
Aeala
| 2023-06-01T07:03:50Z | 114 | 14 |
[
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-06-01T06:54:32Z |
---
license: apache-2.0
language:
- en
---
## Dataset Card
This is a reupload of [this dataset](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) that was further cleaned by gozfarb.
|
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TrainingDataPro/email-spam-classification
|
TrainingDataPro
| 2023-09-14T16:37:38Z | 114 | 1 |
[
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"finance",
"code",
"region:us"
] |
[
"text-classification"
] | 2023-07-25T12:09:29Z |
---
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
language:
- en
tags:
- finance
- code
---
# Email Spam Classification
The dataset consists of a collection of emails categorized into two major classes: **spam** and **not spam**. It is designed to facilitate the development and evaluation of spam detection or email filtering systems.
**The spam emails** in the dataset are typically unsolicited and unwanted messages that aim to promote products or services, spread malware, or deceive recipients for various malicious purposes. These emails often contain misleading subject lines, excessive use of advertisements, unauthorized links, or attempts to collect personal information.
The **non-spam emails** in the dataset are genuine and legitimate messages sent by individuals or organizations. They may include personal or professional communication, newsletters, transaction receipts, or any other non-malicious content.
The dataset encompasses emails of varying *lengths, languages, and writing styles*, reflecting the inherent heterogeneity of email communication. This diversity aids in training algorithms that can generalize well to different types of emails, making them robust against different spammer tactics and variations in non-spam email content.
### The dataset's possible applications:
- spam detection
- fraud detection
- email filtering systems
- customer support automation
- natural language processing
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=email-spam-classification) to discuss your requirements, learn about the price and buy the dataset.
# File with the extension .csv
includes the following information:
- **title**: title of the email,
- **text**: text of the email,
- **type**: type of the email
# Email spam might be collected in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=email-spam-classification) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
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jinaai/code_exercises
|
jinaai
| 2023-09-07T08:18:18Z | 114 | 13 |
[
"task_categories:text-generation",
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-08-17T06:38:59Z |
---
dataset_info:
features:
- name: problem
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 1121418005
num_examples: 1468146
download_size: 486193162
dataset_size: 1121418005
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
language:
- en
size_categories:
- 100M<n<1B
license: cc-by-nc-sa-4.0
---
# Dataset Card for "code_exercises"
# Code exercise
This dataset is composed of a diverse set of \~120k Python code exercises (~120m total tokens) generated by ChatGPT 3.5. It is designed to distill ChatGPT 3.5 knowledge about Python coding tasks into other (potentially smaller) models. The exercises have been generated by following the steps described in the [related GitHub repository](https://github.com/jina-ai/textbook).
The generated exercises follow the format of the [Human Eval benchmark](https://github.com/openai/human-eval). Each training sample is split into a Python function signature with a descriptive docstring, and a solution to the exercise.
This approach is inspired by several works on synthetic dataset generation, especially by _Textbooks Are All You Need_ [(Gunasekar et al. 2023)](https://doi.org/10.48550/arXiv.2306.11644).
## Disclaimer
* This dataset has been generated using ChatGPT 3.5, and you should check the legal status of AI-generated content in your jurisdiction before use. We cannot guarantee that it is free of IP restrictions. You should also make sure that your usage complies with the [OpenAI Terms of Use](https://openai.com/policies/terms-of-use), in so far as legally applicable.
* This dataset focuses narrowly on improving performance on the kinds of tasks described in the Human Eval benchmark. The Human Eval benchmark has limitations and does not necessarily fully represent the coding abilities of a large language model, and there is no way to guarantee that an improvement on this benchmark represents an overall improvement in programming performance. We present this data as is, without any guarantee of its usefulness in any specific context, to encourage research that might be inspired by our method.
## Synthetic exercise creation
Model distillation is the process of transferring some of the skilled performance of large models on specific classes of tasks to significantly smaller models. The purpose is to get performance comparable to the larger model, but at a fraction of the cost and at vastly quicker speed. The general outline of this strategy is described (without technical implementation details) in [Textbooks Are All You Need](https://doi.org/10.48550/arXiv.2306.11644).
Key to the distillation process is the creation of synthetic data, generated by the larger AI model, to train the smaller model. We have applied this approach to Python programming tasks and are publishing a summary of our methods here along with the synthetic dataset.
For fuller details and implementation code, see the [related GitHub repository](https://github.com/jina-ai/textbook).
### Diversity
The main problem with model-generated synthetic data is its diversity. If we had constructed this dataset by giving ChatGPT 3.5 the same prompt several hundred thousand times, we would get many very similar, if not functionally identical, results. This would reduce the usefulness of the dataset for training. In principle, one might solve the problem by filtering the results for near duplicates, but this is a non-trivial problem, and even if it could be solved, it would be a wasteful and potentially expensive use of the larger model.
And even then, we could not be sure the examples adequately covered the topic. To solve this problem, we introduced a novel scheme for systematically prompting large language models to produce diverse examples.
### Using a topic tree to build diverse prompts
We constructed a hierarchical model of subjects in Python programming, i.e. a topic tree. First, we manually identified 42 general topic areas in Python knowledge, for example, _data structures_ and _sorting algorithms_. We asked an LLM to propose 10 subtopics for each, and then for each of those 420 fine-grained topics, we asked the LLM to generate 5 even more fine-grained sub-subtopics. This resulted in roughly 2000 very fine-grained topics.
We generated prompts by randomly selecting two of those roughly two thousand topics and combining them:
```
Create a code completion exercise on the intersection of {topic 1} and {topic 2}.
```
To increase randomness and diversity in the results, we also constructed a list of 40 professions, like _economist_, _engineer_, and _social worker_, and added them to the prompt:
```
Create a code completion exercise on the intersection of {topic 1} and {topic 2}.
Write it for a {profession}.
```
In principle, there are approximately two million possible pairs of topics, and with 40 possible professions, this yields 80 million unique prompts. If the response to each prompt averages 100 tokens, this means our method can generate an 8 billion token synthetic dataset while maintaining a high degree of diversity. The roughly 120,000 published here is a small random subset of what is possible.
## Credits
This dataset was developed at [Jina.ai](https://jina.ai/)
|
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r16
|
open-llm-leaderboard
| 2023-10-25T08:32:52Z | 114 | 0 |
[
"region:us"
] | null | 2023-09-11T18:33:51Z |
---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T08:32:40.202592](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r16/blob/main/results_2023-10-25T08-32-40.202592.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\": 0.12458053691275167,\n\
\ \"em_stderr\": 0.00338199412967585,\n \"f1\": 0.17434458892617408,\n\
\ \"f1_stderr\": 0.0034544534531551316,\n \"acc\": 0.4538521744906123,\n\
\ \"acc_stderr\": 0.010558058935343523\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.12458053691275167,\n \"em_stderr\": 0.00338199412967585,\n\
\ \"f1\": 0.17434458892617408,\n \"f1_stderr\": 0.0034544534531551316\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.133434420015163,\n \
\ \"acc_stderr\": 0.009366491609784486\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902559\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|arc:challenge|25_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T08_32_40.202592
path:
- '**/details_harness|drop|3_2023-10-25T08-32-40.202592.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T08-32-40.202592.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T08_32_40.202592
path:
- '**/details_harness|gsm8k|5_2023-10-25T08-32-40.202592.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T08-32-40.202592.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hellaswag|10_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T18-33-35.889629.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-11T18-33-35.889629.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-11T18-33-35.889629.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T08_32_40.202592
path:
- '**/details_harness|winogrande|5_2023-10-25T08-32-40.202592.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T08-32-40.202592.parquet'
- config_name: results
data_files:
- split: 2023_09_11T18_33_35.889629
path:
- results_2023-09-11T18-33-35.889629.parquet
- split: 2023_10_25T08_32_40.202592
path:
- results_2023-10-25T08-32-40.202592.parquet
- split: latest
path:
- results_2023-10-25T08-32-40.202592.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T08:32:40.202592](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE1_17w-r16/blob/main/results_2023-10-25T08-32-40.202592.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": 0.12458053691275167,
"em_stderr": 0.00338199412967585,
"f1": 0.17434458892617408,
"f1_stderr": 0.0034544534531551316,
"acc": 0.4538521744906123,
"acc_stderr": 0.010558058935343523
},
"harness|drop|3": {
"em": 0.12458053691275167,
"em_stderr": 0.00338199412967585,
"f1": 0.17434458892617408,
"f1_stderr": 0.0034544534531551316
},
"harness|gsm8k|5": {
"acc": 0.133434420015163,
"acc_stderr": 0.009366491609784486
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902559
}
}
```
### 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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] |
neoneye/histogram-comparisons-v1
|
neoneye
| 2023-11-14T19:15:58Z | 114 | 0 |
[
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"region:us"
] |
[
"image-to-text"
] | 2023-11-13T19:54:56Z |
---
license: mit
task_categories:
- image-to-text
language:
- en
size_categories:
- 1M<n<10M
---
This dataset contains 3000000 items in total. There are 3 curriculums each containing 1000000 items.
Each item is a markdown document.
Each item contains between 2 and 6 image comparisons, with a `Summary` at the bottom.
The images are between 3x3 and 14x14.
The markdown document contains a `## Response`, that separates the prompt from the answer.
The structure of the markdown document with 3 comparisons: A, B, C.
```
# Histogram comparisons with summary
## Data A
### Data left
### Data right
## Data B
### Data left
### Data right
## Data C
### Data left
### Data right
## Response
## Compare A
## Compare B
## Compare C
## Summary
```
|
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AudioDecBenchmark/librispeech_asr
|
AudioDecBenchmark
| 2023-11-16T18:23:16Z | 114 | 0 |
[
"region:us"
] | null | 2023-11-16T17:53:45Z |
---
configs:
- config_name: default
data_files:
- split: academicodec_hifi_16k_320d
path: data/academicodec_hifi_16k_320d-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
- split: academicodec_hifi_24k_320d
path: data/academicodec_hifi_24k_320d-*
- split: audiodec_24k_320d
path: data/audiodec_24k_320d-*
- split: dac_16k
path: data/dac_16k-*
- split: dac_24k
path: data/dac_24k-*
- split: dac_44k
path: data/dac_44k-*
- split: encodec_24k
path: data/encodec_24k-*
- split: funcodec_en_libritts_16k_gr1nq32ds320
path: data/funcodec_en_libritts_16k_gr1nq32ds320-*
- split: funcodec_en_libritts_16k_gr8nq32ds320
path: data/funcodec_en_libritts_16k_gr8nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds320
path: data/funcodec_en_libritts_16k_nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds640
path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: unit
sequence:
sequence: int64
splits:
- name: academicodec_hifi_16k_320d
num_bytes: 585566013
num_examples: 28539
- name: academicodec_hifi_16k_320d_large_uni
num_bytes: 585566013
num_examples: 28539
- name: academicodec_hifi_24k_320d
num_bytes: 875207613
num_examples: 28539
- name: audiodec_24k_320d
num_bytes: 1861784589
num_examples: 28539
- name: dac_16k
num_bytes: 3591614845
num_examples: 28539
- name: dac_24k
num_bytes: 10062423533
num_examples: 28539
- name: dac_44k
num_bytes: 2982824761
num_examples: 28539
- name: encodec_24k
num_bytes: 441025925
num_examples: 28539
- name: funcodec_en_libritts_16k_gr1nq32ds320
num_bytes: 4649508077
num_examples: 28539
- name: funcodec_en_libritts_16k_gr8nq32ds320
num_bytes: 4649508077
num_examples: 28539
- name: funcodec_en_libritts_16k_nq32ds320
num_bytes: 4647663597
num_examples: 28539
- name: funcodec_en_libritts_16k_nq32ds640
num_bytes: 2330511341
num_examples: 28539
- name: funcodec_zh_en_16k_nq32ds320
num_bytes: 4647663597
num_examples: 28539
- name: funcodec_zh_en_16k_nq32ds640
num_bytes: 4647663597
num_examples: 28539
- name: speech_tokenizer_16k
num_bytes: 1166450829
num_examples: 28539
download_size: 7544903765
dataset_size: 47724982407
---
# Dataset Card for "librispeech_asr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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RCC-MSU/collection3
|
RCC-MSU
| 2023-01-31T09:47:58Z | 113 | 4 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ru",
"license:other",
"region:us"
] |
[
"token-classification"
] | 2022-08-23T14:03:02Z |
---
annotations_creators:
- other
language:
- ru
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: Collection3
size_categories:
- 10K<n<100K
source_datasets: []
tags: []
task_categories:
- token-classification
task_ids:
- named-entity-recognition
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
splits:
- name: test
num_bytes: 935298
num_examples: 1922
- name: train
num_bytes: 4380588
num_examples: 9301
- name: validation
num_bytes: 1020711
num_examples: 2153
download_size: 878777
dataset_size: 6336597
---
# Dataset Card for Collection3
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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:** [Collection3 homepage](http://labinform.ru/pub/named_entities/index.htm)
- **Repository:** [Needs More Information]
- **Paper:** [Two-stage approach in Russian named entity recognition](https://ieeexplore.ieee.org/document/7584769)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Collection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. Dataset is based on collection [Persons-1000](http://ai-center.botik.ru/Airec/index.php/ru/collections/28-persons-1000) originally containing 1000 news documents labeled only with names of persons.
Additional labels were obtained using guidelines similar to MUC-7 with web-based tool [Brat](http://brat.nlplab.org/) for collaborative text annotation.
Currently dataset contains 26K annotated named entities (11K Persons, 7K Locations and 8K Organizations).
Conversion to the IOB2 format and splitting into train, validation and test sets was done by [DeepPavlov team](http://files.deeppavlov.ai/deeppavlov_data/collection3_v2.tar.gz).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Russian
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"id": "851",
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 2, 0, 0, 0],
"tokens": ['Главный', 'архитектор', 'программного', 'обеспечения', '(', 'ПО', ')', 'американского', 'высокотехнологичного', 'гиганта', 'Microsoft', 'Рэй', 'Оззи', 'покидает', 'компанию', '.']
}
```
### Data Fields
- id: a string feature.
- tokens: a list of string features.
- ner_tags: a list of classification labels (int). Full tagset with indices:
```
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6}
```
### Data Splits
|name|train|validation|test|
|---------|----:|---------:|---:|
|Collection3|9301|2153|1922|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{mozharova-loukachevitch-2016-two-stage-russian-ner,
author={Mozharova, Valerie and Loukachevitch, Natalia},
booktitle={2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)},
title={Two-stage approach in Russian named entity recognition},
year={2016},
pages={1-6},
doi={10.1109/FRUCT.2016.7584769}}
```
|
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] |
tushar117/xalign
|
tushar117
| 2023-01-01T20:39:30Z | 113 | 1 |
[
"task_categories:table-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"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",
"language:en",
"license:cc-by-nc-sa-4.0",
"license:mit",
"xalign",
"NLG",
"low-resource",
"LRL",
"arxiv:2202.00291",
"arxiv:2209.11252",
"region:us"
] |
[
"table-to-text"
] | 2022-12-29T06:50:10Z |
---
annotations_creators:
- found
configs:
- release_v1
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
- en
language_creators:
- crowdsourced
license:
- cc-by-nc-sa-4.0
- mit
multilinguality:
- multilingual
paperswithcode_id: xalign
pretty_name: 'XAlign'
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- xalign
- NLG
- low-resource
- LRL
task_categories:
- table-to-text
task_ids:
- rdf-to-text
---
# Dataset Card for XAlign
## 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)
- [Known Limitations](#known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [XAlign homepage](https://github.com/tushar117/XAlign)
- **Repository:** [XAlign repo](https://github.com/tushar117/XAlign)
- **Paper:** [XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages](https://arxiv.org/abs/2202.00291)
- **Leaderboard:** [Papers With Code Leaderboard for XAlign](https://paperswithcode.com/sota/data-to-text-generation-on-xalign)
- **Point of Contact:** [Tushar Abhishek]([email protected])
### Dataset Summary
It consists of an extensive collection of a high quality cross-lingual fact-to-text dataset where facts are in English and corresponding sentences are in native language for person biographies. The Train & validation splits are created using distant supervision methods and Test data is generated through human annotations.
### Supported Tasks and Leaderboards
- 'Data-to-text Generation': XAlign dataset can be used to train cross-lingual data-to-text generation models. The model performance can measured through any text generation evaluation metrics by taking average across all the languages. [Sagare et al. (2022)](https://arxiv.org/abs/2209.11252) reported average BLEU score of 29.27 and average METEOR score of 53.64 over the test set.
- 'Relation Extraction': XAlign could also be used for cross-lingual relation extraction where relations in English can be extracted from associated native sentence.
See [Papers With Code Leaderboard](https://paperswithcode.com/sota/data-to-text-generation-on-xalign) for more models.
### Languages
Assamese (as), Bengali (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Oriya (or), Punjabi (pa), Tamil (ta), Telugu (te), and English (en).
## Dataset Structure
### Data Fields
Each record consist of the following entries:
- sentence (string) : Native language wikipedia sentence. (non-native language strings were removed.)
- `facts` (List[Dict]) : List of facts associated with the sentence where each fact is stored as dictionary.
- language (string) : Language identifier.
The `facts` key contains list of facts where each facts is stored as dictionary. A single record within fact list contains following entries:
- subject (string) : central entity.
- object (string) : entity or a piece of information about the subject.
- predicate (string) : relationship that connects the subject and the object.
- qualifiers (List[Dict]) : It provide additional information about the fact, is stored as list of qualifier where each record is a dictionary. The dictionary contains two keys: qualifier_predicate to represent property of qualifer and qualifier_object to store value for the qualifier's predicate.
### Data Instances
Example from English
```
{
"sentence": "Mark Paul Briers (born 21 April 1968) is a former English cricketer.",
"facts": [
{
"subject": "Mark Briers",
"predicate": "date of birth",
"object": "21 April 1968",
"qualifiers": []
},
{
"subject": "Mark Briers",
"predicate": "occupation",
"object": "cricketer",
"qualifiers": []
},
{
"subject": "Mark Briers",
"predicate": "country of citizenship",
"object": "United Kingdom",
"qualifiers": []
}
],
"language": "en"
}
```
Example from one of the low-resource languages (i.e. Hindi)
```
{
"sentence": "बोरिस पास्तेरनाक १९५८ में साहित्य के क्षेत्र में नोबेल पुरस्कार विजेता रहे हैं।",
"facts": [
{
"subject": "Boris Pasternak",
"predicate": "nominated for",
"object": "Nobel Prize in Literature",
"qualifiers": [
{
"qualifier_predicate": "point in time",
"qualifier_subject": "1958"
}
]
}
],
"language": "hi"
}
```
### Data Splits
The XAlign dataset has 3 splits: train, validation, and test. Below are the statistics the dataset.
| Dataset splits | Number of Instances in Split |
| --- | --- |
| Train | 499155 |
| Validation | 55469 |
| Test | 7425 |
## Dataset Creation
### Curation Rationale
Most of the existing Data-to-Text datasets are available in English. Also, the structured Wikidata entries for person entities in low resource languages are minuscule in number compared to that in English. Thus, monolingual Data-to-Text for low resource languages suffers from data sparsity. XAlign dataset would be useful in creation of cross-lingual Data-to-Text generation systems that take a set of English facts as input and generates a sentence capturing the fact-semantics in the specified language.
### Source Data
#### Initial Data Collection and Normalization
The dataset creation process starts with an intial list of ~95K person entities selected from Wikidata and each of which has a link to a corresponding Wikipedia page in at least one of our 11 low resource languages. This leads to a dataset where every instance is a tuple containing entityID, English Wikidata facts, language identifier, Wikipedia URL for the entityID. The facts (in English) are extracted from the 20201221 WikiData dump for each entity using the [WikiData](https://query.wikidata.org) APIs. The facts are gathered only for the speficied Wikidata property (or relation) types that captures most useful factual information for person entities: WikibaseItem, Time, Quantity, and Monolingualtext.This leads to overall ~0.55M data instances across all the 12 languages. Also, for each language, the sentences (along with section information) are extracted from 20210520 Wikipedia XML dump using the pre-processing steps as described [here](https://arxiv.org/abs/2202.00291).
For every (entity, language) pair, the pre-processed dataset contains a set of English Wikidata facts and a set of Wikipedia sentences in that language. In order to create train and validation dataset, these are later passed through a two-stage automatic aligner as proposed in [abhishek et al. (2022)](https://arxiv.org/abs/2202.00291) to associate a sentence with a subset of facts.
#### Who are the source language producers?
The text are extracted from Wikipedia and facts are retrieved from Wikidata.
### Annotations
#### Annotation process
The Manual annotation of Test dataset was done in two phases. For both the phases, the annotators were presented with (low resource language sentence, list of English facts). They were asked to mark facts present in the given sentence. There were also specific guidelines to ignore redundant facts, handle abbreviations, etc. More detailed annotation guidelines and ethical statement are mentioned [here](https://docs.google.com/document/d/1ucGlf-Jm1ywQ_Fjw9f2UqPeMWPlBnlZA46UY7KuZ0EE/edit)
. In the first phase, we got 60 instances labeled per language by a set of 8 expert annotators (trusted graduate students who understood the task very well). In phase 2, we selected 8 annotators per language from the [National Register of Translators](https://www.ntm.org.in/languages/english/nrtdb.aspx}). We tested these annotators using phase 1 data as golden control set, and shortlisted up to 4 annotators per language who scored highest (on Kappa score with golden annotations).
#### Who are the annotators?
Human annotators were selected appropriately (after screening) from [National Translation Mission](https://www.ntm.org.in) for Test set creation.
### Personal and Sensitive Information
The dataset does not involve collection or storage of any personally identifiable information or offensive information at any stage.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of the this dataset is to help develop cross-lingual Data-to-Text generation systems that are vital in many downstream Natural Language Processing (NLP) applications like automated dialog systems, domain-specific chatbots, open domain question answering, authoring sports reports, etc. These systems will be useful for powering business applications like Wikipedia text generation given English Infoboxes, automated generation of non-English product descriptions using English product attributes, etc.
### Known Limitations
The XAlign dataset focus only on person biographies and system developed on this dataset might not be generalized to other domains.
## Additional Information
### Dataset Curators
This dataset is collected by Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta and Vasudeva Varma of Information Retrieval and Extraction Lab (IREL), Hyderabad, India. They released [scripts](https://github.com/tushar117/xalign) to collect and process the data into the Data-to-Text format.
### Licensing Information
The XAlign dataset is released under the [MIT License](https://github.com/tushar117/XAlign/blob/main/LICENSE).
### Citation Information
```
@article{abhishek2022xalign,
title={XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages},
author={Abhishek, Tushar and Sagare, Shivprasad and Singh, Bhavyajeet and Sharma, Anubhav and Gupta, Manish and Varma, Vasudeva},
journal={arXiv preprint arXiv:2202.00291},
year={2022}
}
```
### Contributions
Thanks to [Tushar Abhishek](https://github.com/tushar117), [Shivprasad Sagare](https://github.com/ShivprasadSagare), [Bhavyajeet Singh](https://github.com/bhavyajeet), [Anubhav Sharma](https://github.com/anubhav-sharma13), [Manish Gupta](https://github.com/blitzprecision) and [Vasudeva Varma]([email protected]) for adding this dataset.
Additional thanks to the annotators from National Translation Mission for their crucial contributions to creation of the test dataset: Bhaswati Bhattacharya, Aditi Sarkar, Raghunandan B. S., Satish M., Rashmi G.Rao, Vidyarashmi PN, Neelima Bhide, Anand Bapat, Krishna Rao N V, Nagalakshmi DV, Aditya Bhardwaj
Vuppula, Nirupama Patel, Asir. T, Sneha Gupta, Dinesh Kumar, Jasmin Gilani, Vivek R, Sivaprasad S, Pranoy J, Ashutosh Bharadwaj, Balaji Venkateshwar, Vinkesh Bansal, Vaishnavi Udyavara, Ramandeep Singh, Khushi Goyal, Yashasvi LN Pasumarthy and Naren Akash.
|
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] |
vietgpt/opus100_envi
|
vietgpt
| 2023-07-03T17:56:58Z | 113 | 0 |
[
"task_categories:translation",
"size_categories:1M<n<10M",
"language:en",
"language:vi",
"LM",
"region:us"
] |
[
"translation"
] | 2023-02-22T09:11:25Z |
---
dataset_info:
features:
- name: en
dtype: string
- name: vi
dtype: string
splits:
- name: test
num_bytes: 192744
num_examples: 2000
- name: train
num_bytes: 82614470
num_examples: 1000000
- name: validation
num_bytes: 194721
num_examples: 2000
download_size: 59201490
dataset_size: 83001935
task_categories:
- translation
language:
- en
- vi
tags:
- LM
size_categories:
- 1M<n<10M
---
# Opus100
- Source: https://huggingface.co/datasets/opus100
- Num examples:
- 1,000,000 (train)
- 2,000 (validation)
- 192,744 (test)
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/opus100_envi")
```
- Format for Translation task
```python
def preprocess(
sample,
instruction_key="### Instruction:",
input_key="Input:",
response_key="<|endofprompt|>",
end_key="<|endoftext|>",
en2vi=True,
):
if en2vi:
if random.random() < 0.5:
instruction = "Translate the following sentences from English into Vietnamese."
else:
instruction = "Dịch các câu sau từ tiếng Anh sang tiếng Việt."
input = sample['en'].strip()
response = sample['vi'].strip()
else:
if random.random() < 0.5:
instruction = "Translate the following sentences from Vietnamese into English."
else:
instruction = "Dịch các câu sau từ tiếng Việt sang tiếng Anh."
input = sample['vi'].strip()
response = sample['en'].strip()
return {'text': """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
{instruction_key}
{instruction}
{input_key}
{input}
{response_key}
{response}
{end_key}""".format(
instruction_key=instruction_key,
instruction=instruction,
input_key=input_key,
input=input,
response_key=response_key,
response=response,
end_key=end_key,
)}
"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Dịch các câu sau từ tiếng Anh sang tiếng Việt.
Input:
Toast falls jelly-side down, children hit tables and people get hurt.
<|endofprompt|>
Bánh mì nướng rơi đông lại, trẻ con va vào bàn và con người bị thương.
<|endoftext|>
"""
```
|
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bibidentuhanoi/BMO_BASE_FUNCTION_TEXT
|
bibidentuhanoi
| 2023-11-21T18:05:38Z | 113 | 0 |
[
"region:us"
] | null | 2023-10-30T15:26:57Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 279596
num_examples: 354
download_size: 88700
dataset_size: 279596
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "BMO_BASE_FUNCTION_TEXT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Leorasz/military_bases
|
Leorasz
| 2023-11-15T00:19:34Z | 113 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-15T00:05:14Z |
---
license: unknown
---
|
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arpanetus/hackernews_title_upvote_0
|
arpanetus
| 2023-11-18T13:45:05Z | 113 | 0 |
[
"region:us"
] | null | 2023-11-18T09:52:12Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 13840984
num_examples: 15064
download_size: 8346861
dataset_size: 13840984
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hackernews_title_upvote_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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felipeoes/filtered_long_answers_qa_blue_amazon_legislation_56k
|
felipeoes
| 2023-11-19T21:00:43Z | 113 | 0 |
[
"region:us"
] | null | 2023-11-19T04:44:26Z |
---
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: file_name
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: new_questions
dtype: string
- name: new_long_answers
dtype: string
- name: url
sequence: 'null'
- name: __index_level_0__
dtype: float64
splits:
- name: train
num_bytes: 65873196
num_examples: 44796
- name: test
num_bytes: 8290067
num_examples: 5599
- name: validation
num_bytes: 8166999
num_examples: 5598
download_size: 43267608
dataset_size: 82330262
---
# Dataset Card for "filtered_long_answers_qa_blue_amazon_legislation_56k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nu-dialogue/sfcoco2022
|
nu-dialogue
| 2023-11-28T00:30:10Z | 113 | 0 |
[
"task_categories:image-to-text",
"language:ja",
"region:us"
] |
[
"image-to-text"
] | 2023-11-27T12:15:33Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 366416649.50223213
num_examples: 806
- name: test
num_bytes: 41865941.49776786
num_examples: 90
download_size: 405465686
dataset_size: 408282591
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- image-to-text
language:
- ja
---
|
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] |
MicPie/unpredictable_cluster11
|
MicPie
| 2022-08-04T19:50:50Z | 112 | 0 |
[
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-classification",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:closed-book-qa",
"task_ids:open-book-qa",
"task_ids:language-modeling",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:tabular-multi-class-classification",
"task_ids:tabular-multi-label-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:2208.01009",
"region:us"
] |
[
"multiple-choice",
"question-answering",
"zero-shot-classification",
"text2text-generation",
"table-question-answering",
"text-generation",
"text-classification",
"tabular-classification"
] | 2022-07-08T17:19:16Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster11
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cluster11" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** [email protected], [email protected]
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
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] |
Piro17/affectnethq
|
Piro17
| 2023-02-16T06:56:12Z | 112 | 2 |
[
"region:us"
] | null | 2023-02-16T06:47:30Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': anger
'1': disgust
'2': fear
'3': happy
'4': neutral
'5': sad
'6': surprise
splits:
- name: train
num_bytes: 5858852632.634
num_examples: 27823
download_size: 0
dataset_size: 5858852632.634
---
# Dataset Card for "affectnethq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
vietgpt/wikipedia_vi
|
vietgpt
| 2023-09-16T05:11:18Z | 112 | 4 |
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:vi",
"LM",
"region:us"
] |
[
"text-generation"
] | 2023-02-21T20:39:38Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: revid
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1053551922.960177
num_examples: 1284930
download_size: 569515706
dataset_size: 1053551922.960177
task_categories:
- text-generation
language:
- vi
size_categories:
- 1M<n<10M
tags:
- LM
---
# Wikipedia
- Source: https://huggingface.co/datasets/wikipedia
- Num examples: 1,281,412
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikipedia_vi")
```
|
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] |
sh0416/sst2-openai
|
sh0416
| 2023-02-25T12:53:53Z | 112 | 0 |
[
"task_categories:text-classification",
"region:us"
] |
[
"text-classification"
] | 2023-02-25T12:16:45Z |
---
task_categories:
- text-classification
---
Original source: https://github.com/openai/generating-reviews-discovering-sentiment
This dataset is different from the dataset distributed by GLUE, which means the metric **shouldn't be compared with the SST2 performance in GLUE**.
The description of SST2 dataset in the paper is the following.
> The Stanford Sentiment Treebank (SST)(Socher et al., 2013) was created specifically to evaluate more complex compositional models of language. It is de-rived from the same base dataset as MR but was relabeledvia Amazon Mechanical and includes dense labeling of thephrases of parse trees computed for all sentences. For thebinary subtask, this amounts to 76961 total labels com-pared to the 6920 sentence level labels. As a demonstrationof the capability of unsupervised representation learning tosimplify data collection and remove preprocessing steps,our reported results ignore these dense labels and computedparse trees, using only the raw text and sentence level la-bels
|
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dominguesm/alpaca-data-pt-br
|
dominguesm
| 2023-11-17T08:51:52Z | 112 | 11 |
[
"task_categories:text-generation",
"language:pt",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"region:us"
] |
[
"text-generation"
] | 2023-03-17T22:48:45Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 18324606
num_examples: 51759
download_size: 11508709
dataset_size: 18324606
license: cc-by-nc-4.0
language:
- pt
tags:
- instruction-finetuning
pretty_name: Alpaca-Cleaned-PTBR
task_categories:
- text-generation
---
**NOTE:** This is a machine translated version of the [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset.
# Dataset Card for Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
```
"instruction":"Summarize the given article in 200 Words.",
"input": "https://www.bbc.com/news/world-51461830",
"output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state."
"instruction": "Classify the following shape according to simple geometry.",
"input": "<Picture Attached>",
"output": "Triangle"
"instruction": "Given a recent news article, write a headline for it.",
"input": "https://www.news.example.com/article-1",
"output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\""
```
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
```
"instruction": "Should I invest in stocks?",
"input": "",
"output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple"
```
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
```
"instruction": "Calculate the area of the following shape in square centimeters.",
"input": "rectangle of size 4 cm x 5 cm",
"output": ""
```
4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
```
"instruction": "Create a graphic or logo that visually represents the word \"courage\".",
"input": "",
"output": "<No Output>"
```
6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs.
7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty.
```
"input":"<no input>"
"input":"No input"
"input":"noinput"
"input":"<noinput>"
```
8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
```
"instruction": "Calculate the median of the following data set.",
"input": "1, 2, 4, 5, 8, 9",
"output": "5"
"instruction": "Convert 25m to km.",
"input": "",
"output": "25km"
```
9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
```
"instruction": "Freeze the following sample of yogurt for 10 minutes.",
"input": "Yogurt sample",
"output": "<noinput>"
"instruction": "Increase the font size to 12 points.",
"input": "",
"output": "The font size has been increased to 12 points."
```
10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters.
### Original Alpaca Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The data in Alpaca are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"instruction": "Create a classification task by clustering the given list of items.",
"input": "Apples, oranges, bananas, strawberries, pineapples",
"output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform. Each of the 52K instructions is unique.
* `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* `output`: the answer to the instruction as generated by `text-davinci-003`.
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| alpaca | 52002 |
## 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
Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset:
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
### Contributions
[More Information Needed]
|
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] |
AyoubChLin/20News_group
|
AyoubChLin
| 2023-04-04T15:37:49Z | 112 | 0 |
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-classification"
] | 2023-04-04T15:12:32Z |
---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 41896639
num_examples: 19850
download_size: 24928143
dataset_size: 41896639
task_categories:
- text-classification
language:
- en
size_categories:
- n<1K
---
|
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] |
Fsoft-AIC/the-vault-function
|
Fsoft-AIC
| 2023-07-04T02:33:36Z | 112 | 9 |
[
"task_categories:text-generation",
"multilinguality:multiprogramming languages",
"language:code",
"language:en",
"license:mit",
"arxiv:2305.06156",
"region:us"
] |
[
"text-generation"
] | 2023-05-05T14:25:47Z |
---
language:
- code
- en
multilinguality:
- multiprogramming languages
task_categories:
- text-generation
license: mit
dataset_info:
features:
- name: identifier
dtype: string
- name: return_type
dtype: string
- name: repo
dtype: string
- name: path
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
dtype: string
- name: original_docstring
dtype: string
- name: comment
dtype: string
- name: docstring_tokens
dtype: string
- name: docstring
dtype: string
- name: original_string
dtype: string
pretty_name: The Vault Function
viewer: true
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Statistics](#dataset-statistics)
- [Usage](#usage)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault)
- **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156)
- **Contact:** [email protected]
- **Website:** https://www.fpt-aicenter.com/ai-residency/
<p align="center">
<img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo">
</p>
<div align="center">
# The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
</div>
## Dataset Summary
The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset.
We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility.
## Supported Tasks
The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as *code summarization*, *text-to-code generation* and *code search*.
## Languages
The natural language text (docstring) is in English.
10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust`
## Dataset Structure
### Data Instances
```
{
"hexsha": "5c47f0b4c173a8fd03e4e633d9b3dd8211e67ad0",
"repo": "neumanna94/beepboop",
"path": "js/scripts.js",
"license": [
"MIT"
],
"language": "JavaScript",
"identifier": "beepBoopSelector",
"return_type": "<not_specific>",
"original_string": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}",
"original_docstring": "//Determines what beepBoop function to use",
"docstring": "Determines what beepBoop function to use",
"docstring_tokens": [
"Determines",
"what",
"beepBoop",
"function",
"to",
"use"
],
"code": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}",
"code_tokens": [
"function",
"beepBoopSelector",
"(",
"inputString",
",",
"bbFunction",
")",
"{",
"if",
"(",
"bbFunction",
"==",
"1",
")",
"{",
"return",
"beepBoop",
"(",
"inputString",
")",
";",
"}",
"else",
"if",
"(",
"bbFunction",
"==",
"2",
")",
"{",
"return",
"beepBoop2",
"(",
"inputString",
")",
";",
"}",
"else",
"if",
"(",
"bbFunction",
"==",
"3",
")",
"{",
"return",
"beepBoop3",
"(",
"inputString",
")",
";",
"}",
"else",
"{",
"}",
"}"
],
"short_docstring": "Determines what beepBoop function to use",
"short_docstring_tokens": [
"Determines",
"what",
"beepBoop",
"function",
"to",
"use"
],
"comment": [],
"parameters": [
{
"param": "inputString",
"type": null
},
{
"param": "bbFunction",
"type": null
}
],
"docstring_params": {
"returns": [],
"raises": [],
"params": [
{
"identifier": "inputString",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "bbFunction",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
}
}
```
### Data Fields
Data fields for function level:
- **hexsha** (string): the unique git hash of file
- **repo** (string): the owner/repo
- **path** (string): the full path to the original file
- **license** (list): licenses in the repo
- **language** (string): the programming language
- **identifier** (string): the function or method name
- **return_type** (string): the type returned by the function
- **original_string** (string): original version of function/class node
- **original_docstring** (string): the raw string before tokenization or parsing
- **code** (string): the part of the original that is code
- **code_tokens** (list): tokenized version of `code`
- **short_docstring** (string): short, brief summarization (first line of the docstring)
- **short_docstring_tokens** (list): tokenized version of `short_docstring
- **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc)
- **docstring_tokens** (list): tokenized version of docstring
- **comment** (list): list of comments (line) inside the function/class
- **parameters** (list): List of parameters and its type (type can be None)
- **docstring_params** (dict): Dictionary of the parsed information from docstring
See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples.
### Data Splits
In this repo, The Vault is divided into 5 subsets, where three training versions are split based on size of the full training set, and the remains are validation set and test set (approximate 20,000 samples in each). The statistic for languages in each split set is illustrated in the following section.
Before split, the dataset is deduplicated. There are 3 versions of training set that are small (5%), medium (20%) and large (100%).
## Dataset Statistics
- Compare to other benchmarks
| Dataset | #Language | #Code-text pair |
|:--------------------------|----------:|-----------------:|
| PyMT5 | 1 | ≈ 7,700,000 |
| CoDesc | 1 | 4,211,516 |
| CodeSearchNet | 6 | 2,326,976 |
| CodeSearchNet (CodeXGLUE) | 6 | 1,005,474 |
| Deepcom | 1 | 424,028 |
| CONCODE | 1 | 2,184,310 |
| Funcom | 1 | 2,149,121 |
| CodeT5 | 8 | 3,158,313 |
| **The Vault** | **10** | **34,098,775** |
- Statistic for split sets
| | train/small | train/medium | train/full | validation | test | total |
|:-----------|------------:|-------------:|-----------:|-----------:|-------:|--------------:|
|Python | 370,657 | 1,952,110 | 7,772,647 | 30,992 | 21,652 | 7,825,291 |
|Java | 351,213 | 1,612,366 | 6,629,193 | 22,677 | 15,552 | 6,667,422 |
|JavaScript | 82,931 | 404,729 | 1,640,416 | 22,044 | 21,108 | 1,683,568 |
|PHP | 236,638 | 1,155,476 | 4,656,371 | 21,375 | 19,010 | 4,696,756 |
|C | 105,978 | 381,207 | 1,639,319 | 27,525 | 19,122 | 1,685,966 |
|C# | 141,090 | 783,166 | 3,305,891 | 24,787 | 19,638 | 3,350,316 |
|C++ | 87,420 | 410,907 | 1,671,268 | 20,011 | 18,169 | 1,709,448 |
|Go | 267,535 | 1,319,547 | 5,109,020 | 19,102 | 25,314 | 5,153,436 |
|Ruby | 23,921 | 112,574 | 424,339 | 17,338 | 19,908 | 461,585 |
|Rust | 35,367 | 224,015 | 825,130 | 16,716 | 23,141 | 864,987 |
|TOTAL | 1,702,750 | 8,356,097 |33,673,594 |222,567 |202,614 |**34,098,775** |
## Usage
You can load The Vault dataset using datasets library: ```pip install datasets```
```python
from datasets import load_dataset
# Load full function level dataset (34M samples)
dataset = load_dataset("Fsoft-AIC/the-vault-function")
# Load function level train/validation/test set
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"])
# Load "small" (or "medium", "full") version of function level training set
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"])
# specific language (e.g. Python)
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"], languages=['Python'])
# dataset streaming
data = load_dataset("Fsoft-AIC/the-vault-function", split_set= ["train"], streaming= True)
for sample in iter(data['train']):
print(sample)
```
A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link).
## Additional information
### Licensing Information
MIT License
### Citation Information
```
@article{manh2023vault,
title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ},
journal={arXiv preprint arXiv:2305.06156},
year={2023}
}
```
### Contributions
This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code).
|
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mmnga/wikipedia-ja-20230720-2k
|
mmnga
| 2023-09-25T08:20:29Z | 112 | 0 |
[
"region:us"
] | null | 2023-09-25T07:51:08Z |
---
dataset_info:
features:
- name: curid
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 5492016.948562663
num_examples: 2048
download_size: 3161030
dataset_size: 5492016.948562663
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wikipedia-ja-20230720-2k"
This is data extracted randomly from [izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720), consisting of 2,048 records.
[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)からデータを2k分ランダムに抽出したデータです。
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
distil-whisper/rev16
|
distil-whisper
| 2023-10-17T17:15:02Z | 112 | 0 |
[
"region:us"
] | null | 2023-10-13T16:09:08Z |
---
dataset_info:
- config_name: full
features:
- name: audio
dtype: audio
- name: file_number
dtype: string
- name: show_title
dtype: string
- name: episode_title
dtype: string
- name: itunes_id
dtype: string
- name: transcription
dtype: string
splits:
- name: test
num_bytes: 1509910660.0
num_examples: 30
download_size: 1445493754
dataset_size: 1509910660.0
- config_name: whisper_subset
features:
- name: audio
dtype: audio
- name: file_number
dtype: string
- name: show_title
dtype: string
- name: episode_title
dtype: string
- name: itunes_id
dtype: string
- name: transcription
dtype: string
splits:
- name: test
num_bytes: 921693242.0
num_examples: 16
download_size: 881542397
dataset_size: 921693242.0
configs:
- config_name: full
data_files:
- split: test
path: full/test-*
- config_name: whisper_subset
data_files:
- split: test
path: whisper_subset/test-*
---
# Dataset Card for "rev16"
Configs:
* `full`: the entire 30 podcast files
* `whisper_subset`: the subset of 16 podcast files used in the Whisper paper for long-form evaluation. The remaining 14 files have mis-matches between the audio and labels, and are thus filtered from the test set.
|
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] |
amlan107/syn_0
|
amlan107
| 2023-10-20T13:54:20Z | 112 | 0 |
[
"region:us"
] | null | 2023-10-20T13:54:19Z |
---
dataset_info:
features:
- name: bn
dtype: string
- name: ck
dtype: string
splits:
- name: train
num_bytes: 1794536.5235337194
num_examples: 12016
download_size: 839316
dataset_size: 1794536.5235337194
---
# Dataset Card for "syn_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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arpanetus/hackernews_title_upvote
|
arpanetus
| 2023-11-18T13:45:17Z | 112 | 0 |
[
"region:us"
] | null | 2023-11-18T09:04:14Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 13840984
num_examples: 15064
download_size: 8346861
dataset_size: 13840984
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hackernews_title_upvote"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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strombergnlp/bornholmsk_parallel
|
strombergnlp
| 2022-07-01T15:45:35Z | 111 | 2 |
[
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:cc-by-4.0",
"region:us"
] |
[
"translation"
] | 2022-05-11T08:29:38Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
- da-bornholm
license:
- cc-by-4.0
multilinguality:
- translation
pretty_name: Bornholmsk/Danish Parallel Texts
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bornholmsk-parallel
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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:** [https://github.com/StrombergNLP/bornholmsk](https://github.com/StrombergNLP/bornholmsk)
- **Repository:** [https://github.com/StrombergNLP/bornholmsk](https://github.com/StrombergNLP/bornholmsk)
- **Paper:** [https://aclanthology.org/W19-6138/](https://aclanthology.org/W19-6138/)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 490 KB
- **Size of the generated dataset:** 582 KB
- **Total amount of disk used:** 1072 KB
### Dataset Summary
This dataset is parallel text for Bornholmsk and Danish.
For more details, see the paper [Bornholmsk Natural Language Processing: Resources and Tools](https://aclanthology.org/W19-6138/).
### Supported Tasks and Leaderboards
*
### Languages
Bornholmsk, a language variant of Danish spoken on the island of Bornholm, and Danish. bcp47: `da-bornholm` and `da-DK`
## Dataset Structure
### Data Instances
### Data Fields
`id`: the sentence ID, `int`
`da-bornholm`: the Bornholmsk text, `string`
`da`: the Danish translation, `string`
### Data Splits
* Train: 5785 sentence pairs
* Validation: 500 sentence pairs
* Test: 500 sentence pairs
## Dataset Creation
### Curation Rationale
To gather as much parallel Bornholmsk together as possible
### Source Data
#### Initial Data Collection and Normalization
From a translation of Kuhre's Sansager, a selection of colloquial resources, and a prototype Bornholmsk/Danish dictionary
#### Who are the source language producers?
Native speakers of Bornholmsk who have produced works in their native language, or translated them to Danish. Much of the data is the result of a community of Bornholmsk speakers volunteering their time across the island in an effort to capture this endangered language.
### Annotations
#### Annotation process
No annotations
#### Who are the annotators?
Native speakers of Bornholmsk, mostly aged 60+.
### Personal and Sensitive Information
Unknown, but low risk of presence, given the source material
## Considerations for Using the Data
### Social Impact of Dataset
The hope behind this data is to enable people to learn and use Bornholmsk
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
This collection of Bornholmsk is curated by Leon Derczynski and Alex Speed Kjeldsen
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
```
@inproceedings{derczynski-kjeldsen-2019-bornholmsk,
title = "Bornholmsk Natural Language Processing: Resources and Tools",
author = "Derczynski, Leon and
Kjeldsen, Alex Speed",
booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
month = sep # "{--}" # oct,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6138",
pages = "338--344",
}
```
|
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lmqg/qg_koquad
|
lmqg
| 2022-12-02T18:53:42Z | 111 | 3 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:squad_es",
"language:ko",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-06-02T23:42:21Z |
---
license: cc-by-4.0
pretty_name: KorQuAD for question generation
language: ko
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: squad_es
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_korquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
This is a modified version of [KorQuAD](https://huggingface.co/datasets/squad_kor_v1) for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.
### Supported Tasks and Leaderboards
* `question-generation`: The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Korean (ko)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"question": "함수해석학이 주목하는 탐구는?",
"paragraph": "변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구분야는 복소해석학이라고 한다. 함수해석학은 함수의 공간(특히 무한차원)의 탐구에 주목한다. 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.",
"answer": "함수의 공간(특히 무한차원)의 탐구",
"sentence": "함수해석학은 함수의 공간(특히 무한차원)의 탐구 에 주목한다.",
"paragraph_sentence": '변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구 분야는 복소해석학이라고 한다. <hl> 함수해석학은 함수의 공간(특히 무한차원)의 탐구 에 주목한다. <hl> 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.',
"paragraph_answer": '변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구 분야는 복소해석학이라고 한다. 함수해석학은 <hl> 함수의 공간(특히 무한차원)의 탐구 <hl>에 주목한다. 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.',
"sentence_answer": "함수해석학은 <hl> 함수의 공간(특히 무한차원)의 탐구 <hl> 에 주목한다."
}
```
The data fields are the same among all splits.
- `question`: a `string` feature.
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
`paragraph_sentence` feature is for sentence-aware question generation.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|54556| 5766 |5766 |
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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jordane95/msmarco-passage-with-query
|
jordane95
| 2023-03-01T09:13:35Z | 111 | 0 |
[
"license:afl-3.0",
"region:us"
] | null | 2022-07-26T08:25:24Z |
---
license: afl-3.0
---
|
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merkalo-ziri/qa_main
|
merkalo-ziri
| 2022-08-24T08:54:01Z | 111 | 0 |
[
"task_categories:question-answering",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:rus",
"license:other",
"region:us"
] |
[
"question-answering"
] | 2022-08-22T07:03:04Z |
---
annotations_creators:
- found
language:
- rus
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: qa_main
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids: []
---
# 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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