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 |
|---|---|---|---|---|---|---|---|---|---|
yuyijiong/FoodSafe-Doc-QA-Chinese | yuyijiong | 2023-12-05T09:05:29Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
]
| [
"text-generation"
]
| 2023-12-05T08:02:16Z | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- zh
size_categories:
- 1K<n<10K
---
## 食品安全领域指令微调数据
* 包含两个任务：多文档QA、论文QA
* 文档数据来自中国食品安全国标、教材、综述论文 | [
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|
KETI-AIR/kor_quail | KETI-AIR | 2023-12-05T08:19:44Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-12-05T08:11:03Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: challenge
path: data/challenge-*
dataset_info:
features:
- name: data_index_by_user
dtype: int32
- name: id
dtype: string
- name: context_id
dtype: string
- name: question_id
dtype: string
- name: domain
dtype: string
- name: metadata
struct:
- name: author
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: question_type
dtype: string
- name: answers
sequence: string
- name: correct_answer_id
dtype: int32
splits:
- name: train
num_bytes: 27612173
num_examples: 10246
- name: validation
num_bytes: 5860893
num_examples: 2164
- name: challenge
num_bytes: 1451663
num_examples: 556
download_size: 2671154
dataset_size: 34924729
license: cc-by-nc-sa-4.0
---
# Dataset Card for "kor_quail"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
# Source Data Citation Information
```
@inproceedings{DBLP:conf/aaai/RogersKDR20,
author = {Anna Rogers and
Olga Kovaleva and
Matthew Downey and
Anna Rumshisky},
title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
Real Tasks},
booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
February 7-12, 2020},
pages = {8722--8731},
publisher = {{AAAI} Press},
year = {2020},
url = {https://aaai.org/ojs/index.php/AAAI/article/view/6398},
timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},
biburl = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
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|
alvarobartt/ultrafeedback-binarized-preferences-clean | alvarobartt | 2023-12-05T08:43:28Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T08:42:35Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: instruction
dtype: string
- name: models
sequence: string
- name: completions
list:
- name: annotations
struct:
- name: instruction_following
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: honesty
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: truthfulness
struct:
- name: Type
sequence: string
- name: Rationale
dtype: string
- name: Rating
dtype: string
- name: Rationale For Rating
dtype: string
- name: helpfulness
struct:
- name: Type
sequence: string
- name: Rationale
dtype: string
- name: Rating
dtype: string
- name: Rationale For Rating
dtype: string
- name: custom_system_prompt
dtype: string
- name: model
dtype: string
- name: principle
dtype: string
- name: response
dtype: string
- name: critique
dtype: string
- name: overall_score
dtype: float64
- name: correct_answers
sequence: string
- name: incorrect_answers
sequence: string
splits:
- name: train
num_bytes: 831088377.5421702
num_examples: 63136
download_size: 318279041
dataset_size: 831088377.5421702
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Ogirimah/ask_herts | Ogirimah | 2023-12-05T08:58:18Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T08:53:06Z | ---
license: apache-2.0
---
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|
open-llm-leaderboard/details_DiscoResearch__DiscoLM-70b | open-llm-leaderboard | 2023-12-05T09:10:27Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T09:09:38Z | ---
pretty_name: Evaluation run of DiscoResearch/DiscoLM-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [DiscoResearch/DiscoLM-70b](https://huggingface.co/DiscoResearch/DiscoLM-70b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_DiscoResearch__DiscoLM-70b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-05T09:06:38.645783](https://huggingface.co/datasets/open-llm-leaderboard/details_DiscoResearch__DiscoLM-70b/blob/main/results_2023-12-05T09-06-38.645783.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6867356803337994,\n\
\ \"acc_stderr\": 0.030796614406114163,\n \"acc_norm\": 0.6887936208915021,\n\
\ \"acc_norm_stderr\": 0.03141252742960891,\n \"mc1\": 0.41615667074663404,\n\
\ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.576426250198526,\n\
\ \"mc2_stderr\": 0.015041628962992867\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6527303754266212,\n \"acc_stderr\": 0.013913034529620453,\n\
\ \"acc_norm\": 0.6877133105802048,\n \"acc_norm_stderr\": 0.013542598541688065\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6768571997610038,\n\
\ \"acc_stderr\": 0.004667209383690245,\n \"acc_norm\": 0.8609838677554272,\n\
\ \"acc_norm_stderr\": 0.0034525630964691296\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03317672787533157,\n\
\ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03317672787533157\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\
\ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7396226415094339,\n \"acc_stderr\": 0.027008766090708045,\n\
\ \"acc_norm\": 0.7396226415094339,\n \"acc_norm_stderr\": 0.027008766090708045\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7916666666666666,\n\
\ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.7916666666666666,\n\
\ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\
: 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6510638297872341,\n \"acc_stderr\": 0.03115852213135779,\n\
\ \"acc_norm\": 0.6510638297872341,\n \"acc_norm_stderr\": 0.03115852213135779\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.046570472605949625,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.046570472605949625\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531006,\n \"\
acc_norm\": 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531006\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8096774193548387,\n\
\ \"acc_stderr\": 0.022331707611823074,\n \"acc_norm\": 0.8096774193548387,\n\
\ \"acc_norm_stderr\": 0.022331707611823074\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5270935960591133,\n \"acc_stderr\": 0.03512819077876106,\n\
\ \"acc_norm\": 0.5270935960591133,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\"\
: 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.02931118867498312,\n\
\ \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.02931118867498312\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8434343434343434,\n \"acc_stderr\": 0.025890520358141454,\n \"\
acc_norm\": 0.8434343434343434,\n \"acc_norm_stderr\": 0.025890520358141454\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.0180883938390789,\n\
\ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.0180883938390789\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.29259259259259257,\n \"acc_stderr\": 0.027738969632176088,\n \
\ \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.027738969632176088\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7689075630252101,\n \"acc_stderr\": 0.027381406927868886,\n\
\ \"acc_norm\": 0.7689075630252101,\n \"acc_norm_stderr\": 0.027381406927868886\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"\
acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8752293577981651,\n \"acc_stderr\": 0.01416829835915634,\n \"\
acc_norm\": 0.8752293577981651,\n \"acc_norm_stderr\": 0.01416829835915634\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5694444444444444,\n \"acc_stderr\": 0.03376922151252335,\n \"\
acc_norm\": 0.5694444444444444,\n \"acc_norm_stderr\": 0.03376922151252335\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8872549019607843,\n \"acc_stderr\": 0.02219857103945679,\n \"\
acc_norm\": 0.8872549019607843,\n \"acc_norm_stderr\": 0.02219857103945679\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8649789029535865,\n \"acc_stderr\": 0.022245776632003694,\n \
\ \"acc_norm\": 0.8649789029535865,\n \"acc_norm_stderr\": 0.022245776632003694\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7937219730941704,\n\
\ \"acc_stderr\": 0.02715715047956382,\n \"acc_norm\": 0.7937219730941704,\n\
\ \"acc_norm_stderr\": 0.02715715047956382\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.0349814938546247,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.0349814938546247\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8677685950413223,\n \"acc_stderr\": 0.03092278832044579,\n \"\
acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.03092278832044579\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8518518518518519,\n\
\ \"acc_stderr\": 0.03434300243631002,\n \"acc_norm\": 0.8518518518518519,\n\
\ \"acc_norm_stderr\": 0.03434300243631002\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.03083349114628124,\n\
\ \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.03083349114628124\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6339285714285714,\n\
\ \"acc_stderr\": 0.04572372358737431,\n \"acc_norm\": 0.6339285714285714,\n\
\ \"acc_norm_stderr\": 0.04572372358737431\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822583,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822583\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n\
\ \"acc_stderr\": 0.01911989279892498,\n \"acc_norm\": 0.905982905982906,\n\
\ \"acc_norm_stderr\": 0.01911989279892498\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8607918263090677,\n\
\ \"acc_stderr\": 0.01237878610188515,\n \"acc_norm\": 0.8607918263090677,\n\
\ \"acc_norm_stderr\": 0.01237878610188515\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7745664739884393,\n \"acc_stderr\": 0.022497230190967558,\n\
\ \"acc_norm\": 0.7745664739884393,\n \"acc_norm_stderr\": 0.022497230190967558\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4223463687150838,\n\
\ \"acc_stderr\": 0.016519594275297117,\n \"acc_norm\": 0.4223463687150838,\n\
\ \"acc_norm_stderr\": 0.016519594275297117\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7652733118971061,\n\
\ \"acc_stderr\": 0.02407180588767704,\n \"acc_norm\": 0.7652733118971061,\n\
\ \"acc_norm_stderr\": 0.02407180588767704\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8117283950617284,\n \"acc_stderr\": 0.021751866060815896,\n\
\ \"acc_norm\": 0.8117283950617284,\n \"acc_norm_stderr\": 0.021751866060815896\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5567375886524822,\n \"acc_stderr\": 0.02963483847376601,\n \
\ \"acc_norm\": 0.5567375886524822,\n \"acc_norm_stderr\": 0.02963483847376601\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5449804432855281,\n\
\ \"acc_stderr\": 0.012718456618701782,\n \"acc_norm\": 0.5449804432855281,\n\
\ \"acc_norm_stderr\": 0.012718456618701782\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7169117647058824,\n \"acc_stderr\": 0.02736586113151381,\n\
\ \"acc_norm\": 0.7169117647058824,\n \"acc_norm_stderr\": 0.02736586113151381\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7516339869281046,\n \"acc_stderr\": 0.017479487001364764,\n \
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.017479487001364764\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940588,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940588\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7714285714285715,\n \"acc_stderr\": 0.02688214492230774,\n\
\ \"acc_norm\": 0.7714285714285715,\n \"acc_norm_stderr\": 0.02688214492230774\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.024845753212306042,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.024845753212306042\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.02753912288906145,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.02753912288906145\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41615667074663404,\n\
\ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.576426250198526,\n\
\ \"mc2_stderr\": 0.015041628962992867\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.010410849775222795\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6353297952994693,\n \
\ \"acc_stderr\": 0.013258428375662245\n }\n}\n```"
repo_url: https://huggingface.co/DiscoResearch/DiscoLM-70b
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_12_05T09_06_38.645783
path:
- '**/details_harness|arc:challenge|25_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|gsm8k|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hellaswag|10_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T09-06-38.645783.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-05T09-06-38.645783.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- '**/details_harness|winogrande|5_2023-12-05T09-06-38.645783.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-05T09-06-38.645783.parquet'
- config_name: results
data_files:
- split: 2023_12_05T09_06_38.645783
path:
- results_2023-12-05T09-06-38.645783.parquet
- split: latest
path:
- results_2023-12-05T09-06-38.645783.parquet
---
# Dataset Card for Evaluation run of DiscoResearch/DiscoLM-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/DiscoResearch/DiscoLM-70b
- **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 [DiscoResearch/DiscoLM-70b](https://huggingface.co/DiscoResearch/DiscoLM-70b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_DiscoResearch__DiscoLM-70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-05T09:06:38.645783](https://huggingface.co/datasets/open-llm-leaderboard/details_DiscoResearch__DiscoLM-70b/blob/main/results_2023-12-05T09-06-38.645783.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6867356803337994,
"acc_stderr": 0.030796614406114163,
"acc_norm": 0.6887936208915021,
"acc_norm_stderr": 0.03141252742960891,
"mc1": 0.41615667074663404,
"mc1_stderr": 0.017255657502903043,
"mc2": 0.576426250198526,
"mc2_stderr": 0.015041628962992867
},
"harness|arc:challenge|25": {
"acc": 0.6527303754266212,
"acc_stderr": 0.013913034529620453,
"acc_norm": 0.6877133105802048,
"acc_norm_stderr": 0.013542598541688065
},
"harness|hellaswag|10": {
"acc": 0.6768571997610038,
"acc_stderr": 0.004667209383690245,
"acc_norm": 0.8609838677554272,
"acc_norm_stderr": 0.0034525630964691296
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7894736842105263,
"acc_stderr": 0.03317672787533157,
"acc_norm": 0.7894736842105263,
"acc_norm_stderr": 0.03317672787533157
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7396226415094339,
"acc_stderr": 0.027008766090708045,
"acc_norm": 0.7396226415094339,
"acc_norm_stderr": 0.027008766090708045
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7916666666666666,
"acc_stderr": 0.033961162058453336,
"acc_norm": 0.7916666666666666,
"acc_norm_stderr": 0.033961162058453336
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6510638297872341,
"acc_stderr": 0.03115852213135779,
"acc_norm": 0.6510638297872341,
"acc_norm_stderr": 0.03115852213135779
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4298245614035088,
"acc_stderr": 0.046570472605949625,
"acc_norm": 0.4298245614035088,
"acc_norm_stderr": 0.046570472605949625
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6413793103448275,
"acc_stderr": 0.039966295748767186,
"acc_norm": 0.6413793103448275,
"acc_norm_stderr": 0.039966295748767186
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.43915343915343913,
"acc_stderr": 0.025559920550531006,
"acc_norm": 0.43915343915343913,
"acc_norm_stderr": 0.025559920550531006
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677172,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677172
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8096774193548387,
"acc_stderr": 0.022331707611823074,
"acc_norm": 0.8096774193548387,
"acc_norm_stderr": 0.022331707611823074
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5270935960591133,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.5270935960591133,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.04461960433384739,
"acc_norm": 0.73,
"acc_norm_stderr": 0.04461960433384739
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8303030303030303,
"acc_stderr": 0.02931118867498312,
"acc_norm": 0.8303030303030303,
"acc_norm_stderr": 0.02931118867498312
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8434343434343434,
"acc_stderr": 0.025890520358141454,
"acc_norm": 0.8434343434343434,
"acc_norm_stderr": 0.025890520358141454
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9326424870466321,
"acc_stderr": 0.0180883938390789,
"acc_norm": 0.9326424870466321,
"acc_norm_stderr": 0.0180883938390789
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.676923076923077,
"acc_stderr": 0.02371088850197057,
"acc_norm": 0.676923076923077,
"acc_norm_stderr": 0.02371088850197057
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.29259259259259257,
"acc_stderr": 0.027738969632176088,
"acc_norm": 0.29259259259259257,
"acc_norm_stderr": 0.027738969632176088
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7689075630252101,
"acc_stderr": 0.027381406927868886,
"acc_norm": 0.7689075630252101,
"acc_norm_stderr": 0.027381406927868886
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.48344370860927155,
"acc_stderr": 0.040802441856289715,
"acc_norm": 0.48344370860927155,
"acc_norm_stderr": 0.040802441856289715
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8752293577981651,
"acc_stderr": 0.01416829835915634,
"acc_norm": 0.8752293577981651,
"acc_norm_stderr": 0.01416829835915634
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5694444444444444,
"acc_stderr": 0.03376922151252335,
"acc_norm": 0.5694444444444444,
"acc_norm_stderr": 0.03376922151252335
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8872549019607843,
"acc_stderr": 0.02219857103945679,
"acc_norm": 0.8872549019607843,
"acc_norm_stderr": 0.02219857103945679
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8649789029535865,
"acc_stderr": 0.022245776632003694,
"acc_norm": 0.8649789029535865,
"acc_norm_stderr": 0.022245776632003694
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7937219730941704,
"acc_stderr": 0.02715715047956382,
"acc_norm": 0.7937219730941704,
"acc_norm_stderr": 0.02715715047956382
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.0349814938546247,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.0349814938546247
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8677685950413223,
"acc_stderr": 0.03092278832044579,
"acc_norm": 0.8677685950413223,
"acc_norm_stderr": 0.03092278832044579
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8518518518518519,
"acc_stderr": 0.03434300243631002,
"acc_norm": 0.8518518518518519,
"acc_norm_stderr": 0.03434300243631002
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8098159509202454,
"acc_stderr": 0.03083349114628124,
"acc_norm": 0.8098159509202454,
"acc_norm_stderr": 0.03083349114628124
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.6339285714285714,
"acc_stderr": 0.04572372358737431,
"acc_norm": 0.6339285714285714,
"acc_norm_stderr": 0.04572372358737431
},
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}
```
### 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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|
tiennv/vietnamese-news | tiennv | 2023-12-05T09:45:05Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T09:14:46Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 28837412151
num_examples: 12573213
download_size: 15141327938
dataset_size: 28837412151
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vietnamese-news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
tiennv/english-wiki-corpus | tiennv | 2023-12-05T09:49:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T09:46:42Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 8275936982
num_examples: 10686170
download_size: 1407476006
dataset_size: 8275936982
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "english-wiki-corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
tiennv/english-mc4 | tiennv | 2023-12-05T10:26:36Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T09:55:22Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 24653765251
num_examples: 14294240
download_size: 15068999152
dataset_size: 24653765251
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "english-mc4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
zzak00/ai71 | zzak00 | 2023-12-05T11:17:47Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T09:55:25Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 21310
num_examples: 102
download_size: 7931
dataset_size: 21310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Elifslh/llamaconvert | Elifslh | 2023-12-05T09:55:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T09:55:33Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 54588
num_examples: 381
download_size: 12187
dataset_size: 54588
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
AbeShinzo0708/AbeShinzo_voicedata_for_Bert-VITS2 | AbeShinzo0708 | 2023-12-05T10:35:04Z | 0 | 0 | [
"language:ja",
"license:openrail",
"安倍晋三",
"AbeShinzo",
"region:us"
]
| null | 2023-12-05T10:12:30Z | ---
license: openrail
tags:
- 安倍晋三
- AbeShinzo
pretty_name: 安倍晋三
language:
- ja
--- | [
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|
punwaiw/diffusionJockeyDynamicsTight | punwaiw | 2023-12-05T10:37:00Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T10:12:47Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: dynamics
dtype: image
splits:
- name: train
num_bytes: 3947703601.25
num_examples: 17635
download_size: 3918487869
dataset_size: 3947703601.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
welli97/voicepaiva | welli97 | 2023-12-05T10:27:25Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T10:24:59Z | ---
license: openrail
---
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]
|
AbeShinzo0708/SugaYosihide_voicedata_for_Bert-VITS2 | AbeShinzo0708 | 2023-12-05T10:34:29Z | 0 | 0 | [
"language:ja",
"菅義偉",
"SugaYoshihide",
"region:us"
]
| null | 2023-12-05T10:33:12Z | ---
language:
- ja
tags:
- 菅義偉
- SugaYoshihide
pretty_name: 菅義偉
--- | [
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|
AbeShinzo0708/KishidaFumio_voicedata_for_Bert-VITS2 | AbeShinzo0708 | 2023-12-05T10:40:44Z | 0 | 0 | [
"language:ja",
"license:openrail",
"岸田文雄",
"KishidaFumio",
"region:us"
]
| null | 2023-12-05T10:35:40Z | ---
license: openrail
language:
- ja
tags:
- 岸田文雄
- KishidaFumio
pretty_name: 岸田文雄
--- | [
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|
cllatMTK/openorca-chinese-zhtw_google | cllatMTK | 2023-12-08T02:51:34Z | 0 | 1 | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:zh",
"license:mit",
"arxiv:2301.13688",
"region:us"
]
| [
"conversational",
"text-classification",
"token-classification",
"table-question-answering",
"question-answering",
"zero-shot-classification",
"summarization",
"feature-extraction",
"text-generation",
"text2text-generation"
]
| 2023-12-05T10:39:23Z | ---
language:
- zh
license: mit
size_categories:
- 10M<n<100M
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca-Chinese
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 6477736021
num_examples: 4233915
download_size: 4104476393
dataset_size: 6477736021
---
<p><h1>🐋 OpenOrca-Chinese 数据集！🐋</h1></p>
**注意 有2個ID有出現超過兩次，約有900k個ID出現兩次**
感謝 [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) 資料集的發布，為廣大NLP研究人員和開發者帶來了寶貴的資源！
這是一個對 [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) 資料集中文翻譯的版本，翻譯引擎為 Google 翻譯，希望能為中文 LLM 研究做出一點點貢獻。
<br/>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
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|
dresen/common_voice_13_0_da_pseudo_labelled | dresen | 2023-12-05T10:56:26Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T10:41:05Z | ---
dataset_info:
config_name: da
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 70558926.204
num_examples: 2746
- name: validation
num_bytes: 70260246.264
num_examples: 2222
- name: test
num_bytes: 63222825.0
num_examples: 2160
download_size: 195972937
dataset_size: 204041997.468
configs:
- config_name: da
data_files:
- split: train
path: da/train-*
- split: validation
path: da/validation-*
- split: test
path: da/test-*
---
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|
HydraIndicLM/hindi_alpaca_dolly_67k | HydraIndicLM | 2023-12-05T11:08:49Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T11:08:43Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: id
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 212592103
num_examples: 67017
download_size: 80604522
dataset_size: 212592103
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
fgokmenoglu/msc-demo | fgokmenoglu | 2023-12-05T11:16:31Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T11:14:45Z | ---
license: apache-2.0
---
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|
Seongill/squad_conflict | Seongill | 2023-12-05T11:16:48Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T11:16:26Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: masked_query
dtype: string
- name: query_embedding
sequence: float64
- name: ent_type
dtype: string
splits:
- name: train
num_bytes: 306405278.9382866
num_examples: 42943
download_size: 223170500
dataset_size: 306405278.9382866
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
HydraIndicLM/tamil_alpaca_dolly_51K | HydraIndicLM | 2023-12-05T11:17:05Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T11:16:59Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
- name: system_prompt
dtype: string
splits:
- name: train
num_bytes: 287556653
num_examples: 51876
download_size: 84685617
dataset_size: 287556653
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
HydraIndicLM/punjabi_alpaca_52K | HydraIndicLM | 2023-12-05T11:18:24Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T11:18:22Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 46649317
num_examples: 52002
download_size: 18652304
dataset_size: 46649317
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
sankovic/xxxdataset | sankovic | 2023-12-05T11:22:47Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T11:22:20Z | ---
license: openrail
---
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|
Juanbao/NUC | Juanbao | 2023-12-05T11:35:13Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T11:28:05Z | ---
license: openrail
---
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|
blanchon/EuroSat | blanchon | 2023-12-05T11:33:01Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T11:32:40Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Annual Crop
'1': Forest
'2': Herbaceous Vegetation
'3': Highway
'4': Industrial Buildings
'5': Pasture
'6': Permanent Crop
'7': Residential Buildings
'8': River
'9': SeaLake
- name: filename
dtype: string
splits:
- name: train
num_bytes: 104485303.0
num_examples: 16200
- name: test
num_bytes: 34726245.0
num_examples: 5400
- name: validation
num_bytes: 34781690.0
num_examples: 5400
download_size: 174279561
dataset_size: 173993238.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
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|
ImadSaddik/SME_AUDIT_SCENARIOS_EN | ImadSaddik | 2023-12-09T21:07:58Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T11:38:05Z | ---
license: apache-2.0
---
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|
satani/common_voice_13_0_hi_pseudo_labelled | satani | 2023-12-08T06:43:28Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T11:40:38Z | ---
dataset_info:
- config_name: hi
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
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dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
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num_bytes: 133795020.934
num_examples: 4479
- name: validation
num_bytes: 67494362.935
num_examples: 2281
- name: test
num_bytes: 102994313.039
num_examples: 2947
download_size: 269388323
dataset_size: 304283696.908
- config_name: hi_in
features:
- name: id
dtype: int32
- name: num_samples
dtype: int32
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: raw_transcription
dtype: string
- name: gender
dtype:
class_label:
names:
'0': male
'1': female
'2': other
- name: lang_id
dtype:
class_label:
names:
'0': af_za
'1': am_et
'2': ar_eg
'3': as_in
'4': ast_es
'5': az_az
'6': be_by
'7': bg_bg
'8': bn_in
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'10': ca_es
'11': ceb_ph
'12': ckb_iq
'13': cmn_hans_cn
'14': cs_cz
'15': cy_gb
'16': da_dk
'17': de_de
'18': el_gr
'19': en_us
'20': es_419
'21': et_ee
'22': fa_ir
'23': ff_sn
'24': fi_fi
'25': fil_ph
'26': fr_fr
'27': ga_ie
'28': gl_es
'29': gu_in
'30': ha_ng
'31': he_il
'32': hi_in
'33': hr_hr
'34': hu_hu
'35': hy_am
'36': id_id
'37': ig_ng
'38': is_is
'39': it_it
'40': ja_jp
'41': jv_id
'42': ka_ge
'43': kam_ke
'44': kea_cv
'45': kk_kz
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'50': lb_lu
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'57': mi_nz
'58': mk_mk
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'61': mr_in
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'63': mt_mt
'64': my_mm
'65': nb_no
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'75': ps_af
'76': pt_br
'77': ro_ro
'78': ru_ru
'79': sd_in
'80': sk_sk
'81': sl_si
'82': sn_zw
'83': so_so
'84': sr_rs
'85': sv_se
'86': sw_ke
'87': ta_in
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'89': tg_tj
'90': th_th
'91': tr_tr
'92': uk_ua
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'94': ur_pk
'95': uz_uz
'96': vi_vn
'97': wo_sn
'98': xh_za
'99': yo_ng
'100': yue_hant_hk
'101': zu_za
'102': all
- name: language
dtype: string
- name: lang_group_id
dtype:
class_label:
names:
'0': western_european_we
'1': eastern_european_ee
'2': central_asia_middle_north_african_cmn
'3': sub_saharan_african_ssa
'4': south_asian_sa
'5': south_east_asian_sea
'6': chinese_japanase_korean_cjk
- name: whisper_transcript
sequence: int64
splits:
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num_bytes: 1537557908.2
num_examples: 2120
- name: validation
num_bytes: 164618710.0
num_examples: 239
- name: test
num_bytes: 310072327.0
num_examples: 418
download_size: 1998285116
dataset_size: 2012248945.2
configs:
- config_name: hi
data_files:
- split: train
path: hi/train-*
- split: validation
path: hi/validation-*
- split: test
path: hi/test-*
- config_name: hi_in
data_files:
- split: train
path: hi_in/train-*
- split: validation
path: hi_in/validation-*
- split: test
path: hi_in/test-*
---
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|
plaguss/curation-ultrafeedback-scores | plaguss | 2023-12-05T12:59:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T12:47:30Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: instruction
dtype: string
- name: best_rated_is_different_from_best_overall
dtype: bool
- name: best_overall_model
dtype: string
- name: score_best_overall
dtype: float64
- name: best_rated_model
dtype: string
- name: score_best_rated
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- name: best_overall_score_response
struct:
- name: annotations
struct:
- name: helpfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: honesty
struct:
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dtype: string
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struct:
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dtype: string
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struct:
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dtype: string
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dtype: string
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dtype: string
- name: Type
sequence: string
- name: average_rating
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- name: critique
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struct:
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struct:
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struct:
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struct:
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- name: completions
list:
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struct:
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- name: random_response_for_rated
dtype: float64
- name: rating-distilabel-gpt4
sequence: float64
- name: rationale-distilabel-gpt4
sequence: string
splits:
- name: train
num_bytes: 46976035
num_examples: 2405
download_size: 18006660
dataset_size: 46976035
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
DJFelipeBR/vozcarlos | DJFelipeBR | 2023-12-05T12:57:05Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T12:56:06Z | ---
license: openrail
---
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|
Flavinhouaua2022/UAUA | Flavinhouaua2022 | 2023-12-05T12:56:17Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T12:56:17Z | ---
license: apache-2.0
---
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|
plaguss/curation-ultrafeedback-scores-raw | plaguss | 2023-12-05T13:02:13Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T13:02:11Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: generations
sequence: string
- name: labelling_model
dtype: string
- name: labelling_prompt
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_labelling_response
dtype: string
- name: rating-distilabel-gpt4
sequence: float64
- name: rationale-distilabel-gpt4
sequence: string
splits:
- name: train
num_bytes: 11333592
num_examples: 2405
download_size: 4830548
dataset_size: 11333592
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Rafa775/ramon | Rafa775 | 2023-12-05T13:05:13Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T13:04:00Z | ---
license: openrail
---
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|
Gabriel1322/koko | Gabriel1322 | 2023-12-05T13:28:02Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T13:27:39Z | ---
license: openrail
---
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|
Gabriel1322/kaka | Gabriel1322 | 2023-12-05T13:29:33Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T13:29:14Z | ---
license: openrail
---
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|
maimi2009/Heisei | maimi2009 | 2023-12-05T13:36:14Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-12-05T13:36:13Z | ---
license: unknown
---
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|
maimi2009/Heiseigalstylegirl | maimi2009 | 2023-12-05T13:36:33Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-12-05T13:36:33Z | ---
license: unknown
---
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|
collabteza/sys-human_db | collabteza | 2023-12-07T08:55:42Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T14:16:45Z | ---
dataset_info:
features:
- name: System Prompt
dtype: string
- name: Human Prompt
dtype: string
- name: Output
dtype: string
splits:
- name: train
num_bytes: 89800
num_examples: 100
download_size: 33909
dataset_size: 89800
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sys-human_db"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
blanchon/UC_Merced | blanchon | 2023-12-05T14:30:56Z | 0 | 0 | [
"task_categories:image-classification",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"land-cover-classification",
"USGS National Map",
"USGS",
"arxiv:1911.06721",
"region:us"
]
| [
"image-classification"
]
| 2023-12-05T14:29:29Z | ---
language: en
license: unknown
task_categories:
- image-classification
paperswithcode_id: uc-merced-land-use-dataset
pretty_name: UC Merced
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- land-cover-classification
- USGS National Map
- USGS
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': agricultural
'1': airplane
'2': baseballdiamond
'3': beach
'4': buildings
'5': chaparral
'6': denseresidential
'7': forest
'8': freeway
'9': golfcourse
'10': harbor
'11': intersection
'12': mediumresidential
'13': mobilehomepark
'14': overpass
'15': parkinglot
'16': river
'17': runway
'18': sparseresidential
'19': storagetanks
'20': tenniscourt
splits:
- name: train
num_bytes: 438356371.8
num_examples: 2100
download_size: 416445676
dataset_size: 438356371.8
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# UC Merced
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
The UC Merced Land Use dataset is a land use classification dataset of 2.1k 256x256 1ft resolution RGB images of urban locations around the U.S. extracted from the USGS National Map Urban Area Imagery collection with 21 land use classes (100 images per class).
- **Paper:** https://arxiv.org/abs/1911.06721
- **Homepage:** http://weegee.vision.ucmerced.edu/datasets/landuse.html
## Description
<!-- Provide a longer summary of what this dataset is. -->
- **Total Number of Images**: 2100
- **Bands**: 3 (RGB)
- **Image Size**: 256x256
- **Resolution**: 0.3m
- **Land Cover Classes**: 21
- **Classes**: agricultural, airplane, baseballdiamond, beach, buildings, chaparral, denseresidential, forest, freeway, golfcourse, harbor, intersection, mediumresidential, mobilehomepark, overpass, parkinglot, river, runway, sparseresidential, storagetanks, tenniscourt
- **Source**: USGS
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/UC_Merced")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
UC_Merced = load_dataset("blanchon/UC_Merced")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{neumann2019indomain,
title = {In-domain representation learning for remote sensing},
author = {Maxim Neumann and Andre Susano Pinto and Xiaohua Zhai and Neil Houlsby},
year = {2019},
journal = {arXiv preprint arXiv: 1911.06721}
}
```
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|
sogeeking/vqvae_token | sogeeking | 2023-12-05T14:59:23Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T14:59:20Z | ---
dataset_info:
config_name: Burgers_Sols_Nu0.002
features:
- name: parameters
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: mean
sequence: float32
- name: std
sequence: float32
splits:
- name: train
num_bytes: 82800000
num_examples: 10000
download_size: 16914568
dataset_size: 82800000
configs:
- config_name: Burgers_Sols_Nu0.002
data_files:
- split: train
path: Burgers_Sols_Nu0.002/train-*
---
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|
notwiu/kaiokaio | notwiu | 2023-12-05T15:49:47Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T15:03:44Z | ---
license: openrail
---
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|
JinglesDados/FernandaCrispim | JinglesDados | 2023-12-05T15:14:57Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T15:14:40Z | ---
license: openrail
---
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|
wilsonorozco/wilsonptnoti | wilsonorozco | 2023-12-05T17:42:31Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T15:21:04Z | ---
license: openrail
---
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|
m-ivanov1152/hhh_test | m-ivanov1152 | 2023-12-05T15:29:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T15:29:27Z | ---
dataset_info:
features:
- name: sent
dtype: int64
- name: token
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 9137
num_examples: 284
download_size: 4380
dataset_size: 9137
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hhh_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
blanchon/OSCD_RGB | blanchon | 2023-12-05T15:37:18Z | 0 | 0 | [
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"change-detection",
"sentinel-2",
"arxiv:1810.08468",
"region:us"
]
| [
"change-detection"
]
| 2023-12-05T15:35:37Z | ---
language: en
license: unknown
task_categories:
- change-detection
paperswithcode_id: oscd
pretty_name: OSCD RGB
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- change-detection
- sentinel-2
dataset_info:
features:
- name: image1
dtype: image
- name: image2
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 50684097.0
num_examples: 14
- name: test
num_bytes: 23804288.0
num_examples: 10
download_size: 38020647
dataset_size: 74488385.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# OSCD RGB
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
The [Onera Satellite Change Detection](https://rcdaudt.github.io/oscd/) (OSCD) dataset is a change detection dataset that focuses on multispectral (MS) images taken by the [ESA Sentinel-2 satellite](https://sentinel.esa.int/web/sentinel/missions/sentinel-2). This dataset provides 24 registered image pairs from various continents, captured between the years 2015 and 2018. Along with the image pairs, the dataset includes binary change masks that highlight areas where changes have occurred. Pixel-level change ground truth is provided for all 14 training and 10 test image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms.
- **Paper:** https://arxiv.org/abs/1810.08468
- **Homepage:** https://github.com/phelber/OSCD
## Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset is available in two versions: **RGB only** (this repo) and all 13 [Multispectral (MS) Sentinel-2 bands](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial).
- **Total Number of Images**: 24
- **Bands**: 3 (RGB)
- **Image Size**: 10000x10000
- **Image Resolution**: 10m
- **Land Cover Classes**: 2
- **Classes**: no change, change
- **Source**: Sentinel-2
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/OSCD_RGB")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
OSCD_RGB = load_dataset("blanchon/OSCD_RGB")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{daudt2018urban,
title = {Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks},
author = {R. C. Daudt and B. L. Saux and Alexandre Boulch and Y. Gousseau},
journal = {IEEE International Geoscience and Remote Sensing Symposium},
year = {2018},
doi = {10.1109/IGARSS.2018.8518015},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/18755de13d2d868255bbf332fa80ec218163c74e}
}
```
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|
blanchon/OSCD_MSI | blanchon | 2023-12-05T15:43:10Z | 0 | 0 | [
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"change-detection",
"sentinel-2",
"arxiv:1810.08468",
"region:us"
]
| [
"change-detection"
]
| 2023-12-05T15:41:03Z | ---
language: en
license: unknown
task_categories:
- change-detection
paperswithcode_id: oscd
pretty_name: OSCD MSI
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- change-detection
- sentinel-2
dataset_info:
features:
- name: image1
dtype:
array3_d:
dtype: uint16
shape:
- 13
- 10000
- 10000
- name: image2
dtype:
array3_d:
dtype: uint16
shape:
- 13
- 10000
- 10000
- name: mask
dtype: image
splits:
- name: train
num_bytes: 365977532.0
num_examples: 14
- name: test
num_bytes: 172930908.0
num_examples: 10
download_size: 397786113
dataset_size: 538908440.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# OSCD MSI
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
The [Onera Satellite Change Detection](https://rcdaudt.github.io/oscd/) (OSCD) dataset is a change detection dataset that focuses on multispectral (MS) images taken by the [ESA Sentinel-2 satellite](https://sentinel.esa.int/web/sentinel/missions/sentinel-2). This dataset provides 24 registered image pairs from various continents, captured between the years 2015 and 2018. Along with the image pairs, the dataset includes binary change masks that highlight areas where changes have occurred. Pixel-level change ground truth is provided for all 14 training and 10 test image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms.
- **Paper:** https://arxiv.org/abs/1810.08468
- **Homepage:** https://github.com/phelber/OSCD
## Description
<!-- Provide a longer summary of what this dataset is. -->
The dataset is available in two versions: RGB only and all **13 MSI Bands** (this repo) [Multispectral (MS) Sentinel-2 bands](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial).
- **Total Number of Images**: 24
- **Bands**: 13 (MSI)
- **Image Size**: 10000x10000
- **Image Resolution**: 10m
- **Land Cover Classes**: 2
- **Classes**: no change, change
- **Source**: Sentinel-2
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/OSCD_MSI")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
OSCD_MSI = load_dataset("blanchon/OSCD_MSI")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{daudt2018urban,
title = {Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks},
author = {R. C. Daudt and B. L. Saux and Alexandre Boulch and Y. Gousseau},
journal = {IEEE International Geoscience and Remote Sensing Symposium},
year = {2018},
doi = {10.1109/IGARSS.2018.8518015},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/18755de13d2d868255bbf332fa80ec218163c74e}
}
```
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|
ninsean1/twitter-mental-illness-detection | ninsean1 | 2023-12-05T15:44:31Z | 0 | 0 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"mental",
"mental-health",
"bert",
"roberta",
"twitter",
"twitter-mental-health",
"mental-illness",
"region:us"
]
| [
"text-classification"
]
| 2023-12-05T15:41:47Z | ---
task_categories:
- text-classification
language:
- en
tags:
- mental
- mental-health
- bert
- roberta
- twitter
- twitter-mental-health
- mental-illness
pretty_name: B
size_categories:
- 10K<n<100K
--- | [
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|
DISCOX/where-is-disco | DISCOX | 2023-12-05T16:01:27Z | 0 | 0 | [
"size_categories:10M<n<100M",
"music",
"region:us"
]
| null | 2023-12-05T15:42:53Z | ---
tags:
- music
pretty_name: Where is DISCO-10M?
size_categories:
- 10M<n<100M
---
Dear followers of open-source and democratized music research,
DISCO-10M and its subsets had to be temporarily taken offline while we work through some legal questions. | [
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|
Seongill/squad_conflict_v2 | Seongill | 2023-12-05T15:48:42Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T15:48:30Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: masked_query
dtype: string
- name: query_embedding
sequence: float64
- name: answer_sentence
dtype: string
- name: is_named_entity
dtype: bool
- name: ent_type
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 254128812
num_examples: 34740
download_size: 185475387
dataset_size: 254128812
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Jahoorkar/Medical-Entities-Dataset | Jahoorkar | 2023-12-05T16:07:15Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T15:57:34Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4638
num_examples: 10
download_size: 5619
dataset_size: 4638
---
# Dataset Card for "Medical-Entities-Dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
davozing223/jonasmaneiro | davozing223 | 2023-12-05T15:57:42Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T15:57:42Z | ---
license: openrail
---
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|
blanchon/LEVIR_CDPlus | blanchon | 2023-12-05T16:25:56Z | 0 | 0 | [
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"remote-sensing",
"earth-observation",
"geospatial",
"satellite-imagery",
"land-cover-classification",
"region:us"
]
| [
"change-detection"
]
| 2023-12-05T16:12:26Z | ---
language: en
license: unknown
size_categories:
- 10K<n<100K
task_categories:
- change-detection
pretty_name: LEVIR CD+
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- land-cover-classification
dataset_info:
features:
- name: image1
dtype: image
- name: image2
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 2472433175.0
num_examples: 637
- name: test
num_bytes: 1316259239.0
num_examples: 348
download_size: 3788415141
dataset_size: 3788692414.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# LEVIR CD+
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
The LEVIR-CD+ dataset is an urban building change detection dataset that focuses on RGB image pairs extracted from Google Earth. This dataset consists of a total of 985 image pairs, each with a resolution of 1024x1024 pixels and a spatial resolution of 0.5 meters per pixel. The dataset includes building and land use change masks for 20 different regions in Texas, spanning the years 2002 to 2020, with a time span of 5 years between observations. LEVIR-CD+ is designed as the easier version of the S2Looking dataset, primarily due to its urban locations and near-nadir angles.
- **Paper:** https://www.mdpi.com/2072-4292/12/10/1662
- **Homepage:** https://github.com/S2Looking/Dataset
## Description
<!-- Provide a longer summary of what this dataset is. -->
The bitemporal images in LEVIR-CD are from 20 different regions that sit in several cities in Texas of the US, including Austin, Lakeway, Bee Cave, Buda, Kyle, Manor, Pflugervilletx, Dripping Springs, etc. The Figure below illustrates the geospatial distribution of our new dataset and an enlarged image patch. The captured time of our image data varies from 2002 to 2018. Images in different regions may be taken at different times. We want to introduce variations due to seasonal changes and illumination changes into our new dataset, which could help develop effective methods that can mitigate the impact of irrelevant changes on real changes.
- **Total Number of Images**: 985
- **Bands**: 3 (RGB)
- **Image Size**: 1024x1024
- **Image Resolution**: 0.5m
- **Land Cover Classes**: 2
- **Classes**: no-change, change
- **Source**: Google Earth
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/LEVIR_CDPlus")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
LEVIR_CDPlus = load_dataset("blanchon/LEVIR_CDPlus")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{Chen2020,
AUTHOR = {Chen, Hao and Shi, Zhenwei},
TITLE = {A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection},
JOURNAL = {Remote Sensing},
VOLUME = {12},
YEAR = {2020},
NUMBER = {10},
ARTICLE-NUMBER = {1662},
URL = {https://www.mdpi.com/2072-4292/12/10/1662},
ISSN = {2072-4292},
DOI = {10.3390/rs12101662}
}
```
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|
mikehemberger/medicinal-plants | mikehemberger | 2023-12-05T18:45:54Z | 0 | 0 | [
"task_categories:question-answering",
"task_categories:translation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
"language:de",
"license:apache-2.0",
"biology",
"art",
"medical",
"region:us"
]
| [
"question-answering",
"translation",
"summarization",
"conversational",
"feature-extraction"
]
| 2023-12-05T16:18:14Z | ---
license: apache-2.0
task_categories:
- question-answering
- translation
- summarization
- conversational
- feature-extraction
language:
- en
- de
tags:
- biology
- art
- medical
size_categories:
- n<1K
---
A growing dataset about medicinal plants. We plan to construct a multimodal dataset with images and text content extracted from
1) books that went out of copywrite and
2) diverse and high-quality video data taken via smart phone and various lenses with a DSLR camera (24mm Macro, 50mm, 100mm Macro and a 24-120mm Zoom lens).
Parts of the iNaturalist and PlantNet300K datasets will be integrated as well to cover a wide spectrum of the kingdom Plantae.
The resulting dataset should be able to power a free visual search / retrieval application for smart phones that integrates vast amounts of detailed knowledge about medicinal plants and their lifecycle. | [
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|
goldstream/bolehpisan | goldstream | 2023-12-05T16:37:25Z | 0 | 0 | [
"license:other",
"region:us"
]
| null | 2023-12-05T16:20:48Z | ---
license: other
license_name: pelicula
license_link: LICENSE
---
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|
Hjallti/solar-strike | Hjallti | 2023-12-05T16:59:32Z | 0 | 0 | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"sol",
"region:us"
]
| [
"text-generation",
"text-classification",
"token-classification",
"question-answering"
]
| 2023-12-05T16:28:27Z | ---
license: mit
task_categories:
- text-generation
- text-classification
- token-classification
- question-answering
language:
- en
tags:
- sol
pretty_name: solar-strike
size_categories:
- 1K<n<10K
---
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|
lawful-good-project/ipc_decisions_4k_selected | lawful-good-project | 2023-12-05T16:34:52Z | 0 | 0 | [
"license:gpl-3.0",
"region:us"
]
| null | 2023-12-05T16:34:00Z | ---
license: gpl-3.0
---
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|
helvioviana/Clone | helvioviana | 2023-12-05T16:41:46Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T16:39:26Z | ---
license: openrail
---
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|
plaguss/curation-ultrafeedback-bad-rated | plaguss | 2023-12-05T16:56:01Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T16:43:57Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: generations
dtype: string
- name: score_best_overall
dtype: float64
- name: rating-distilabel-gpt4
dtype: float64
- name: rationale-distilabel-gpt4
dtype: string
splits:
- name: train
num_bytes: 3681620
num_examples: 1968
download_size: 1959130
dataset_size: 3681620
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Harshraj4423/Himanshu | Harshraj4423 | 2023-12-05T17:15:57Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T16:57:24Z | ---
license: apache-2.0
---
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|
marcos292/CARLOS | marcos292 | 2023-12-05T17:01:18Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T16:59:41Z | ---
license: openrail
---
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|
wid4soe/182-final-proj | wid4soe | 2023-12-05T17:53:51Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T17:19:39Z | ---
dataset_info:
features:
- name: original_image
dtype: string
- name: edit_prompt
dtype: string
- name: new_image
dtype: string
splits:
- name: test
num_bytes: 18956
num_examples: 161
- name: validation
num_bytes: 6584
num_examples: 55
- name: train
num_bytes: 68238
num_examples: 570
download_size: 34004
dataset_size: 93778
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
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|
kdtv/kk | kdtv | 2023-12-05T17:46:45Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-05T17:46:45Z | ---
license: mit
---
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|
ihaflix1/herbert | ihaflix1 | 2023-12-05T17:58:49Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T17:58:16Z | ---
license: openrail
---
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|
bertbsb/vozprtim | bertbsb | 2023-12-05T18:13:32Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T18:11:36Z | ---
license: openrail
---
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|
ajibawa-2023/Code-74k-ShareGPT | ajibawa-2023 | 2023-12-08T19:05:49Z | 0 | 1 | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"region:us"
]
| [
"conversational"
]
| 2023-12-05T18:25:14Z | ---
license: cc-by-nc-nd-4.0
task_categories:
- conversational
language:
- en
tags:
- code
size_categories:
- 10K<n<100K
---
**Code-74k-ShareGPT**
This dataset is in Vicuna/ShareGPT format. There are around 74000 set of conversations. Each set having 2 conversations.
Along with Python, Java, JavaScript, GO, C++, Rust etc. code with detailed explanation are provided. It is built upon using my existing Dataset [Python-Code-23k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT).
Additional dataset was generated using GPT-3.5, GPT-4 etc. | [
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|
nid3rf/Lilgiela33 | nid3rf | 2023-12-05T18:43:26Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T18:42:51Z | ---
license: openrail
---
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|
wid4soe/182_3 | wid4soe | 2023-12-05T20:36:44Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T18:43:58Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: new_image
dtype: image
splits:
- name: train
num_bytes: 1661844.0
num_examples: 54
download_size: 1660946
dataset_size: 1661844.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
hippocrates/OphthoFillIN_train | hippocrates | 2023-12-05T18:49:18Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T18:49:15Z | ---
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: 15076037
num_examples: 18389
- name: valid
num_bytes: 1938116
num_examples: 2298
- name: test
num_bytes: 1938116
num_examples: 2298
download_size: 6134435
dataset_size: 18952269
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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|
hippocrates/OphthoLastSent_train | hippocrates | 2023-12-05T18:50:58Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T18:50:49Z | ---
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: 259001731
num_examples: 82778
- name: valid
num_bytes: 32501570
num_examples: 10347
- name: test
num_bytes: 32595866
num_examples: 10348
download_size: 159395740
dataset_size: 324099167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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|
hippocrates/OphthoFreeResponse_train | hippocrates | 2023-12-05T19:00:24Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T18:52:44Z | ---
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: 519751
num_examples: 820
- name: valid
num_bytes: 519751
num_examples: 820
- name: test
num_bytes: 64223
num_examples: 103
download_size: 324203
dataset_size: 1103725
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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|
hippocrates/OphthoQA_train | hippocrates | 2023-12-05T19:06:19Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T18:53:20Z | ---
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: 2731207
num_examples: 2833
- name: valid
num_bytes: 346480
num_examples: 354
- name: test
num_bytes: 346480
num_examples: 354
download_size: 1342514
dataset_size: 3424167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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|
noahshinn/squad-embeddings | noahshinn | 2023-12-05T19:20:32Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T19:17:18Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: text
dtype: string
- name: answer_embedding
sequence: float64
splits:
- name: train
num_bytes: 122527254
num_examples: 8760
- name: test
num_bytes: 14707545
num_examples: 1057
download_size: 92115032
dataset_size: 137234799
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
spwencer/sukuna | spwencer | 2023-12-05T19:31:06Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T19:29:42Z | ---
license: openrail
---
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|
caiosoares26/zevaqueiro | caiosoares26 | 2023-12-05T19:57:42Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T19:57:08Z | ---
license: openrail
---
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|
c4ba/minhavoz98 | c4ba | 2023-12-06T14:26:10Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T19:59:33Z | ---
license: openrail
---
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|
KAUE2006/QuicoNelsonMachadoVozAntiga | KAUE2006 | 2023-12-05T20:04:34Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T20:02:25Z | ---
license: openrail
---
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|
Dubhe-zmc/high_quality | Dubhe-zmc | 2023-12-06T01:46:31Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T20:17:40Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 64392282484.03
num_examples: 71065
download_size: 53714621716
dataset_size: 64392282484.03
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
laiyer/prompt-injection-benchmark | laiyer | 2023-12-10T21:13:47Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T20:30:56Z | ---
license: apache-2.0
---
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|
hippocrates/PubmedKeyword_train | hippocrates | 2023-12-05T20:36:27Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T20:36:19Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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: 61599434
num_examples: 20000
download_size: 30080559
dataset_size: 61599434
---
# Dataset Card for "PubmedKeyword_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
hippocrates/PubmedNext_train | hippocrates | 2023-12-05T21:15:06Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T21:15:05Z | ---
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: 50728857
num_examples: 20000
download_size: 26436472
dataset_size: 50728857
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
cmu-mlsp/librispeech960-encodec1024_asr_tokenized_final | cmu-mlsp | 2023-12-05T21:17:43Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T21:15:39Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: validation_tts
path: data/validation_tts-*
- split: test
path: data/test-*
- split: test_tts
path: data/test_tts-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 7058957907
num_examples: 281241
- name: validation
num_bytes: 79544090
num_examples: 5406
- name: validation_tts
num_bytes: 39772045
num_examples: 2703
- name: test
num_bytes: 39828951
num_examples: 2620
- name: test_tts
num_bytes: 39828951
num_examples: 2620
download_size: 620258987
dataset_size: 7257931944
---
# Dataset Card for "librispeech960-encodec1024_asr_tokenized_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
dresen/fleurs_da_pseudo_labelled | dresen | 2023-12-05T21:52:42Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T21:26:28Z | ---
dataset_info:
config_name: da_dk
features:
- name: id
dtype: int32
- name: num_samples
dtype: int32
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: raw_transcription
dtype: string
- name: gender
dtype:
class_label:
names:
'0': male
'1': female
'2': other
- name: lang_id
dtype:
class_label:
names:
'0': af_za
'1': am_et
'2': ar_eg
'3': as_in
'4': ast_es
'5': az_az
'6': be_by
'7': bg_bg
'8': bn_in
'9': bs_ba
'10': ca_es
'11': ceb_ph
'12': ckb_iq
'13': cmn_hans_cn
'14': cs_cz
'15': cy_gb
'16': da_dk
'17': de_de
'18': el_gr
'19': en_us
'20': es_419
'21': et_ee
'22': fa_ir
'23': ff_sn
'24': fi_fi
'25': fil_ph
'26': fr_fr
'27': ga_ie
'28': gl_es
'29': gu_in
'30': ha_ng
'31': he_il
'32': hi_in
'33': hr_hr
'34': hu_hu
'35': hy_am
'36': id_id
'37': ig_ng
'38': is_is
'39': it_it
'40': ja_jp
'41': jv_id
'42': ka_ge
'43': kam_ke
'44': kea_cv
'45': kk_kz
'46': km_kh
'47': kn_in
'48': ko_kr
'49': ky_kg
'50': lb_lu
'51': lg_ug
'52': ln_cd
'53': lo_la
'54': lt_lt
'55': luo_ke
'56': lv_lv
'57': mi_nz
'58': mk_mk
'59': ml_in
'60': mn_mn
'61': mr_in
'62': ms_my
'63': mt_mt
'64': my_mm
'65': nb_no
'66': ne_np
'67': nl_nl
'68': nso_za
'69': ny_mw
'70': oc_fr
'71': om_et
'72': or_in
'73': pa_in
'74': pl_pl
'75': ps_af
'76': pt_br
'77': ro_ro
'78': ru_ru
'79': sd_in
'80': sk_sk
'81': sl_si
'82': sn_zw
'83': so_so
'84': sr_rs
'85': sv_se
'86': sw_ke
'87': ta_in
'88': te_in
'89': tg_tj
'90': th_th
'91': tr_tr
'92': uk_ua
'93': umb_ao
'94': ur_pk
'95': uz_uz
'96': vi_vn
'97': wo_sn
'98': xh_za
'99': yo_ng
'100': yue_hant_hk
'101': zu_za
'102': all
- name: language
dtype: string
- name: lang_group_id
dtype:
class_label:
names:
'0': western_european_we
'1': eastern_european_ee
'2': central_asia_middle_north_african_cmn
'3': sub_saharan_african_ssa
'4': south_asian_sa
'5': south_east_asian_sea
'6': chinese_japanase_korean_cjk
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 1732021021.405
num_examples: 2465
- name: test
num_bytes: 678265026.0
num_examples: 930
download_size: 2361072176
dataset_size: 2410286047.4049997
configs:
- config_name: da_dk
data_files:
- split: train
path: da_dk/train-*
- split: test
path: da_dk/test-*
---
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|
israelfx/brunoleonardo | israelfx | 2023-12-05T21:36:19Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T21:32:40Z | ---
license: openrail
---
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|
Lifan-Z/fasta_datasets | Lifan-Z | 2023-12-05T22:12:59Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"biology",
"region:us"
]
| [
"text-generation"
]
| 2023-12-05T21:40:11Z | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- biology
pretty_name: fasta_datasets
size_categories:
- n<1K
---
natural -- Toxin-antitoxin protein sequences from the .faa file.
random -- Sequences generated by the model 'nferruz/ProtGPT2'.
protgpt2 -- Sequences generated by the finetuned model 'Lifan-Z/protGPT2_5'. | [
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|
Kornberg/landsat_unfiltered | Kornberg | 2023-12-05T22:04:30Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T21:43:03Z | ---
dataset_info:
features:
- name: input
dtype: image
- name: target
dtype: image
- name: image_caption
dtype: string
splits:
- name: train
num_bytes: 1510993883.265
num_examples: 34085
download_size: 1485983470
dataset_size: 1510993883.265
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
deivsu/MODELYUNA | deivsu | 2023-12-05T22:06:15Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T21:58:54Z | ---
license: openrail
---
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|
harpreetsahota/diverse-token-sampler | harpreetsahota | 2023-12-05T22:08:59Z | 0 | 1 | [
"license:mit",
"region:us"
]
| null | 2023-12-05T22:06:15Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 7838
num_examples: 68
download_size: 7314
dataset_size: 7838
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
pretty_name: Diverse Token Sampler
---
# 🌈 Diverse Token Sampler Dataset 🌟
## Overview 📜
Welcome to the `DiverseTokenSampler` dataset! 🚀 This one-of-a-kind collection is ingeniously crafted to challenge and test the boundaries of LLMs, especially in evaluating their versatility and robustness. 🤖 It encompasses a broad spectrum of prompts, from conventional linguistic constructs to the most perplexing arrays of mixed-language scripts, emojis, 🎉 technical code snippets, and even nonsensical strings. An invaluable resource for researchers and developers 🧑💻 aiming to probe the depths and limitations of their NLP models with diverse and complex inputs.
## Contents 📚
`DiverseTokenSampler` includes an eclectic mix of prompt types:
- **📖 Narrative Beginnings**: Unleash creativity in storytelling.
- **🌄 Descriptive Texts**: Paint vivid pictures with words.
- **💬 Dialogue Initiations**: Spark engaging conversations.
- **🔬 Technical and Academic Texts**: Dive into specialized knowledge.
- **🎶 Poetic Openings**: Explore the beauty of lyrical language.
- **💡 Thought-Provoking Statements**: Stimulate reflective thinking.
- **🏛 Historical Contexts**: Travel through time with historical narratives.
- **🌌 Fictional World-building**: Craft realms of imagination.
- **🔍 Mystery Setups**: Invoke intrigue and curiosity.
- **🧩 Mixed Content**: A kaleidoscope of languages, emojis, and code.
- **❓ Non-linguistic**: Challenge models with abstract character assortments.
## Applications 🛠
Use `DiverseTokenSampler` for:
- **🎓 Model Training and Fine-Tuning**: Augment models' linguistic versatility.
- **🔗 Robustness Testing**: Gauge models against unusual and unexpected inputs.
- **⚖️ Bias Detection**: Uncover and address potential biases.
- **🧠 Language Understanding Evaluation**: Assess comprehension across varied prompts.
## Contribution 🤝
Eager for your ideas and improvements! 🌟 If you have novel prompts or enhancements, feel free to submit a pull request or open an issue.
## License 📄
This dataset is open-sourced under the [MIT License](LICENSE.md).
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|
TheDKBR/thedk | TheDKBR | 2023-12-05T22:09:56Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T22:09:23Z | ---
license: openrail
---
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|
noril13/claire-dialogue-subset | noril13 | 2023-12-05T23:20:12Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| null | 2023-12-05T23:19:28Z | ---
license: cc-by-nc-sa-4.0
---
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|
Vertex-Test/FireSmokeDataset | Vertex-Test | 2023-12-05T23:23:12Z | 0 | 1 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-05T23:20:08Z | ---
license: apache-2.0
---
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|
Dloring1/processed_demo | Dloring1 | 2023-12-05T23:35:20Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T23:35:19Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: text_path
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 533743
num_examples: 10
download_size: 303556
dataset_size: 533743
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Dloring1/processed_demo_2 | Dloring1 | 2023-12-05T23:50:21Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-05T23:38:19Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 532949
num_examples: 10
download_size: 302072
dataset_size: 532949
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
robson2286/Josecarlos | robson2286 | 2023-12-05T23:44:47Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-05T23:44:25Z | ---
license: openrail
---
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|
mergedlm/MergedLM-License | mergedlm | 2023-12-05T23:53:21Z | 0 | 0 | [
"language:en",
"license:other",
"mergedlm",
"region:us"
]
| null | 2023-12-05T23:52:34Z | ---
license: other
license-name: mergedlm
license-link: README.md
language:
- en
tags:
- mergedlm
---
This software is licensed under the latest version of the MergedLM License, which takes precedent over the provided version.
# MergedLM License
Version 1.0, December 2023
## Overview
This overview is not legal advice or a replacement for the license text.
The MergedLM license is an open and permissive license for artificial intelligence models, especially language models and large neural networks. It is not technically an open source license, however it allows for commercial use with several restrictions.
**Permissions**
* Commercial use
* Modification
* Distribution
* Patent use
* Private use
**Conditions**
* Disclose source
* License and copyright notice
* Same license
* Commercial use restriction on derivative works (you may use the base model for commercial purposes, however there is a restriction on derivative works - see the license for details)
* Restriction on directly providing a API/hosted service
* Attribution required
* Ethical restrictions
**Limitations**
* Liability
* Warranty
## Definitions
* "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by this document
* "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
* "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
* "Model" shall mean the preferred form of usage, including but not limited to artificial intelligence model weights, software source code, documentation source, and configuration files.
* "Object" form shall mean any form resulting from mechanical transformation or translation of a Model, including but not limited to compiled object code, generated documentation, quantized models, and conversions to other media types.
* "Work" shall mean the work of authorship, whether in Model or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work.
* "Derivative Works" shall mean any work, whether in Model or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. Derivative Works include fine-tuned models, modified models, and trimmed/distilled models.
* "Commercial Settings" shall mean any any form, setting, enterprise, business, or organization that uses the Work or a Derivative Work for commercial purposes or purposes that generate revenue, charge for products, etc, including services with advertisements, donations, "tip jars," etc.
* "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
* "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
## Grant of Copyright License
Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, and distribute the Work and such Derivative Works in Model or Object form, so long as You comply with this license and the Restrictions contained within it. Upon violation of the License or Restrictions, the License will be immediately revoked.
## Restrictions
Usage of the Work is subject to the following restrictions:
* You may not use the Model or any Derivative Works for any military weapons purposes or in the service of nuclear proliferation or nuclear weapons technology, purposes of surveillance, including nay research or development, purposes of generating and disseminating information of content, in any context (e.g. posts, articles, social media posts, chatbots, blogs, or other kinds of automated bots) without clear and prominent notices that the content has been generated by the Model and clear and prominent attribution to the Model, purposes of "real time" remote biometric processing or identification systems in publicly accessible spaces, purposes of genocide, purposes of harming individuals or living animals, fully automated decision making without human monitoring, and/or purposes of predictive administration of justice, law enforcement, immigration, and/or asylum processes.
* You may not use the Model or any Derivative Works for any purpose that violates any applicable national, federal, state, provincial, local, or international laws or regulations, to generate verifiably false information and/or content with the purpose of harming others, to generate personally identifiable information with the purpose of harming others, to defame, disparage, or otherwise harass others, to write malware, worms, or other malicious software, to create "spam" or other unwanted content, to defame, disparage, or otherwise harass others, for any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior, known or predicted personal or personality characteristics, religion, race, and/or ethnicity, to exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical, or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical, psychological, of financial harm, for any use intended to or which ahs the effect of discriminating against individuals or groups based on legally protected characteristics or categories, and/or to provide medical advice.
* You may not use the Work or a Derivative Work for any purpose that violates the laws and regulations of the United States of America or the State of California.
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|
Dloring1/processed_demo_3 | Dloring1 | 2023-12-06T00:33:56Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-06T00:08:07Z | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 278977
num_examples: 5
download_size: 159137
dataset_size: 278977
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
deivsu/EUNBIMODEL | deivsu | 2023-12-06T00:08:48Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-06T00:08:25Z | ---
license: openrail
---
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|
TheDKBR/thedkbr | TheDKBR | 2023-12-06T00:14:16Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-06T00:12:19Z | ---
license: openrail
---
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|
Zack157/junin | Zack157 | 2023-12-06T01:02:34Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-06T01:01:55Z | ---
license: openrail
---
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|
ebony59/20231130_chai_prize_reward_model_data | ebony59 | 2023-12-06T01:38:33Z | 0 | 1 | [
"region:us"
]
| null | 2023-12-06T01:38:29Z | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 71814173
num_examples: 39576
download_size: 38065021
dataset_size: 71814173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "20231130_chai_prize_reward_model_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
LucasGomasCunha/Voz_Tinoco | LucasGomasCunha | 2023-12-06T02:37:49Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-06T02:35:17Z | ---
license: openrail
---
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|
ekolasky/DWIEConsolForCustomLEDForQA | ekolasky | 2023-12-06T03:53:08Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-06T02:40:19Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
- name: grouping_vector
sequence:
sequence: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 6322086
num_examples: 720
- name: validation
num_bytes: 303195
num_examples: 34
download_size: 1148360
dataset_size: 6625281
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
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