Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
_id: string
id: string
author: string
cardData: string
lastModified: timestamp[ns]
likes: int64
trendingScore: double
private: bool
sha: string
subdomain: string
sdk: string
tags: list<element: string>
child 0, element: string
createdAt: timestamp[ns]
siblings: list<element: struct<rfilename: string>>
child 0, element: struct<rfilename: string>
child 0, rfilename: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1681
to
{'_id': Value('string'), 'id': Value('string'), 'author': Value('string'), 'cardData': Value('string'), 'disabled': Value('bool'), 'gated': Value('string'), 'lastModified': Value('timestamp[ns]'), 'likes': Value('int64'), 'trendingScore': Value('float64'), 'private': Value('bool'), 'sha': Value('string'), 'description': Value('string'), 'downloads': Value('int64'), 'downloadsAllTime': Value('int64'), 'tags': Value('string'), 'createdAt': Value('timestamp[ns]'), 'paperswithcode_id': Value('string'), 'citation': Value('string'), 'embedding': List(Value('float32'), length=128)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 609, in wrapped
for item in generator(*args, **kwargs):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/lance/lance.py", line 177, in _generate_tables
yield Key(frag_idx, batch_idx), self._cast_table(table)
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/lance/lance.py", line 147, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
_id: string
id: string
author: string
cardData: string
lastModified: timestamp[ns]
likes: int64
trendingScore: double
private: bool
sha: string
subdomain: string
sdk: string
tags: list<element: string>
child 0, element: string
createdAt: timestamp[ns]
siblings: list<element: struct<rfilename: string>>
child 0, element: struct<rfilename: string>
child 0, rfilename: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1681
to
{'_id': Value('string'), 'id': Value('string'), 'author': Value('string'), 'cardData': Value('string'), 'disabled': Value('bool'), 'gated': Value('string'), 'lastModified': Value('timestamp[ns]'), 'likes': Value('int64'), 'trendingScore': Value('float64'), 'private': Value('bool'), 'sha': Value('string'), 'description': Value('string'), 'downloads': Value('int64'), 'downloadsAllTime': Value('int64'), 'tags': Value('string'), 'createdAt': Value('timestamp[ns]'), 'paperswithcode_id': Value('string'), 'citation': Value('string'), 'embedding': List(Value('float32'), length=128)}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1328, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1919, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
_id
string | id
string | author
string | cardData
string | disabled
bool | gated
string | lastModified
timestamp[ns] | likes
int64 | trendingScore
float64 | private
bool | sha
string | description
string | downloads
int64 | downloadsAllTime
int64 | tags
string | createdAt
timestamp[ns] | paperswithcode_id
string | citation
string | embedding
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
69524c8ad001e56220ced9bc
|
Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b
|
Alibaba-Apsara
|
{"license": "cc-by-4.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "math", "scientific-qa", "instruction-following", "reasoning", "thinking", "gpt-oss-120b", "distill"], "size_categories": ["435K"], "configs": [{"config_name": "stage1", "data_files": "Superior-Reasoning-SFT-gpt-oss-120b-stage1-train-data.jsonl", "features": [{"name": "uuid", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "meta", "dtype": "string"}]}, {"config_name": "stage2", "data_files": "Superior-Reasoning-SFT-gpt-oss-120b-stage2-train-data.jsonl", "features": [{"name": "uuid", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "meta", "dtype": "string"}]}]}
| false
|
False
| 2026-01-15T06:39:55
| 236
| 115
| false
|
e9d54e2a3f376fd5c62cafd3c4c99b304cdda698
|
Superior-Reasoning-SFT-gpt-oss-120b
🚀 Overview
The Superior-Reasoning-SFT-gpt-oss-120b dataset is a high-quality, open-source collection containing 435K samples designed to democratize the training of high-performance Long Chain-of-Thought (Long-CoT) models. Unlike standard distilled datasets that rely on random sampling or heuristic filtering, Superior-Reasoning-SFT-gpt-oss-120b is constructed using a principled Distribution-Aligned Sequence… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b.
| 12,983
| 12,983
|
['task_categories:text-generation' 'language:en' 'license:cc-by-4.0'
'size_categories:100K<n<1M' 'format:json' 'modality:text'
'library:datasets' 'library:pandas' 'library:polars'
'library:mlcroissant' 'arxiv:2601.09088' 'arxiv:2512.20908' 'region:us'
'code' 'math' 'scientific-qa' 'instruction-following' 'reasoning'
'thinking' 'gpt-oss-120b' 'distill']
| 2025-12-29T09:40:26
| null | null |
[
0.3089316785335541,
0.5958349704742432,
0.5629348754882812,
0.25809532403945923,
0.7968100309371948,
0.4372105300426483,
0.7240143418312073,
0.4595176875591278,
0.9374338984489441,
0.5523331165313721,
0.7086126804351807,
0.2317904531955719,
0.33883732557296753,
0.7165320515632629,
0.1670754849910736,
0.21439510583877563,
0.5796294808387756,
0.6567818522453308,
0.39276474714279175,
0.11251933127641678,
0.12859678268432617,
0.42888686060905457,
0.08490365743637085,
0.7838922739028931,
0.7847870588302612,
0.7033563852310181,
0.35290268063545227,
0.008851968683302402,
0.6693786382675171,
0.40914303064346313,
0.9749311208724976,
0.7333397269248962,
0.7579429149627686,
0.5698100924491882,
0.27816739678382874,
0.6425039172172546,
0.8500996232032776,
0.19924776256084442,
0.26744335889816284,
0.5322825908660889,
0.7328881621360779,
0.9961727857589722,
0.9972332715988159,
0.5759282112121582,
0.6043659448623657,
0.7265216112136841,
0.4551341235637665,
0.8954934477806091,
0.5311533808708191,
0.9634636044502258,
0.7880859375,
0.6056382060050964,
0.5609081983566284,
0.7335776090621948,
0.4187672436237335,
0.9688266515731812,
0.4467526972293854,
0.6129561066627502,
0.9508609771728516,
0.5897073745727539,
0.2388278692960739,
0.4503585994243622,
0.1932346522808075,
0.4359667897224426,
0.6614896059036255,
0.46143755316734314,
0.023709174245595932,
0.46212998032569885,
0.4611108899116516,
0.165871262550354,
0.8275460600852966,
0.9753865599632263,
0.4154370129108429,
0.5912226438522339,
0.36115193367004395,
0.6570637226104736,
0.0935693234205246,
0.10754547268152237,
0.7399710416793823,
0.29777616262435913,
0.3402411639690399,
0.06548082828521729,
0.5971262454986572,
0.6169194579124451,
0.8403961658477783,
0.15609204769134521,
0.8660869002342224,
0.23286698758602142,
0.4459291696548462,
0.8522599935531616,
0.49948850274086,
0.17168952524662018,
0.8392408490180969,
0.4040754437446594,
0.7055819034576416,
0.4673680067062378,
0.14010119438171387,
0.31502699851989746,
0.4461522102355957,
0.882146954536438,
0.23581919074058533,
0.003566600615158677,
0.7701588273048401,
0.1556416153907776,
0.43230023980140686,
0.7345097661018372,
0.5209575891494751,
0.5389747023582458,
0.94219571352005,
0.8654726147651672,
0.8530828952789307,
0.8899089694023132,
0.9207495450973511,
0.30981406569480896,
0.8996129631996155,
0.34888017177581787,
0.2685559093952179,
0.4446038007736206,
0.43652909994125366,
0.7853471636772156,
0.16378335654735565,
0.6111283302307129,
0.587296187877655,
0.6562367081642151,
0.34811779856681824,
0.5922603607177734,
0.017573704943060875,
0.6830538511276245
] |
69676b65aeecdadc87f8da8e
|
facebook/action100m-preview
|
facebook
|
{"license": "fair-noncommercial-research-license", "language": ["en"], "tags": ["video", "action"], "size_categories": ["10M<n<100M"]}
| false
|
False
| 2026-01-14T14:24:13
| 100
| 91
| false
|
c9404b5c9772d6883a2f062945273f171b585275
|
Action100M: A Large-scale Video Action Dataset
Our data can be loaded from the 🤗 huggingface repo at facebook/action100m-preview where we released 10% of the full Action100M for preview. For examples of loading from local parquet files (from cloned repo) and visualization, see our GitHub repo.
from datasets import load_dataset
dataset = load_dataset(
"parquet",
data_files=f"hf://datasets/facebook/Action100M-preview/data/*.parquet",
streaming=True,
)
it =… See the full description on the dataset page: https://huggingface.co/datasets/facebook/action100m-preview.
| 2,908
| 2,908
|
['language:en' 'license:fair-noncommercial-research-license'
'size_categories:100K<n<1M' 'format:parquet' 'modality:text'
'modality:video' 'library:datasets' 'library:dask' 'library:polars'
'library:mlcroissant' 'region:us' 'video' 'action']
| 2026-01-14T10:09:41
| null | null |
[
0.42302507162094116,
0.835867702960968,
0.5068385601043701,
0.1327640563249588,
0.986899197101593,
0.8992253541946411,
0.3560081720352173,
0.32750505208969116,
0.8084222674369812,
0.19109703600406647,
0.34933358430862427,
0.5686920881271362,
0.519487202167511,
0.6172237992286682,
0.33704859018325806,
0.6784782409667969,
0.36872145533561707,
0.5898417830467224,
0.5388248562812805,
0.35920408368110657,
0.01560364942997694,
0.7612704038619995,
0.38306495547294617,
0.26104089617729187,
0.9516863822937012,
0.32915058732032776,
0.7039633393287659,
0.41754862666130066,
0.048626795411109924,
0.7397415637969971,
0.5825143456459045,
0.18983979523181915,
0.8057771921157837,
0.4704039394855499,
0.6579172611236572,
0.23743553459644318,
0.7284781336784363,
0.8639475703239441,
0.6876272559165955,
0.8019775152206421,
0.3705703914165497,
0.4702056348323822,
0.5146085619926453,
0.9204332232475281,
0.9446708559989929,
0.6801378726959229,
0.7860079407691956,
0.5690406560897827,
0.9900528788566589,
0.5599280595779419,
0.8221160769462585,
0.20840494334697723,
0.7571029663085938,
0.8762394189834595,
0.12532001733779907,
0.2539534270763397,
0.359062522649765,
0.023265285417437553,
0.6538854837417603,
0.12185687571763992,
0.05675100162625313,
0.8761706352233887,
0.5471232533454895,
0.6014668345451355,
0.699856162071228,
0.3972164988517761,
0.5005174279212952,
0.4634583592414856,
0.7863407135009766,
0.2974858283996582,
0.9426965117454529,
0.6851482391357422,
0.2750146687030792,
0.6700133085250854,
0.5463738441467285,
0.10872485488653183,
0.852972686290741,
0.13886266946792603,
0.3970728814601898,
0.39932066202163696,
0.7308062314987183,
0.8543099761009216,
0.26056191325187683,
0.4354014992713928,
0.0901624783873558,
0.2210417240858078,
0.35291576385498047,
0.014639048837125301,
0.5468431115150452,
0.49666911363601685,
0.8630680441856384,
0.5908260941505432,
0.9409681558609009,
0.2606368362903595,
0.5364230871200562,
0.7455044984817505,
0.3835567235946655,
0.5202147364616394,
0.6414149403572083,
0.7454091906547546,
0.16131852567195892,
0.08052350580692291,
0.8505926132202148,
0.4195823669433594,
0.6774146556854248,
0.49137410521507263,
0.5429865717887878,
0.8162786364555359,
0.4034561812877655,
0.4437500238418579,
0.703145444393158,
0.913536787033081,
0.16434964537620544,
0.8384164571762085,
0.6968312859535217,
0.1199311763048172,
0.9331443309783936,
0.8912106156349182,
0.6814613938331604,
0.41971272230148315,
0.08957388997077942,
0.2007792741060257,
0.48863714933395386,
0.4392743408679962,
0.02367931604385376,
0.09744176268577576,
0.39666157960891724,
0.017673691734671593
] |
696b2406e6c69ff4f49745f4
|
sojuL/RubricHub_v1
|
sojuL
|
{"license": "apache-2.0", "language": ["zh", "en"], "tags": ["medical", "science", "wirting", "isntruction", "chat", "general"], "pretty_name": "RubricHub", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "reinforcement-learning", "question-answering"]}
| false
|
False
| 2026-01-20T07:16:51
| 81
| 81
| false
|
bec50742963ed3672391fecbcc4b60067b9fa8bc
|
RubricHub_v1
RubricHub is a large-scale (approximately 110K), multi-domain dataset that provides high-quality rubric-based supervision for open-ended generation tasks. It is constructed via an automated coarse-to-fine rubric generation framework, which integrates principle-guided synthesis, multi-model aggregation, and difficulty evolution to produce comprehensive and highly discriminative evaluation criteria, overcoming the supervision ceiling of coarse or static rubrics.… See the full description on the dataset page: https://huggingface.co/datasets/sojuL/RubricHub_v1.
| 390
| 390
|
['task_categories:text-generation'
'task_categories:reinforcement-learning'
'task_categories:question-answering' 'language:zh' 'language:en'
'license:apache-2.0' 'size_categories:100K<n<1M' 'format:parquet'
'modality:text' 'library:datasets' 'library:dask' 'library:polars'
'library:mlcroissant' 'arxiv:2601.08430' 'region:us' 'medical' 'science'
'wirting' 'isntruction' 'chat' 'general']
| 2026-01-17T05:54:14
| null | null |
[
0.49041885137557983,
0.8460575342178345,
0.9790360331535339,
0.08758077770471573,
0.5944743752479553,
0.8000954389572144,
0.3159536123275757,
0.8329492211341858,
0.33446627855300903,
0.8034713268280029,
0.38892868161201477,
0.330281525850296,
0.33613070845603943,
0.1632225066423416,
0.23664133250713348,
0.6493561863899231,
0.6061586737632751,
0.8661786317825317,
0.6577848792076111,
0.7687317728996277,
0.008797397837042809,
0.02263779379427433,
0.41608530282974243,
0.2739258408546448,
0.20557193458080292,
0.7007583975791931,
0.3267110586166382,
0.9761824011802673,
0.6998775601387024,
0.7740248441696167,
0.08271767944097519,
0.8135975003242493,
0.20523862540721893,
0.31932803988456726,
0.10953886061906815,
0.4331963360309601,
0.643587052822113,
0.7159020304679871,
0.22946231067180634,
0.7460468411445618,
0.5954742431640625,
0.5521305799484253,
0.40392810106277466,
0.639167308807373,
0.7378615140914917,
0.35092565417289734,
0.19462467730045319,
0.3884451687335968,
0.5648941397666931,
0.2981978952884674,
0.13485312461853027,
0.6499578356742859,
0.43648582696914673,
0.3156878650188446,
0.3916842043399811,
0.8621839880943298,
0.6055179834365845,
0.16383931040763855,
0.727716863155365,
0.7607055902481079,
0.7630583643913269,
0.805081844329834,
0.905372679233551,
0.37169337272644043,
0.7676843404769897,
0.4010913372039795,
0.6495163440704346,
0.46021825075149536,
0.611596405506134,
0.7718749046325684,
0.5338575839996338,
0.3767832815647125,
0.6444467306137085,
0.44581323862075806,
0.605650007724762,
0.6271697282791138,
0.6633744835853577,
0.5834250450134277,
0.4259984493255615,
0.053023289889097214,
0.065115787088871,
0.6321831345558167,
0.475208044052124,
0.16576619446277618,
0.012196260504424572,
0.48173990845680237,
0.8826897144317627,
0.37875640392303467,
0.3638276159763336,
0.9793826341629028,
0.9144333600997925,
0.35041093826293945,
0.978257417678833,
0.755325436592102,
0.14180439710617065,
0.799485981464386,
0.4236387014389038,
0.21744810044765472,
0.8051720857620239,
0.9289972186088562,
0.15835365653038025,
0.964019775390625,
0.2543700039386749,
0.5395866632461548,
0.21830914914608002,
0.04296219348907471,
0.34521597623825073,
0.4354521930217743,
0.09908493608236313,
0.1742819845676422,
0.7342286109924316,
0.26393812894821167,
0.8715935945510864,
0.3950997591018677,
0.13418720662593842,
0.8788676261901855,
0.0889650210738182,
0.9840443730354309,
0.3171960115432739,
0.3383234441280365,
0.3173443377017975,
0.5653310418128967,
0.4115849435329437,
0.3579041361808777,
0.4480062425136566,
0.9683549404144287,
0.3328586518764496,
0.6302879452705383
] |
6969078587ce326016ddda46
|
lightonai/LightOnOCR-mix-0126
|
lightonai
|
{"dataset_info": {"features": [{"name": "key", "dtype": "string"}, {"name": "page_idx", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "element_counts", "struct": [{"name": "formulas", "dtype": "int64"}, {"name": "images", "dtype": "int64"}, {"name": "tables", "dtype": "int64"}]}, {"name": "token_length", "dtype": "int64"}]}], "splits": [{"name": "pdfa_train", "num_bytes": 38584453222, "num_examples": 16428833}, {"name": "pdfa_validation", "num_bytes": 4689687, "num_examples": 2000}], "download_size": 21111271721, "dataset_size": 38589142909}, "configs": [{"config_name": "default", "data_files": [{"split": "pdfa_train", "path": "data/pdfa_train-*"}, {"split": "pdfa_validation", "path": "data/pdfa_validation-*"}]}], "license": "other", "task_categories": ["text-to-image", "object-detection"], "language": ["en", "fr", "de", "es", "it", "ja", "ru", "pl", "nl", "zh", "pt", "bg", "tr", "ur", "hi", "th", "ar", "sw", "el", "vi"], "tags": ["ocr"], "size_categories": ["10M<n<100M"], "pretty_name": "LightOnOCR-mix"}
| false
|
False
| 2026-01-23T08:39:35
| 60
| 60
| false
|
09e11af7f0aacde1553b4d164049831e5bb7adb7
|
LightOnOCR-mix-0126
LightOnOCR-mix-0126 is a large-scale OCR training dataset built via distillation: a strong vision–language model is prompted to produce naturally ordered full-page transcriptions (Markdown with LaTeX math spans and HTML tables) from rendered document pages. The dataset is designed as supervision for end-to-end OCR / document-understanding models that aim to output clean, human-readable text in a consistent format.
This repository releases the PDFA-derived… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126.
| 831
| 831
|
['task_categories:text-to-image' 'task_categories:object-detection'
'language:en' 'language:fr' 'language:de' 'language:es' 'language:it'
'language:ja' 'language:ru' 'language:pl' 'language:nl' 'language:zh'
'language:pt' 'language:bg' 'language:tr' 'language:ur' 'language:hi'
'language:th' 'language:ar' 'language:sw' 'language:el' 'language:vi'
'license:other' 'size_categories:10M<n<100M' 'format:parquet'
'modality:text' 'library:datasets' 'library:dask' 'library:polars'
'library:mlcroissant' 'arxiv:2601.14251' 'region:eu' 'ocr']
| 2026-01-15T15:28:05
| null | null |
[
0.823688805103302,
0.8406579494476318,
0.483701229095459,
0.9319063425064087,
0.8324227333068848,
0.09852192550897598,
0.8004595637321472,
0.7389633655548096,
0.8095628619194031,
0.43992146849632263,
0.3524768352508545,
0.11228302121162415,
0.8136829137802124,
0.13404196500778198,
0.6520013809204102,
0.4549274146556854,
0.6745935082435608,
0.07285984605550766,
0.907474160194397,
0.5523958206176758,
0.4130737781524658,
0.4068172872066498,
0.42039644718170166,
0.32140108942985535,
0.3626518249511719,
0.004999982658773661,
0.0033254940062761307,
0.09783139079809189,
0.7156193852424622,
0.4133498966693878,
0.2378455400466919,
0.40639999508857727,
0.7418652176856995,
0.34318169951438904,
0.805732786655426,
0.6482906937599182,
0.8630126714706421,
0.5904615521430969,
0.7166235446929932,
0.32075050473213196,
0.34027373790740967,
0.9862895011901855,
0.8832876682281494,
0.8228916525840759,
0.49186787009239197,
0.4412108361721039,
0.49049606919288635,
0.1789439618587494,
0.870346188545227,
0.5977571606636047,
0.6753258109092712,
0.4561585485935211,
0.3725203573703766,
0.37690722942352295,
0.15968205034732819,
0.8953023552894592,
0.1355261504650116,
0.48150062561035156,
0.26929372549057007,
0.9593039751052856,
0.2883625626564026,
0.18785765767097473,
0.8331276178359985,
0.8605663776397705,
0.16825595498085022,
0.430813193321228,
0.5729755759239197,
0.3639633357524872,
0.6489160656929016,
0.5514046549797058,
0.48957663774490356,
0.13981805741786957,
0.055400505661964417,
0.4832855463027954,
0.008844999596476555,
0.19691041111946106,
0.44826215505599976,
0.8715271949768066,
0.8667916059494019,
0.6375746130943298,
0.08494444191455841,
0.694923460483551,
0.3993990421295166,
0.49056777358055115,
0.7712932825088501,
0.6054766774177551,
0.8552294373512268,
0.44704943895339966,
0.6708404421806335,
0.9750330448150635,
0.16942641139030457,
0.5955953598022461,
0.11860226094722748,
0.39933842420578003,
0.8893095254898071,
0.7233197093009949,
0.34624484181404114,
0.03173238784074783,
0.15449798107147217,
0.9505003690719604,
0.6307603716850281,
0.46090957522392273,
0.028900964185595512,
0.712866485118866,
0.013757153414189816,
0.35660433769226074,
0.21484093368053436,
0.010920769535005093,
0.6376861333847046,
0.16819259524345398,
0.8983694911003113,
0.4147373139858246,
0.3873284161090851,
0.8107087016105652,
0.2943233847618103,
0.017092542722821236,
0.9406861662864685,
0.5091233253479004,
0.03423217684030533,
0.5665362477302551,
0.8486164212226868,
0.9085410237312317,
0.5503994822502136,
0.1263435184955597,
0.11222930997610092,
0.1422436684370041,
0.3239775002002716,
0.18615883588790894
] |
69607cc44b1761f4d0cf0403
|
MiniMaxAI/OctoCodingBench
|
MiniMaxAI
|
{"license": "mit", "task_categories": ["text-generation"], "language": ["en"], "tags": ["code", "agent", "benchmark", "evaluation"], "pretty_name": "OctoCodingBench", "size_categories": ["n<1K"]}
| false
|
False
| 2026-01-13T13:02:26
| 245
| 55
| false
|
1555ecb6650a4448c1f7f714ce82d53f140b3414
|
OctoCodingBench: Instruction-Following Benchmark for Coding Agents
English | 中文
🌟 Overview
OctoCodingBench benchmarks scaffold-aware instruction following in repository-grounded agentic coding.
Why OctoCodingBench?
Existing benchmarks (SWE-bench, etc.) focus on task completion — whether the agent produces correct code. However, they miss a critical dimension: does the agent follow the rules while solving the task?
In real-world agentic coding, agents must… See the full description on the dataset page: https://huggingface.co/datasets/MiniMaxAI/OctoCodingBench.
| 13,077
| 13,077
|
['task_categories:text-generation' 'language:en' 'license:mit'
'size_categories:n<1K' 'format:json' 'modality:text' 'library:datasets'
'library:pandas' 'library:polars' 'library:mlcroissant' 'region:us'
'code' 'agent' 'benchmark' 'evaluation']
| 2026-01-09T03:57:56
| null | null |
[
0.8765692710876465,
0.5790888071060181,
0.9762074947357178,
0.9661572575569153,
0.7798381447792053,
0.6735142469406128,
0.9520696401596069,
0.005745115224272013,
0.19977733492851257,
0.021700406447052956,
0.8745269775390625,
0.7878456115722656,
0.034170836210250854,
0.3435129225254059,
0.9575406908988953,
0.10609365254640579,
0.9061168432235718,
0.2203071117401123,
0.09148164093494415,
0.2658495008945465,
0.9681544303894043,
0.9253994226455688,
0.3992159962654114,
0.9797226786613464,
0.29072386026382446,
0.9684861898422241,
0.6340019106864929,
0.4148653745651245,
0.9987253546714783,
0.2837393879890442,
0.30960655212402344,
0.8307152986526489,
0.779539942741394,
0.24946190416812897,
0.353996217250824,
0.2780683934688568,
0.08901757746934891,
0.2685171067714691,
0.10415367037057877,
0.7558113932609558,
0.29775288701057434,
0.05829770117998123,
0.1677137017250061,
0.3789159059524536,
0.7740751504898071,
0.7250357866287231,
0.9086095094680786,
0.6363843679428101,
0.8544477224349976,
0.6558117270469666,
0.8373754620552063,
0.04017204791307449,
0.8693161010742188,
0.6088449954986572,
0.05467758700251579,
0.10619615018367767,
0.49762842059135437,
0.20950481295585632,
0.4808513820171356,
0.15793251991271973,
0.5718107223510742,
0.1124119833111763,
0.218429297208786,
0.16722576320171356,
0.695179283618927,
0.6075374484062195,
0.5343464016914368,
0.5265368223190308,
0.4894680082798004,
0.49545934796333313,
0.19501784443855286,
0.11071962118148804,
0.9715700745582581,
0.6139893531799316,
0.8792323470115662,
0.6804656982421875,
0.4766288995742798,
0.919176459312439,
0.5679032206535339,
0.4371987283229828,
0.7628798484802246,
0.4623311460018158,
0.17526179552078247,
0.2051122933626175,
0.8575578331947327,
0.40439674258232117,
0.12517599761486053,
0.5211976766586304,
0.7101225256919861,
0.9020758867263794,
0.0986320823431015,
0.9713338017463684,
0.10858261585235596,
0.16479158401489258,
0.6908838748931885,
0.21238967776298523,
0.7846001386642456,
0.5779739022254944,
0.024606719613075256,
0.4161486327648163,
0.5805605053901672,
0.4222140610218048,
0.7849359512329102,
0.7568867206573486,
0.160536989569664,
0.9839672446250916,
0.13475079834461212,
0.45376649498939514,
0.6111904382705688,
0.8129822611808777,
0.21160642802715302,
0.9823273420333862,
0.40084409713745117,
0.16893848776817322,
0.42937716841697693,
0.6166742444038391,
0.09486287832260132,
0.4036106765270233,
0.9059104919433594,
0.20212222635746002,
0.4051481783390045,
0.2936197817325592,
0.33300381898880005,
0.43248263001441956,
0.49375563859939575,
0.48667508363723755,
0.9625611305236816,
0.9423445463180542
] |
68ba0ffd343a84103b603c45
|
Pageshift-Entertainment/LongPage
|
Pageshift-Entertainment
|
{"pretty_name": "LongPage", "dataset_name": "LongPage", "library_name": "datasets", "language": ["en"], "license": ["cc-by-4.0", "other"], "task_categories": ["text-generation"], "task_ids": ["language-modeling", "text2text-generation"], "size_categories": ["n<1K"], "source_datasets": ["original"], "annotations_creators": ["machine-generated"], "language_creators": ["found"], "multilinguality": ["monolingual"], "tags": ["long-context", "cot", "reasoning", "creative-writing", "Cold start reasoning data"], "pretty_visual": "assets/cover_image.png"}
| false
|
False
| 2026-01-20T14:01:26
| 102
| 51
| false
|
27d907b6a9f92682110e68ef91f001b4812698d6
|
Overview 🚀📚
The first comprehensive dataset for training AI models to write complete novels with sophisticated reasoning.
🧠 Hierarchical Reasoning Architecture — Multi-layered planning traces including character archetypes, story arcs, world rules, and scene breakdowns. A complete cognitive roadmap for long-form narrative construction.
📖 Complete Novel Coverage — From 40,000 to 600,000+ tokens per book, spanning novellas to epic series with consistent quality throughout.
⚡… See the full description on the dataset page: https://huggingface.co/datasets/Pageshift-Entertainment/LongPage.
| 1,999
| 13,284
|
['task_categories:text-generation' 'task_ids:language-modeling'
'task_ids:text2text-generation' 'annotations_creators:machine-generated'
'language_creators:found' 'multilinguality:monolingual'
'source_datasets:original' 'language:en' 'license:cc-by-4.0'
'license:other' 'size_categories:1K<n<10K' 'format:parquet'
'format:optimized-parquet' 'modality:text' 'library:datasets'
'library:dask' 'library:polars' 'library:mlcroissant' 'region:us'
'long-context' 'cot' 'reasoning' 'creative-writing'
'Cold start reasoning data']
| 2025-09-04T22:17:33
| null | null |
[
0.25633519887924194,
0.18907758593559265,
0.17922718822956085,
0.10687944293022156,
0.6752791404724121,
0.10808505117893219,
0.3827035129070282,
0.5174180865287781,
0.44070863723754883,
0.6763702034950256,
0.6158460974693298,
0.3872328996658325,
0.1837841272354126,
0.4385623037815094,
0.16121995449066162,
0.3107205927371979,
0.5183227062225342,
0.404792845249176,
0.815932035446167,
0.4370344579219818,
0.9809409379959106,
0.9346485137939453,
0.07835348695516586,
0.5617200136184692,
0.01664668135344982,
0.958232045173645,
0.1070503368973732,
0.9643290638923645,
0.5536256432533264,
0.5825800895690918,
0.9533225893974304,
0.4434639811515808,
0.3655757009983063,
0.7525675892829895,
0.39530789852142334,
0.9066399335861206,
0.10100024938583374,
0.5638786554336548,
0.12053247541189194,
0.6693472266197205,
0.718874454498291,
0.7034098505973816,
0.976965606212616,
0.5536563992500305,
0.11055741459131241,
0.7877000570297241,
0.2704209089279175,
0.9602229595184326,
0.4628131687641144,
0.9377957582473755,
0.735794723033905,
0.3476434350013733,
0.426873117685318,
0.4312721788883209,
0.5750505328178406,
0.3651026785373688,
0.18418458104133606,
0.9537709355354309,
0.261735737323761,
0.6412503719329834,
0.9975358247756958,
0.8298342823982239,
0.5865424275398254,
0.7656940221786499,
0.12622077763080597,
0.39620816707611084,
0.8906005024909973,
0.5362040996551514,
0.4356565475463867,
0.9630107879638672,
0.748489260673523,
0.18443167209625244,
0.09443702548742294,
0.5224248170852661,
0.10492977499961853,
0.8543305397033691,
0.13923101127147675,
0.002021853346377611,
0.09364965558052063,
0.565045952796936,
0.2728542983531952,
0.07286316156387329,
0.066599540412426,
0.41087406873703003,
0.522296130657196,
0.14317776262760162,
0.12544208765029907,
0.05509013682603836,
0.9018716216087341,
0.04551925137639046,
0.12142443656921387,
0.25280213356018066,
0.525075376033783,
0.2852269113063812,
0.526053786277771,
0.3953067660331726,
0.4572705626487732,
0.6722877025604248,
0.05184311047196388,
0.3139795660972595,
0.1344166100025177,
0.4149865210056305,
0.25878310203552246,
0.32354721426963806,
0.7670634984970093,
0.9127326011657715,
0.7570037245750427,
0.44780513644218445,
0.8232934474945068,
0.4652262032032013,
0.28437501192092896,
0.259714812040329,
0.864814817905426,
0.3395657539367676,
0.40698790550231934,
0.1606135368347168,
0.9309388399124146,
0.9649301171302795,
0.28297728300094604,
0.9923254251480103,
0.9276708960533142,
0.13814763724803925,
0.0159305389970541,
0.9647988080978394,
0.10775063186883926,
0.6191223859786987,
0.6966481804847717,
0.440192312002182
] |
695fb1b373628fa861fe84cf
|
HuggingFaceFW/finetranslations
|
HuggingFaceFW
|
{"license": "odc-by", "task_categories": ["text-generation", "translation"], "pretty_name": "FineTranslations", "size_categories": ["n>1T"], "language": ["abk", "abq", "abs", "acm", "adh", "adi", "ady", "aeb", "afr", "agx", "aii", "aim", "ain", "ajz", "akb", "aln", "als", "alt", "amh", "anp", "aoz", "apc", "apt", "arb", "arg", "arq", "ars", "ary", "arz", "asm", "ast", "atb", "ava", "awa", "ayp", "ayr", "azb", "azj", "bak", "bam", "ban", "bar", "bas", "bbc", "bbk", "bcl", "bdq", "bel", "ben", "bew", "bho", "bhp", "bis", "biu", "bjn", "bod", "bos", "brh", "brx", "bts", "btx", "bug", "bul", "bwi", "bxr", "cat", "cbk", "ccp", "ceb", "ces", "cfm", "cha", "che", "chr", "chu", "chv", "cjs", "ckb", "ckt", "cmn", "cnh", "cnw", "cos", "crh", "crj", "crk", "crl", "crs", "csb", "csw", "csy", "ctd", "cym", "czt", "dak", "dan", "dar", "deu", "dik", "diu", "div", "dje", "dks", "dln", "dng", "dnw", "doi", "dru", "dsb", "dtp", "dty", "dzo", "ekk", "ell", "enl", "enm", "epo", "ess", "eus", "eve", "ewo", "ext", "fao", "fas", "ffm", "fij", "fil", "fin", "fit", "fkv", "fmu", "fra", "fro", "frp", "fry", "fuf", "fur", "fuv", "gag", "gaz", "gcf", "gla", "gle", "glg", "glk", "glv", "gmh", "gnb", "goh", "gom", "gos", "grc", "gsw", "gug", "guj", "guz", "hac", "hae", "hak", "hat", "hau", "haw", "hbo", "heb", "her", "hif", "hil", "hin", "hmr", "hne", "hns", "hrv", "hrx", "hsb", "hun", "hwc", "hye", "hyw", "iba", "ibg", "ibo", "ife", "ike", "ikt", "ilo", "ina", "ind", "inh", "isl", "ita", "ivv", "jav", "jpn", "jun", "kaa", "kab", "kac", "kak", "kal", "kam", "kan", "kas", "kat", "kaz", "kbd", "kca", "kdh", "kdr", "kea", "kei", "kgp", "kha", "khk", "khm", "kik", "kin", "kir", "kiu", "kjb", "kjh", "kmr", "knc", "koi", "kor", "kos", "kpv", "krj", "krl", "kru", "ksh", "ksw", "ktj", "ktz", "kua", "kum", "kwn", "kyu", "kzj", "lad", "lao", "lat", "lbe", "ldn", "lew", "lez", "lfn", "lim", "lin", "lis", "lit", "lki", "lld", "lmk", "lnd", "lrc", "ltg", "ltz", "lud", "lug", "luo", "lus", "lvs", "lwg", "lzh", "mag", "mah", "mai", "mak", "mal", "mar", "mas", "mbf", "mdf", "mer", "mfe", "mfg", "mfy", "mhi", "mhr", "mhy", "min", "mip", "mjw", "mkd", "mlt", "mni", "mnk", "mns", "mnw", "moh", "mph", "mqy", "mri", "mrj", "mrw", "mtg", "mui", "mup", "mus", "mvp", "mwf", "mwl", "mww", "mya", "myv", "myx", "mzh", "nah", "nan", "nap", "naq", "nbu", "nde", "ndo", "nds", "new", "nio", "njn", "njo", "nld", "nmf", "nmz", "nno", "nob", "nog", "non", "npi", "npo", "nrf", "nri", "nrm", "nse", "nus", "nya", "nyn", "nzm", "obo", "oci", "ojb", "olo", "orv", "ory", "oss", "ota", "oto", "otw", "pam", "pan", "pap", "pbt", "pcd", "pck", "pcm", "pfl", "plt", "pmq", "pmx", "pnb", "pnt", "pol", "por", "pov", "ppk", "pps", "prg", "pui", "pxm", "quc", "qul", "qup", "qus", "quz", "raw", "rcf", "rel", "rhg", "ria", "rjs", "rmc", "rml", "rmn", "rmy", "rnl", "roh", "ron", "rtm", "rue", "run", "rus", "sah", "san", "sat", "sck", "scn", "sda", "sdc", "sdh", "ses", "sgc", "sgh", "sid", "sin", "sju", "skr", "slk", "slv", "sma", "sme", "smj", "smn", "smo", "sms", "smt", "sna", "snd", "som", "sot", "spa", "srd", "srp", "ssw", "sun", "swe", "swg", "swh", "syc", "syl", "szl", "tab", "tam", "taq", "tat", "tcy", "tcz", "tel", "tet", "tgk", "tha", "thl", "tig", "tir", "tkl", "tkr", "tlh", "tly", "tok", "ton", "tpi", "tpw", "trc", "trp", "trs", "ttj", "tuk", "tur", "tuv", "twx", "tyv", "tzl", "tzm", "udm", "uig", "ukr", "urd", "uzn", "uzs", "vap", "vie", "vot", "vro", "war", "way", "wba", "wbm", "wes", "whk", "wlx", "wol", "wsg", "wwa", "xal", "xho", "xmm", "xmv", "xog", "yaz", "ydd", "yor", "yrk", "yrl", "yua", "yue", "zea", "zgh", "zom", "zsm", "zul"], "configs": [{"config_name": "all", "data_files": "data/*/*"}, {"config_name": "abk_Cyrl", "data_files": "data/abk_Cyrl/*"}, {"config_name": "abq_Cyrl", "data_files": "data/abq_Cyrl/*"}, {"config_name": "abs_Latn", "data_files": "data/abs_Latn/*"}, {"config_name": "acm_Arab", "data_files": "data/acm_Arab/*"}, {"config_name": "adh_Latn", "data_files": "data/adh_Latn/*"}, {"config_name": "adi_Latn", "data_files": "data/adi_Latn/*"}, {"config_name": "ady_Cyrl", "data_files": "data/ady_Cyrl/*"}, {"config_name": "aeb_Arab", "data_files": "data/aeb_Arab/*"}, {"config_name": "afr_Latn", "data_files": "data/afr_Latn/*"}, {"config_name": "agx_Cyrl", "data_files": "data/agx_Cyrl/*"}, {"config_name": "aii_Syrc", "data_files": "data/aii_Syrc/*"}, {"config_name": "aim_Latn", "data_files": "data/aim_Latn/*"}, {"config_name": "ain_Latn", "data_files": "data/ain_Latn/*"}, {"config_name": "ajz_Latn", "data_files": "data/ajz_Latn/*"}, {"config_name": "akb_Latn", "data_files": "data/akb_Latn/*"}, {"config_name": "aln_Latn", "data_files": "data/aln_Latn/*"}, {"config_name": "als_Latn", "data_files": "data/als_Latn/*"}, {"config_name": "alt_Cyrl", "data_files": "data/alt_Cyrl/*"}, {"config_name": "amh_Ethi", "data_files": "data/amh_Ethi/*"}, {"config_name": "anp_Deva", "data_files": "data/anp_Deva/*"}, {"config_name": "aoz_Latn", "data_files": "data/aoz_Latn/*"}, {"config_name": "apc_Arab", "data_files": "data/apc_Arab/*"}, {"config_name": "apt_Latn", "data_files": "data/apt_Latn/*"}, {"config_name": "arb_Arab", "data_files": "data/arb_Arab/*"}, {"config_name": "arb_Latn", "data_files": "data/arb_Latn/*"}, {"config_name": "arg_Latn", "data_files": "data/arg_Latn/*"}, {"config_name": "arq_Arab", "data_files": "data/arq_Arab/*"}, {"config_name": "ars_Arab", "data_files": "data/ars_Arab/*"}, {"config_name": "ary_Arab", "data_files": "data/ary_Arab/*"}, {"config_name": "arz_Arab", "data_files": "data/arz_Arab/*"}, {"config_name": "asm_Beng", "data_files": "data/asm_Beng/*"}, {"config_name": "asm_Latn", "data_files": "data/asm_Latn/*"}, {"config_name": "ast_Latn", "data_files": "data/ast_Latn/*"}, {"config_name": "atb_Latn", "data_files": "data/atb_Latn/*"}, {"config_name": "ava_Cyrl", "data_files": "data/ava_Cyrl/*"}, {"config_name": "awa_Deva", "data_files": "data/awa_Deva/*"}, {"config_name": "ayp_Arab", "data_files": "data/ayp_Arab/*"}, {"config_name": "ayr_Latn", "data_files": "data/ayr_Latn/*"}, {"config_name": "azb_Arab", "data_files": "data/azb_Arab/*"}, {"config_name": "azj_Latn", "data_files": "data/azj_Latn/*"}, {"config_name": "bak_Cyrl", "data_files": "data/bak_Cyrl/*"}, {"config_name": "bam_Latn", "data_files": "data/bam_Latn/*"}, {"config_name": "ban_Latn", "data_files": "data/ban_Latn/*"}, {"config_name": "bar_Latn", "data_files": "data/bar_Latn/*"}, {"config_name": "bas_Latn", "data_files": "data/bas_Latn/*"}, {"config_name": "bbc_Latn", "data_files": "data/bbc_Latn/*"}, {"config_name": "bbk_Latn", "data_files": "data/bbk_Latn/*"}, {"config_name": "bcl_Latn", "data_files": "data/bcl_Latn/*"}, {"config_name": "bdq_Latn", "data_files": "data/bdq_Latn/*"}, {"config_name": "bel_Cyrl", "data_files": "data/bel_Cyrl/*"}, {"config_name": "ben_Beng", "data_files": "data/ben_Beng/*"}, {"config_name": "ben_Latn", "data_files": "data/ben_Latn/*"}, {"config_name": "bew_Latn", "data_files": "data/bew_Latn/*"}, {"config_name": "bho_Deva", "data_files": "data/bho_Deva/*"}, {"config_name": "bhp_Latn", "data_files": "data/bhp_Latn/*"}, {"config_name": "bis_Latn", "data_files": "data/bis_Latn/*"}, {"config_name": "biu_Latn", "data_files": "data/biu_Latn/*"}, {"config_name": "bjn_Arab", "data_files": "data/bjn_Arab/*"}, {"config_name": "bjn_Latn", "data_files": "data/bjn_Latn/*"}, {"config_name": "bod_Tibt", "data_files": "data/bod_Tibt/*"}, {"config_name": "bos_Latn", "data_files": "data/bos_Latn/*"}, {"config_name": "brh_Arab", "data_files": "data/brh_Arab/*"}, {"config_name": "brx_Deva", "data_files": "data/brx_Deva/*"}, {"config_name": "bts_Latn", "data_files": "data/bts_Latn/*"}, {"config_name": "btx_Latn", "data_files": "data/btx_Latn/*"}, {"config_name": "bug_Latn", "data_files": "data/bug_Latn/*"}, {"config_name": "bul_Cyrl", "data_files": "data/bul_Cyrl/*"}, {"config_name": "bwi_Latn", "data_files": "data/bwi_Latn/*"}, {"config_name": "bxr_Cyrl", "data_files": "data/bxr_Cyrl/*"}, {"config_name": "cat_Latn", "data_files": "data/cat_Latn/*"}, {"config_name": "cbk_Latn", "data_files": "data/cbk_Latn/*"}, {"config_name": "ccp_Latn", "data_files": "data/ccp_Latn/*"}, {"config_name": "ceb_Latn", "data_files": "data/ceb_Latn/*"}, {"config_name": "ces_Latn", "data_files": "data/ces_Latn/*"}, {"config_name": "cfm_Latn", "data_files": "data/cfm_Latn/*"}, {"config_name": "cha_Latn", "data_files": "data/cha_Latn/*"}, {"config_name": "che_Cyrl", "data_files": "data/che_Cyrl/*"}, {"config_name": "chr_Latn", "data_files": "data/chr_Latn/*"}, {"config_name": "chu_Cyrl", "data_files": "data/chu_Cyrl/*"}, {"config_name": "chv_Cyrl", "data_files": "data/chv_Cyrl/*"}, {"config_name": "cjs_Cyrl", "data_files": "data/cjs_Cyrl/*"}, {"config_name": "ckb_Arab", "data_files": "data/ckb_Arab/*"}, {"config_name": "ckt_Cyrl", "data_files": "data/ckt_Cyrl/*"}, {"config_name": "cmn_Hani", "data_files": "data/cmn_Hani/*"}, {"config_name": "cnh_Latn", "data_files": "data/cnh_Latn/*"}, {"config_name": "cnw_Latn", "data_files": "data/cnw_Latn/*"}, {"config_name": "cos_Latn", "data_files": "data/cos_Latn/*"}, {"config_name": "crh_Cyrl", "data_files": "data/crh_Cyrl/*"}, {"config_name": "crh_Latn", "data_files": "data/crh_Latn/*"}, {"config_name": "crj_Cans", "data_files": "data/crj_Cans/*"}, {"config_name": "crk_Cans", "data_files": "data/crk_Cans/*"}, {"config_name": "crk_Latn", "data_files": "data/crk_Latn/*"}, {"config_name": "crl_Cans", "data_files": "data/crl_Cans/*"}, {"config_name": "crs_Latn", "data_files": "data/crs_Latn/*"}, {"config_name": "csb_Latn", "data_files": "data/csb_Latn/*"}, {"config_name": "csw_Latn", "data_files": "data/csw_Latn/*"}, {"config_name": "csy_Latn", "data_files": "data/csy_Latn/*"}, {"config_name": "ctd_Latn", "data_files": "data/ctd_Latn/*"}, {"config_name": "cym_Latn", "data_files": "data/cym_Latn/*"}, {"config_name": "czt_Latn", "data_files": "data/czt_Latn/*"}, {"config_name": "dak_Latn", "data_files": "data/dak_Latn/*"}, {"config_name": "dan_Latn", "data_files": "data/dan_Latn/*"}, {"config_name": "dar_Cyrl", "data_files": "data/dar_Cyrl/*"}, {"config_name": "deu_Latn", "data_files": "data/deu_Latn/*"}, {"config_name": "dik_Latn", "data_files": "data/dik_Latn/*"}, {"config_name": "diu_Latn", "data_files": "data/diu_Latn/*"}, {"config_name": "div_Thaa", "data_files": "data/div_Thaa/*"}, {"config_name": "dje_Latn", "data_files": "data/dje_Latn/*"}, {"config_name": "dks_Latn", "data_files": "data/dks_Latn/*"}, {"config_name": "dln_Latn", "data_files": "data/dln_Latn/*"}, {"config_name": "dng_Cyrl", "data_files": "data/dng_Cyrl/*"}, {"config_name": "dnw_Latn", "data_files": "data/dnw_Latn/*"}, {"config_name": "doi_Deva", "data_files": "data/doi_Deva/*"}, {"config_name": "dru_Latn", "data_files": "data/dru_Latn/*"}, {"config_name": "dsb_Latn", "data_files": "data/dsb_Latn/*"}, {"config_name": "dtp_Latn", "data_files": "data/dtp_Latn/*"}, {"config_name": "dty_Deva", "data_files": "data/dty_Deva/*"}, {"config_name": "dzo_Tibt", "data_files": "data/dzo_Tibt/*"}, {"config_name": "ekk_Latn", "data_files": "data/ekk_Latn/*"}, {"config_name": "ell_Grek", "data_files": "data/ell_Grek/*"}, {"config_name": "enl_Latn", "data_files": "data/enl_Latn/*"}, {"config_name": "enm_Latn", "data_files": "data/enm_Latn/*"}, {"config_name": "epo_Latn", "data_files": "data/epo_Latn/*"}, {"config_name": "ess_Latn", "data_files": "data/ess_Latn/*"}, {"config_name": "eus_Latn", "data_files": "data/eus_Latn/*"}, {"config_name": "eve_Cyrl", "data_files": "data/eve_Cyrl/*"}, {"config_name": "ewo_Latn", "data_files": "data/ewo_Latn/*"}, {"config_name": "ext_Latn", "data_files": "data/ext_Latn/*"}, {"config_name": "fao_Latn", "data_files": "data/fao_Latn/*"}, {"config_name": "fas_Arab", "data_files": "data/fas_Arab/*"}, {"config_name": "ffm_Latn", "data_files": "data/ffm_Latn/*"}, {"config_name": "fij_Latn", "data_files": "data/fij_Latn/*"}, {"config_name": "fil_Latn", "data_files": "data/fil_Latn/*"}, {"config_name": "fin_Latn", "data_files": "data/fin_Latn/*"}, {"config_name": "fit_Latn", "data_files": "data/fit_Latn/*"}, {"config_name": "fkv_Latn", "data_files": "data/fkv_Latn/*"}, {"config_name": "fmu_Deva", "data_files": "data/fmu_Deva/*"}, {"config_name": "fra_Latn", "data_files": "data/fra_Latn/*"}, {"config_name": "fro_Latn", "data_files": "data/fro_Latn/*"}, {"config_name": "frp_Latn", "data_files": "data/frp_Latn/*"}, {"config_name": "fry_Latn", "data_files": "data/fry_Latn/*"}, {"config_name": "fuf_Latn", "data_files": "data/fuf_Latn/*"}, {"config_name": "fur_Latn", "data_files": "data/fur_Latn/*"}, {"config_name": "fuv_Latn", "data_files": "data/fuv_Latn/*"}, {"config_name": "gag_Latn", "data_files": "data/gag_Latn/*"}, {"config_name": "gaz_Latn", "data_files": "data/gaz_Latn/*"}, {"config_name": "gcf_Latn", "data_files": "data/gcf_Latn/*"}, {"config_name": "gla_Latn", "data_files": "data/gla_Latn/*"}, {"config_name": "gle_Latn", "data_files": "data/gle_Latn/*"}, {"config_name": "glg_Latn", "data_files": "data/glg_Latn/*"}, {"config_name": "glk_Arab", "data_files": "data/glk_Arab/*"}, {"config_name": "glv_Latn", "data_files": "data/glv_Latn/*"}, {"config_name": "gmh_Latn", "data_files": "data/gmh_Latn/*"}, {"config_name": "gnb_Latn", "data_files": "data/gnb_Latn/*"}, {"config_name": "goh_Latn", "data_files": "data/goh_Latn/*"}, {"config_name": "gom_Deva", "data_files": "data/gom_Deva/*"}, {"config_name": "gom_Latn", "data_files": "data/gom_Latn/*"}, {"config_name": "gos_Latn", "data_files": "data/gos_Latn/*"}, {"config_name": "grc_Grek", "data_files": "data/grc_Grek/*"}, {"config_name": "gsw_Latn", "data_files": "data/gsw_Latn/*"}, {"config_name": "gug_Latn", "data_files": "data/gug_Latn/*"}, {"config_name": "guj_Gujr", "data_files": "data/guj_Gujr/*"}, {"config_name": "guj_Latn", "data_files": "data/guj_Latn/*"}, {"config_name": "guz_Latn", "data_files": "data/guz_Latn/*"}, {"config_name": "hac_Arab", "data_files": "data/hac_Arab/*"}, {"config_name": "hae_Latn", "data_files": "data/hae_Latn/*"}, {"config_name": "hak_Hani", "data_files": "data/hak_Hani/*"}, {"config_name": "hat_Latn", "data_files": "data/hat_Latn/*"}, {"config_name": "hau_Latn", "data_files": "data/hau_Latn/*"}, {"config_name": "haw_Latn", "data_files": "data/haw_Latn/*"}, {"config_name": "hbo_Hebr", "data_files": "data/hbo_Hebr/*"}, {"config_name": "heb_Hebr", "data_files": "data/heb_Hebr/*"}, {"config_name": "her_Latn", "data_files": "data/her_Latn/*"}, {"config_name": "hif_Latn", "data_files": "data/hif_Latn/*"}, {"config_name": "hil_Latn", "data_files": "data/hil_Latn/*"}, {"config_name": "hin_Deva", "data_files": "data/hin_Deva/*"}, {"config_name": "hin_Latn", "data_files": "data/hin_Latn/*"}, {"config_name": "hmr_Latn", "data_files": "data/hmr_Latn/*"}, {"config_name": "hne_Deva", "data_files": "data/hne_Deva/*"}, {"config_name": "hns_Latn", "data_files": "data/hns_Latn/*"}, {"config_name": "hrv_Latn", "data_files": "data/hrv_Latn/*"}, {"config_name": "hrx_Latn", "data_files": "data/hrx_Latn/*"}, {"config_name": "hsb_Latn", "data_files": "data/hsb_Latn/*"}, {"config_name": "hun_Latn", "data_files": "data/hun_Latn/*"}, {"config_name": "hwc_Latn", "data_files": "data/hwc_Latn/*"}, {"config_name": "hye_Armn", "data_files": "data/hye_Armn/*"}, {"config_name": "hyw_Armn", "data_files": "data/hyw_Armn/*"}, {"config_name": "iba_Latn", "data_files": "data/iba_Latn/*"}, {"config_name": "ibg_Latn", "data_files": "data/ibg_Latn/*"}, {"config_name": "ibo_Latn", "data_files": "data/ibo_Latn/*"}, {"config_name": "ife_Latn", "data_files": "data/ife_Latn/*"}, {"config_name": "ike_Cans", "data_files": "data/ike_Cans/*"}, {"config_name": "ikt_Latn", "data_files": "data/ikt_Latn/*"}, {"config_name": "ilo_Latn", "data_files": "data/ilo_Latn/*"}, {"config_name": "ina_Latn", "data_files": "data/ina_Latn/*"}, {"config_name": "ind_Latn", "data_files": "data/ind_Latn/*"}, {"config_name": "inh_Cyrl", "data_files": "data/inh_Cyrl/*"}, {"config_name": "isl_Latn", "data_files": "data/isl_Latn/*"}, {"config_name": "ita_Latn", "data_files": "data/ita_Latn/*"}, {"config_name": "ivv_Latn", "data_files": "data/ivv_Latn/*"}, {"config_name": "jav_Latn", "data_files": "data/jav_Latn/*"}, {"config_name": "jpn_Jpan", "data_files": "data/jpn_Jpan/*"}, {"config_name": "jun_Orya", "data_files": "data/jun_Orya/*"}, {"config_name": "kaa_Cyrl", "data_files": "data/kaa_Cyrl/*"}, {"config_name": "kaa_Latn", "data_files": "data/kaa_Latn/*"}, {"config_name": "kab_Latn", "data_files": "data/kab_Latn/*"}, {"config_name": "kac_Latn", "data_files": "data/kac_Latn/*"}, {"config_name": "kak_Latn", "data_files": "data/kak_Latn/*"}, {"config_name": "kal_Latn", "data_files": "data/kal_Latn/*"}, {"config_name": "kam_Latn", "data_files": "data/kam_Latn/*"}, {"config_name": "kan_Knda", "data_files": "data/kan_Knda/*"}, {"config_name": "kan_Latn", "data_files": "data/kan_Latn/*"}, {"config_name": "kas_Deva", "data_files": "data/kas_Deva/*"}, {"config_name": "kas_Latn", "data_files": "data/kas_Latn/*"}, {"config_name": "kat_Geor", "data_files": "data/kat_Geor/*"}, {"config_name": "kaz_Cyrl", "data_files": "data/kaz_Cyrl/*"}, {"config_name": "kbd_Cyrl", "data_files": "data/kbd_Cyrl/*"}, {"config_name": "kca_Cyrl", "data_files": "data/kca_Cyrl/*"}, {"config_name": "kdh_Latn", "data_files": "data/kdh_Latn/*"}, {"config_name": "kdr_Latn", "data_files": "data/kdr_Latn/*"}, {"config_name": "kea_Latn", "data_files": "data/kea_Latn/*"}, {"config_name": "kei_Latn", "data_files": "data/kei_Latn/*"}, {"config_name": "kgp_Latn", "data_files": "data/kgp_Latn/*"}, {"config_name": "kha_Latn", "data_files": "data/kha_Latn/*"}, {"config_name": "khk_Cyrl", "data_files": "data/khk_Cyrl/*"}, {"config_name": "khm_Khmr", "data_files": "data/khm_Khmr/*"}, {"config_name": "kik_Latn", "data_files": "data/kik_Latn/*"}, {"config_name": "kin_Latn", "data_files": "data/kin_Latn/*"}, {"config_name": "kir_Cyrl", "data_files": "data/kir_Cyrl/*"}, {"config_name": "kiu_Latn", "data_files": "data/kiu_Latn/*"}, {"config_name": "kjb_Latn", "data_files": "data/kjb_Latn/*"}, {"config_name": "kjh_Cyrl", "data_files": "data/kjh_Cyrl/*"}, {"config_name": "kmr_Cyrl", "data_files": "data/kmr_Cyrl/*"}, {"config_name": "kmr_Latn", "data_files": "data/kmr_Latn/*"}, {"config_name": "knc_Latn", "data_files": "data/knc_Latn/*"}, {"config_name": "koi_Cyrl", "data_files": "data/koi_Cyrl/*"}, {"config_name": "kor_Hang", "data_files": "data/kor_Hang/*"}, {"config_name": "kos_Latn", "data_files": "data/kos_Latn/*"}, {"config_name": "kpv_Cyrl", "data_files": "data/kpv_Cyrl/*"}, {"config_name": "krj_Latn", "data_files": "data/krj_Latn/*"}, {"config_name": "krl_Latn", "data_files": "data/krl_Latn/*"}, {"config_name": "kru_Deva", "data_files": "data/kru_Deva/*"}, {"config_name": "ksh_Latn", "data_files": "data/ksh_Latn/*"}, {"config_name": "ksw_Mymr", "data_files": "data/ksw_Mymr/*"}, {"config_name": "ktj_Latn", "data_files": "data/ktj_Latn/*"}, {"config_name": "ktz_Latn", "data_files": "data/ktz_Latn/*"}, {"config_name": "kua_Latn", "data_files": "data/kua_Latn/*"}, {"config_name": "kum_Cyrl", "data_files": "data/kum_Cyrl/*"}, {"config_name": "kwn_Latn", "data_files": "data/kwn_Latn/*"}, {"config_name": "kyu_Kali", "data_files": "data/kyu_Kali/*"}, {"config_name": "kzj_Latn", "data_files": "data/kzj_Latn/*"}, {"config_name": "lad_Latn", "data_files": "data/lad_Latn/*"}, {"config_name": "lao_Laoo", "data_files": "data/lao_Laoo/*"}, {"config_name": "lat_Latn", "data_files": "data/lat_Latn/*"}, {"config_name": "lbe_Cyrl", "data_files": "data/lbe_Cyrl/*"}, {"config_name": "ldn_Latn", "data_files": "data/ldn_Latn/*"}, {"config_name": "lew_Latn", "data_files": "data/lew_Latn/*"}, {"config_name": "lez_Cyrl", "data_files": "data/lez_Cyrl/*"}, {"config_name": "lfn_Cyrl", "data_files": "data/lfn_Cyrl/*"}, {"config_name": "lim_Latn", "data_files": "data/lim_Latn/*"}, {"config_name": "lin_Latn", "data_files": "data/lin_Latn/*"}, {"config_name": "lis_Lisu", "data_files": "data/lis_Lisu/*"}, {"config_name": "lit_Latn", "data_files": "data/lit_Latn/*"}, {"config_name": "lki_Arab", "data_files": "data/lki_Arab/*"}, {"config_name": "lld_Latn", "data_files": "data/lld_Latn/*"}, {"config_name": "lmk_Latn", "data_files": "data/lmk_Latn/*"}, {"config_name": "lnd_Latn", "data_files": "data/lnd_Latn/*"}, {"config_name": "lrc_Arab", "data_files": "data/lrc_Arab/*"}, {"config_name": "ltg_Latn", "data_files": "data/ltg_Latn/*"}, {"config_name": "ltz_Latn", "data_files": "data/ltz_Latn/*"}, {"config_name": "lud_Latn", "data_files": "data/lud_Latn/*"}, {"config_name": "lug_Latn", "data_files": "data/lug_Latn/*"}, {"config_name": "luo_Latn", "data_files": "data/luo_Latn/*"}, {"config_name": "lus_Latn", "data_files": "data/lus_Latn/*"}, {"config_name": "lvs_Latn", "data_files": "data/lvs_Latn/*"}, {"config_name": "lwg_Latn", "data_files": "data/lwg_Latn/*"}, {"config_name": "lzh_Hani", "data_files": "data/lzh_Hani/*"}, {"config_name": "mag_Deva", "data_files": "data/mag_Deva/*"}, {"config_name": "mah_Latn", "data_files": "data/mah_Latn/*"}, {"config_name": "mai_Deva", "data_files": "data/mai_Deva/*"}, {"config_name": "mak_Latn", "data_files": "data/mak_Latn/*"}, {"config_name": "mal_Latn", "data_files": "data/mal_Latn/*"}, {"config_name": "mal_Mlym", "data_files": "data/mal_Mlym/*"}, {"config_name": "mar_Deva", "data_files": "data/mar_Deva/*"}, {"config_name": "mar_Latn", "data_files": "data/mar_Latn/*"}, {"config_name": "mas_Latn", "data_files": "data/mas_Latn/*"}, {"config_name": "mbf_Latn", "data_files": "data/mbf_Latn/*"}, {"config_name": "mdf_Cyrl", "data_files": "data/mdf_Cyrl/*"}, {"config_name": "mer_Latn", "data_files": "data/mer_Latn/*"}, {"config_name": "mfe_Latn", "data_files": "data/mfe_Latn/*"}, {"config_name": "mfg_Latn", "data_files": "data/mfg_Latn/*"}, {"config_name": "mfy_Latn", "data_files": "data/mfy_Latn/*"}, {"config_name": "mhi_Latn", "data_files": "data/mhi_Latn/*"}, {"config_name": "mhr_Cyrl", "data_files": "data/mhr_Cyrl/*"}, {"config_name": "mhy_Latn", "data_files": "data/mhy_Latn/*"}, {"config_name": "min_Latn", "data_files": "data/min_Latn/*"}, {"config_name": "mip_Latn", "data_files": "data/mip_Latn/*"}, {"config_name": "mjw_Latn", "data_files": "data/mjw_Latn/*"}, {"config_name": "mkd_Cyrl", "data_files": "data/mkd_Cyrl/*"}, {"config_name": "mlt_Latn", "data_files": "data/mlt_Latn/*"}, {"config_name": "mni_Beng", "data_files": "data/mni_Beng/*"}, {"config_name": "mni_Latn", "data_files": "data/mni_Latn/*"}, {"config_name": "mnk_Latn", "data_files": "data/mnk_Latn/*"}, {"config_name": "mns_Cyrl", "data_files": "data/mns_Cyrl/*"}, {"config_name": "mnw_Mymr", "data_files": "data/mnw_Mymr/*"}, {"config_name": "moh_Latn", "data_files": "data/moh_Latn/*"}, {"config_name": "mph_Latn", "data_files": "data/mph_Latn/*"}, {"config_name": "mqy_Latn", "data_files": "data/mqy_Latn/*"}, {"config_name": "mri_Latn", "data_files": "data/mri_Latn/*"}, {"config_name": "mrj_Cyrl", "data_files": "data/mrj_Cyrl/*"}, {"config_name": "mrw_Latn", "data_files": "data/mrw_Latn/*"}, {"config_name": "mtg_Latn", "data_files": "data/mtg_Latn/*"}, {"config_name": "mui_Latn", "data_files": "data/mui_Latn/*"}, {"config_name": "mup_Deva", "data_files": "data/mup_Deva/*"}, {"config_name": "mus_Latn", "data_files": "data/mus_Latn/*"}, {"config_name": "mvp_Latn", "data_files": "data/mvp_Latn/*"}, {"config_name": "mwf_Latn", "data_files": "data/mwf_Latn/*"}, {"config_name": "mwl_Latn", "data_files": "data/mwl_Latn/*"}, {"config_name": "mww_Latn", "data_files": "data/mww_Latn/*"}, {"config_name": "mya_Mymr", "data_files": "data/mya_Mymr/*"}, {"config_name": "myv_Cyrl", "data_files": "data/myv_Cyrl/*"}, {"config_name": "myx_Latn", "data_files": "data/myx_Latn/*"}, {"config_name": "mzh_Latn", "data_files": "data/mzh_Latn/*"}, {"config_name": "nah_Latn", "data_files": "data/nah_Latn/*"}, {"config_name": "nan_Latn", "data_files": "data/nan_Latn/*"}, {"config_name": "nap_Latn", "data_files": "data/nap_Latn/*"}, {"config_name": "naq_Latn", "data_files": "data/naq_Latn/*"}, {"config_name": "nbu_Latn", "data_files": "data/nbu_Latn/*"}, {"config_name": "nde_Latn", "data_files": "data/nde_Latn/*"}, {"config_name": "ndo_Latn", "data_files": "data/ndo_Latn/*"}, {"config_name": "nds_Latn", "data_files": "data/nds_Latn/*"}, {"config_name": "new_Deva", "data_files": "data/new_Deva/*"}, {"config_name": "nio_Cyrl", "data_files": "data/nio_Cyrl/*"}, {"config_name": "njn_Latn", "data_files": "data/njn_Latn/*"}, {"config_name": "njo_Latn", "data_files": "data/njo_Latn/*"}, {"config_name": "nld_Latn", "data_files": "data/nld_Latn/*"}, {"config_name": "nmf_Latn", "data_files": "data/nmf_Latn/*"}, {"config_name": "nmz_Latn", "data_files": "data/nmz_Latn/*"}, {"config_name": "nno_Latn", "data_files": "data/nno_Latn/*"}, {"config_name": "nob_Latn", "data_files": "data/nob_Latn/*"}, {"config_name": "nog_Cyrl", "data_files": "data/nog_Cyrl/*"}, {"config_name": "non_Latn", "data_files": "data/non_Latn/*"}, {"config_name": "npi_Deva", "data_files": "data/npi_Deva/*"}, {"config_name": "npi_Latn", "data_files": "data/npi_Latn/*"}, {"config_name": "npo_Latn", "data_files": "data/npo_Latn/*"}, {"config_name": "nrf_Latn", "data_files": "data/nrf_Latn/*"}, {"config_name": "nri_Latn", "data_files": "data/nri_Latn/*"}, {"config_name": "nrm_Latn", "data_files": "data/nrm_Latn/*"}, {"config_name": "nse_Latn", "data_files": "data/nse_Latn/*"}, {"config_name": "nus_Latn", "data_files": "data/nus_Latn/*"}, {"config_name": "nya_Latn", "data_files": "data/nya_Latn/*"}, {"config_name": "nyn_Latn", "data_files": "data/nyn_Latn/*"}, {"config_name": "nzm_Latn", "data_files": "data/nzm_Latn/*"}, {"config_name": "obo_Latn", "data_files": "data/obo_Latn/*"}, {"config_name": "oci_Latn", "data_files": "data/oci_Latn/*"}, {"config_name": "ojb_Latn", "data_files": "data/ojb_Latn/*"}, {"config_name": "olo_Latn", "data_files": "data/olo_Latn/*"}, {"config_name": "orv_Cyrl", "data_files": "data/orv_Cyrl/*"}, {"config_name": "ory_Latn", "data_files": "data/ory_Latn/*"}, {"config_name": "ory_Orya", "data_files": "data/ory_Orya/*"}, {"config_name": "oss_Cyrl", "data_files": "data/oss_Cyrl/*"}, {"config_name": "ota_Arab", "data_files": "data/ota_Arab/*"}, {"config_name": "oto_Latn", "data_files": "data/oto_Latn/*"}, {"config_name": "otw_Latn", "data_files": "data/otw_Latn/*"}, {"config_name": "pam_Latn", "data_files": "data/pam_Latn/*"}, {"config_name": "pan_Guru", "data_files": "data/pan_Guru/*"}, {"config_name": "pan_Latn", "data_files": "data/pan_Latn/*"}, {"config_name": "pap_Latn", "data_files": "data/pap_Latn/*"}, {"config_name": "pbt_Arab", "data_files": "data/pbt_Arab/*"}, {"config_name": "pcd_Latn", "data_files": "data/pcd_Latn/*"}, {"config_name": "pck_Latn", "data_files": "data/pck_Latn/*"}, {"config_name": "pcm_Latn", "data_files": "data/pcm_Latn/*"}, {"config_name": "pfl_Latn", "data_files": "data/pfl_Latn/*"}, {"config_name": "plt_Latn", "data_files": "data/plt_Latn/*"}, {"config_name": "pmq_Latn", "data_files": "data/pmq_Latn/*"}, {"config_name": "pmx_Latn", "data_files": "data/pmx_Latn/*"}, {"config_name": "pnb_Arab", "data_files": "data/pnb_Arab/*"}, {"config_name": "pnt_Grek", "data_files": "data/pnt_Grek/*"}, {"config_name": "pol_Latn", "data_files": "data/pol_Latn/*"}, {"config_name": "por_Latn", "data_files": "data/por_Latn/*"}, {"config_name": "pov_Latn", "data_files": "data/pov_Latn/*"}, {"config_name": "ppk_Latn", "data_files": "data/ppk_Latn/*"}, {"config_name": "pps_Latn", "data_files": "data/pps_Latn/*"}, {"config_name": "prg_Latn", "data_files": "data/prg_Latn/*"}, {"config_name": "pui_Latn", "data_files": "data/pui_Latn/*"}, {"config_name": "pxm_Latn", "data_files": "data/pxm_Latn/*"}, {"config_name": "quc_Latn", "data_files": "data/quc_Latn/*"}, {"config_name": "qul_Latn", "data_files": "data/qul_Latn/*"}, {"config_name": "qup_Latn", "data_files": "data/qup_Latn/*"}, {"config_name": "qus_Latn", "data_files": "data/qus_Latn/*"}, {"config_name": "quz_Latn", "data_files": "data/quz_Latn/*"}, {"config_name": "raw_Latn", "data_files": "data/raw_Latn/*"}, {"config_name": "rcf_Latn", "data_files": "data/rcf_Latn/*"}, {"config_name": "rel_Latn", "data_files": "data/rel_Latn/*"}, {"config_name": "rhg_Latn", "data_files": "data/rhg_Latn/*"}, {"config_name": "ria_Latn", "data_files": "data/ria_Latn/*"}, {"config_name": "rjs_Deva", "data_files": "data/rjs_Deva/*"}, {"config_name": "rmc_Latn", "data_files": "data/rmc_Latn/*"}, {"config_name": "rml_Latn", "data_files": "data/rml_Latn/*"}, {"config_name": "rmn_Latn", "data_files": "data/rmn_Latn/*"}, {"config_name": "rmy_Cyrl", "data_files": "data/rmy_Cyrl/*"}, {"config_name": "rmy_Latn", "data_files": "data/rmy_Latn/*"}, {"config_name": "rnl_Latn", "data_files": "data/rnl_Latn/*"}, {"config_name": "roh_Latn", "data_files": "data/roh_Latn/*"}, {"config_name": "ron_Cyrl", "data_files": "data/ron_Cyrl/*"}, {"config_name": "ron_Latn", "data_files": "data/ron_Latn/*"}, {"config_name": "rtm_Latn", "data_files": "data/rtm_Latn/*"}, {"config_name": "rue_Cyrl", "data_files": "data/rue_Cyrl/*"}, {"config_name": "run_Latn", "data_files": "data/run_Latn/*"}, {"config_name": "rus_Cyrl", "data_files": "data/rus_Cyrl/*"}, {"config_name": "sah_Cyrl", "data_files": "data/sah_Cyrl/*"}, {"config_name": "san_Deva", "data_files": "data/san_Deva/*"}, {"config_name": "san_Latn", "data_files": "data/san_Latn/*"}, {"config_name": "sat_Latn", "data_files": "data/sat_Latn/*"}, {"config_name": "sck_Deva", "data_files": "data/sck_Deva/*"}, {"config_name": "scn_Latn", "data_files": "data/scn_Latn/*"}, {"config_name": "sda_Latn", "data_files": "data/sda_Latn/*"}, {"config_name": "sdc_Latn", "data_files": "data/sdc_Latn/*"}, {"config_name": "sdh_Arab", "data_files": "data/sdh_Arab/*"}, {"config_name": "ses_Latn", "data_files": "data/ses_Latn/*"}, {"config_name": "sgc_Latn", "data_files": "data/sgc_Latn/*"}, {"config_name": "sgh_Cyrl", "data_files": "data/sgh_Cyrl/*"}, {"config_name": "sid_Latn", "data_files": "data/sid_Latn/*"}, {"config_name": "sin_Sinh", "data_files": "data/sin_Sinh/*"}, {"config_name": "sju_Latn", "data_files": "data/sju_Latn/*"}, {"config_name": "skr_Arab", "data_files": "data/skr_Arab/*"}, {"config_name": "slk_Latn", "data_files": "data/slk_Latn/*"}, {"config_name": "slv_Latn", "data_files": "data/slv_Latn/*"}, {"config_name": "sma_Latn", "data_files": "data/sma_Latn/*"}, {"config_name": "sme_Latn", "data_files": "data/sme_Latn/*"}, {"config_name": "smj_Latn", "data_files": "data/smj_Latn/*"}, {"config_name": "smn_Latn", "data_files": "data/smn_Latn/*"}, {"config_name": "smo_Latn", "data_files": "data/smo_Latn/*"}, {"config_name": "sms_Latn", "data_files": "data/sms_Latn/*"}, {"config_name": "smt_Latn", "data_files": "data/smt_Latn/*"}, {"config_name": "sna_Latn", "data_files": "data/sna_Latn/*"}, {"config_name": "snd_Arab", "data_files": "data/snd_Arab/*"}, {"config_name": "snd_Deva", "data_files": "data/snd_Deva/*"}, {"config_name": "snd_Latn", "data_files": "data/snd_Latn/*"}, {"config_name": "som_Latn", "data_files": "data/som_Latn/*"}, {"config_name": "sot_Latn", "data_files": "data/sot_Latn/*"}, {"config_name": "spa_Latn", "data_files": "data/spa_Latn/*"}, {"config_name": "srd_Latn", "data_files": "data/srd_Latn/*"}, {"config_name": "srp_Cyrl", "data_files": "data/srp_Cyrl/*"}, {"config_name": "srp_Latn", "data_files": "data/srp_Latn/*"}, {"config_name": "ssw_Latn", "data_files": "data/ssw_Latn/*"}, {"config_name": "sun_Latn", "data_files": "data/sun_Latn/*"}, {"config_name": "swe_Latn", "data_files": "data/swe_Latn/*"}, {"config_name": "swg_Latn", "data_files": "data/swg_Latn/*"}, {"config_name": "swh_Latn", "data_files": "data/swh_Latn/*"}, {"config_name": "syc_Syrc", "data_files": "data/syc_Syrc/*"}, {"config_name": "syl_Latn", "data_files": "data/syl_Latn/*"}, {"config_name": "szl_Latn", "data_files": "data/szl_Latn/*"}, {"config_name": "tab_Cyrl", "data_files": "data/tab_Cyrl/*"}, {"config_name": "tam_Latn", "data_files": "data/tam_Latn/*"}, {"config_name": "tam_Taml", "data_files": "data/tam_Taml/*"}, {"config_name": "taq_Tfng", "data_files": "data/taq_Tfng/*"}, {"config_name": "tat_Cyrl", "data_files": "data/tat_Cyrl/*"}, {"config_name": "tat_Latn", "data_files": "data/tat_Latn/*"}, {"config_name": "tcy_Knda", "data_files": "data/tcy_Knda/*"}, {"config_name": "tcz_Latn", "data_files": "data/tcz_Latn/*"}, {"config_name": "tel_Latn", "data_files": "data/tel_Latn/*"}, {"config_name": "tel_Telu", "data_files": "data/tel_Telu/*"}, {"config_name": "tet_Latn", "data_files": "data/tet_Latn/*"}, {"config_name": "tgk_Cyrl", "data_files": "data/tgk_Cyrl/*"}, {"config_name": "tha_Thai", "data_files": "data/tha_Thai/*"}, {"config_name": "thl_Deva", "data_files": "data/thl_Deva/*"}, {"config_name": "tig_Ethi", "data_files": "data/tig_Ethi/*"}, {"config_name": "tir_Ethi", "data_files": "data/tir_Ethi/*"}, {"config_name": "tkl_Latn", "data_files": "data/tkl_Latn/*"}, {"config_name": "tkr_Cyrl", "data_files": "data/tkr_Cyrl/*"}, {"config_name": "tlh_Latn", "data_files": "data/tlh_Latn/*"}, {"config_name": "tly_Latn", "data_files": "data/tly_Latn/*"}, {"config_name": "tok_Latn", "data_files": "data/tok_Latn/*"}, {"config_name": "ton_Latn", "data_files": "data/ton_Latn/*"}, {"config_name": "tpi_Latn", "data_files": "data/tpi_Latn/*"}, {"config_name": "tpw_Latn", "data_files": "data/tpw_Latn/*"}, {"config_name": "trc_Latn", "data_files": "data/trc_Latn/*"}, {"config_name": "trp_Latn", "data_files": "data/trp_Latn/*"}, {"config_name": "trs_Latn", "data_files": "data/trs_Latn/*"}, {"config_name": "ttj_Latn", "data_files": "data/ttj_Latn/*"}, {"config_name": "tuk_Arab", "data_files": "data/tuk_Arab/*"}, {"config_name": "tuk_Cyrl", "data_files": "data/tuk_Cyrl/*"}, {"config_name": "tuk_Latn", "data_files": "data/tuk_Latn/*"}, {"config_name": "tur_Latn", "data_files": "data/tur_Latn/*"}, {"config_name": "tuv_Latn", "data_files": "data/tuv_Latn/*"}, {"config_name": "twx_Latn", "data_files": "data/twx_Latn/*"}, {"config_name": "tyv_Cyrl", "data_files": "data/tyv_Cyrl/*"}, {"config_name": "tzl_Latn", "data_files": "data/tzl_Latn/*"}, {"config_name": "tzm_Tfng", "data_files": "data/tzm_Tfng/*"}, {"config_name": "udm_Cyrl", "data_files": "data/udm_Cyrl/*"}, {"config_name": "uig_Arab", "data_files": "data/uig_Arab/*"}, {"config_name": "uig_Cyrl", "data_files": "data/uig_Cyrl/*"}, {"config_name": "uig_Latn", "data_files": "data/uig_Latn/*"}, {"config_name": "ukr_Cyrl", "data_files": "data/ukr_Cyrl/*"}, {"config_name": "urd_Arab", "data_files": "data/urd_Arab/*"}, {"config_name": "urd_Latn", "data_files": "data/urd_Latn/*"}, {"config_name": "uzn_Cyrl", "data_files": "data/uzn_Cyrl/*"}, {"config_name": "uzn_Latn", "data_files": "data/uzn_Latn/*"}, {"config_name": "uzs_Arab", "data_files": "data/uzs_Arab/*"}, {"config_name": "vap_Latn", "data_files": "data/vap_Latn/*"}, {"config_name": "vie_Latn", "data_files": "data/vie_Latn/*"}, {"config_name": "vot_Latn", "data_files": "data/vot_Latn/*"}, {"config_name": "vro_Latn", "data_files": "data/vro_Latn/*"}, {"config_name": "war_Latn", "data_files": "data/war_Latn/*"}, {"config_name": "way_Latn", "data_files": "data/way_Latn/*"}, {"config_name": "wba_Latn", "data_files": "data/wba_Latn/*"}, {"config_name": "wbm_Latn", "data_files": "data/wbm_Latn/*"}, {"config_name": "wes_Latn", "data_files": "data/wes_Latn/*"}, {"config_name": "whk_Latn", "data_files": "data/whk_Latn/*"}, {"config_name": "wlx_Latn", "data_files": "data/wlx_Latn/*"}, {"config_name": "wol_Latn", "data_files": "data/wol_Latn/*"}, {"config_name": "wsg_Telu", "data_files": "data/wsg_Telu/*"}, {"config_name": "wwa_Latn", "data_files": "data/wwa_Latn/*"}, {"config_name": "xal_Cyrl", "data_files": "data/xal_Cyrl/*"}, {"config_name": "xho_Latn", "data_files": "data/xho_Latn/*"}, {"config_name": "xmm_Latn", "data_files": "data/xmm_Latn/*"}, {"config_name": "xmv_Latn", "data_files": "data/xmv_Latn/*"}, {"config_name": "xog_Latn", "data_files": "data/xog_Latn/*"}, {"config_name": "yaz_Latn", "data_files": "data/yaz_Latn/*"}, {"config_name": "ydd_Hebr", "data_files": "data/ydd_Hebr/*"}, {"config_name": "yor_Latn", "data_files": "data/yor_Latn/*"}, {"config_name": "yrk_Cyrl", "data_files": "data/yrk_Cyrl/*"}, {"config_name": "yrl_Latn", "data_files": "data/yrl_Latn/*"}, {"config_name": "yua_Latn", "data_files": "data/yua_Latn/*"}, {"config_name": "yue_Hani", "data_files": "data/yue_Hani/*"}, {"config_name": "zea_Latn", "data_files": "data/zea_Latn/*"}, {"config_name": "zgh_Tfng", "data_files": "data/zgh_Tfng/*"}, {"config_name": "zom_Latn", "data_files": "data/zom_Latn/*"}, {"config_name": "zsm_Arab", "data_files": "data/zsm_Arab/*"}, {"config_name": "zsm_Latn", "data_files": "data/zsm_Latn/*"}, {"config_name": "zul_Latn", "data_files": "data/zul_Latn/*"}]}
| false
|
False
| 2026-01-09T16:45:58
| 248
| 42
| false
|
af3f4ca895450216d4771cdbf3e3b95c5bacaa2a
|
💬 FineTranslations
The world's knowledge in 1+1T tokens of parallel text
What is it?
This dataset contains over 1 trillion tokens of parallel text in English and 500+ languages. It was obtained by translating data from 🥂 FineWeb2 into English using Gemma3 27B.
We relied on datatrove's inference runner to deploy a synthetic data pipeline at scale. Its checkpointing and VLLM lifecycle management features allowed us to use leftover compute from the HF cluster… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/finetranslations.
| 38,530
| 38,530
|
['task_categories:text-generation' 'task_categories:translation'
'language:abk' 'language:abq' 'language:abs' 'language:acm'
'language:adh' 'language:adi' 'language:ady' 'language:aeb'
'language:afr' 'language:agx' 'language:aii' 'language:aim'
'language:ain' 'language:ajz' 'language:akb' 'language:aln'
'language:als' 'language:alt' 'language:amh' 'language:anp'
'language:aoz' 'language:apc' 'language:apt' 'language:arb'
'language:arg' 'language:arq' 'language:ars' 'language:ary'
'language:arz' 'language:asm' 'language:ast' 'language:atb'
'language:ava' 'language:awa' 'language:ayp' 'language:ayr'
'language:azb' 'language:azj' 'language:bak' 'language:bam'
'language:ban' 'language:bar' 'language:bas' 'language:bbc'
'language:bbk' 'language:bcl' 'language:bdq' 'language:bel'
'language:ben' 'language:bew' 'language:bho' 'language:bhp'
'language:bis' 'language:biu' 'language:bjn' 'language:bod'
'language:bos' 'language:brh' 'language:brx' 'language:bts'
'language:btx' 'language:bug' 'language:bul' 'language:bwi'
'language:bxr' 'language:cat' 'language:cbk' 'language:ccp'
'language:ceb' 'language:ces' 'language:cfm' 'language:cha'
'language:che' 'language:chr' 'language:chu' 'language:chv'
'language:cjs' 'language:ckb' 'language:ckt' 'language:cmn'
'language:cnh' 'language:cnw' 'language:cos' 'language:crh'
'language:crj' 'language:crk' 'language:crl' 'language:crs'
'language:csb' 'language:csw' 'language:csy' 'language:ctd'
'language:cym' 'language:czt' 'language:dak' 'language:dan'
'language:dar' 'language:deu' 'language:dik' 'language:diu'
'language:div' 'language:dje' 'language:dks' 'language:dln'
'language:dng' 'language:dnw' 'language:doi' 'language:dru'
'language:dsb' 'language:dtp' 'language:dty' 'language:dzo'
'language:ekk' 'language:ell' 'language:enl' 'language:enm'
'language:epo' 'language:ess' 'language:eus' 'language:eve'
'language:ewo' 'language:ext' 'language:fao' 'language:fas'
'language:ffm' 'language:fij' 'language:fil' 'language:fin'
'language:fit' 'language:fkv' 'language:fmu' 'language:fra'
'language:fro' 'language:frp' 'language:fry' 'language:fuf'
'language:fur' 'language:fuv' 'language:gag' 'language:gaz'
'language:gcf' 'language:gla' 'language:gle' 'language:glg'
'language:glk' 'language:glv' 'language:gmh' 'language:gnb'
'language:goh' 'language:gom' 'language:gos' 'language:grc'
'language:gsw' 'language:gug' 'language:guj' 'language:guz'
'language:hac' 'language:hae' 'language:hak' 'language:hat'
'language:hau' 'language:haw' 'language:hbo' 'language:heb'
'language:her' 'language:hif' 'language:hil' 'language:hin'
'language:hmr' 'language:hne' 'language:hns' 'language:hrv'
'language:hrx' 'language:hsb' 'language:hun' 'language:hwc'
'language:hye' 'language:hyw' 'language:iba' 'language:ibg'
'language:ibo' 'language:ife' 'language:ike' 'language:ikt'
'language:ilo' 'language:ina' 'language:ind' 'language:inh'
'language:isl' 'language:ita' 'language:ivv' 'language:jav'
'language:jpn' 'language:jun' 'language:kaa' 'language:kab'
'language:kac' 'language:kak' 'language:kal' 'language:kam'
'language:kan' 'language:kas' 'language:kat' 'language:kaz'
'language:kbd' 'language:kca' 'language:kdh' 'language:kdr'
'language:kea' 'language:kei' 'language:kgp' 'language:kha'
'language:khk' 'language:khm' 'language:kik' 'language:kin'
'language:kir' 'language:kiu' 'language:kjb' 'language:kjh'
'language:kmr' 'language:knc' 'language:koi' 'language:kor'
'language:kos' 'language:kpv' 'language:krj' 'language:krl'
'language:kru' 'language:ksh' 'language:ksw' 'language:ktj'
'language:ktz' 'language:kua' 'language:kum' 'language:kwn'
'language:kyu' 'language:kzj' 'language:lad' 'language:lao'
'language:lat' 'language:lbe' 'language:ldn' 'language:lew'
'language:lez' 'language:lfn' 'language:lim' 'language:lin'
'language:lis' 'language:lit' 'language:lki' 'language:lld'
'language:lmk' 'language:lnd' 'language:lrc' 'language:ltg'
'language:ltz' 'language:lud' 'language:lug' 'language:luo'
'language:lus' 'language:lvs' 'language:lwg' 'language:lzh'
'language:mag' 'language:mah' 'language:mai' 'language:mak'
'language:mal' 'language:mar' 'language:mas' 'language:mbf'
'language:mdf' 'language:mer' 'language:mfe' 'language:mfg'
'language:mfy' 'language:mhi' 'language:mhr' 'language:mhy'
'language:min' 'language:mip' 'language:mjw' 'language:mkd'
'language:mlt' 'language:mni' 'language:mnk' 'language:mns'
'language:mnw' 'language:moh' 'language:mph' 'language:mqy'
'language:mri' 'language:mrj' 'language:mrw' 'language:mtg'
'language:mui' 'language:mup' 'language:mus' 'language:mvp'
'language:mwf' 'language:mwl' 'language:mww' 'language:mya'
'language:myv' 'language:myx' 'language:mzh' 'language:nah'
'language:nan' 'language:nap' 'language:naq' 'language:nbu'
'language:nde' 'language:ndo' 'language:nds' 'language:new'
'language:nio' 'language:njn' 'language:njo' 'language:nld'
'language:nmf' 'language:nmz' 'language:nno' 'language:nob'
'language:nog' 'language:non' 'language:npi' 'language:npo'
'language:nrf' 'language:nri' 'language:nrm' 'language:nse'
'language:nus' 'language:nya' 'language:nyn' 'language:nzm'
'language:obo' 'language:oci' 'language:ojb' 'language:olo'
'language:orv' 'language:ory' 'language:oss' 'language:ota'
'language:oto' 'language:otw' 'language:pam' 'language:pan'
'language:pap' 'language:pbt' 'language:pcd' 'language:pck'
'language:pcm' 'language:pfl' 'language:plt' 'language:pmq'
'language:pmx' 'language:pnb' 'language:pnt' 'language:pol'
'language:por' 'language:pov' 'language:ppk' 'language:pps'
'language:prg' 'language:pui' 'language:pxm' 'language:quc'
'language:qul' 'language:qup' 'language:qus' 'language:quz'
'language:raw' 'language:rcf' 'language:rel' 'language:rhg'
'language:ria' 'language:rjs' 'language:rmc' 'language:rml'
'language:rmn' 'language:rmy' 'language:rnl' 'language:roh'
'language:ron' 'language:rtm' 'language:rue' 'language:run'
'language:rus' 'language:sah' 'language:san' 'language:sat'
'language:sck' 'language:scn' 'language:sda' 'language:sdc'
'language:sdh' 'language:ses' 'language:sgc' 'language:sgh'
'language:sid' 'language:sin' 'language:sju' 'language:skr'
'language:slk' 'language:slv' 'language:sma' 'language:sme'
'language:smj' 'language:smn' 'language:smo' 'language:sms'
'language:smt' 'language:sna' 'language:snd' 'language:som'
'language:sot' 'language:spa' 'language:srd' 'language:srp'
'language:ssw' 'language:sun' 'language:swe' 'language:swg'
'language:swh' 'language:syc' 'language:syl' 'language:szl'
'language:tab' 'language:tam' 'language:taq' 'language:tat'
'language:tcy' 'language:tcz' 'language:tel' 'language:tet'
'language:tgk' 'language:tha' 'language:thl' 'language:tig'
'language:tir' 'language:tkl' 'language:tkr' 'language:tlh'
'language:tly' 'language:tok' 'language:ton' 'language:tpi'
'language:tpw' 'language:trc' 'language:trp' 'language:trs'
'language:ttj' 'language:tuk' 'language:tur' 'language:tuv'
'language:twx' 'language:tyv' 'language:tzl' 'language:tzm'
'language:udm' 'language:uig' 'language:ukr' 'language:urd'
'language:uzn' 'language:uzs' 'language:vap' 'language:vie'
'language:vot' 'language:vro' 'language:war' 'language:way'
'language:wba' 'language:wbm' 'language:wes' 'language:whk'
'language:wlx' 'language:wol' 'language:wsg' 'language:wwa'
'language:xal' 'language:xho' 'language:xmm' 'language:xmv'
'language:xog' 'language:yaz' 'language:ydd' 'language:yor'
'language:yrk' 'language:yrl' 'language:yua' 'language:yue'
'language:zea' 'language:zgh' 'language:zom' 'language:zsm'
'language:zul' 'license:odc-by' 'size_categories:1B<n<10B'
'format:parquet' 'modality:tabular' 'modality:text' 'library:datasets'
'library:dask' 'library:polars' 'library:mlcroissant' 'region:us']
| 2026-01-08T13:31:31
| null | null |
[
0.5460655689239502,
0.4864030182361603,
0.5000516176223755,
0.9900186657905579,
0.1692168265581131,
0.7447656393051147,
0.32584118843078613,
0.7762579321861267,
0.7680622339248657,
0.4788389205932617,
0.6054204106330872,
0.6475067734718323,
0.5458201766014099,
0.6172909736633301,
0.9210823178291321,
0.369437575340271,
0.5793867707252502,
0.7921778559684753,
0.3671889901161194,
0.8416861295700073,
0.14414748549461365,
0.69453364610672,
0.494844913482666,
0.3492816090583801,
0.5076533555984497,
0.27494433522224426,
0.00986990425735712,
0.8539280295372009,
0.6788570880889893,
0.5468606948852539,
0.21578483283519745,
0.14838171005249023,
0.3265358805656433,
0.8389328122138977,
0.19483248889446259,
0.5402029156684875,
0.828289270401001,
0.2235858142375946,
0.1549556404352188,
0.7334448099136353,
0.44580912590026855,
0.7053351998329163,
0.8088645935058594,
0.05851937085390091,
0.11989819258451462,
0.02537793293595314,
0.6909528970718384,
0.7144057154655457,
0.5851600170135498,
0.19637438654899597,
0.68854820728302,
0.6664077043533325,
0.5329163074493408,
0.022251369431614876,
0.9879553914070129,
0.11935113370418549,
0.1403881311416626,
0.5699728727340698,
0.9184528589248657,
0.21703658998012543,
0.4408474564552307,
0.3811996281147003,
0.4843685030937195,
0.8864269256591797,
0.09325139224529266,
0.11291766166687012,
0.3155858516693115,
0.5383902192115784,
0.8460705280303955,
0.276004821062088,
0.9371681809425354,
0.6035900712013245,
0.19904126226902008,
0.7662090063095093,
0.24600516259670258,
0.2001059651374817,
0.36187639832496643,
0.9681530594825745,
0.27530068159103394,
0.9907775521278381,
0.7600200176239014,
0.03329284489154816,
0.2198706567287445,
0.3641273081302643,
0.3010384738445282,
0.8482109308242798,
0.20310215651988983,
0.9090430736541748,
0.5627179145812988,
0.7658533453941345,
0.9953508973121643,
0.009814544580876827,
0.20374052226543427,
0.25901341438293457,
0.10358471423387527,
0.8750115036964417,
0.26230189204216003,
0.854164719581604,
0.8488139510154724,
0.7139420509338379,
0.42490342259407043,
0.9015870094299316,
0.4074624478816986,
0.21294385194778442,
0.7401962280273438,
0.01683482900261879,
0.17476898431777954,
0.9487276077270508,
0.9327031970024109,
0.13744106888771057,
0.5516064763069153,
0.13246873021125793,
0.43504786491394043,
0.733884334564209,
0.21421490609645844,
0.052485402673482895,
0.6305380463600159,
0.5153260231018066,
0.39437687397003174,
0.05941732972860336,
0.7222175002098083,
0.055794961750507355,
0.9229758977890015,
0.23083122074604034,
0.7408763766288757,
0.8140774965286255,
0.5680239200592041,
0.5776697397232056
] |
695df55a4e351abe5277cca5
|
UniParser/OmniScience
|
UniParser
|
{"license": "cc-by-nc-sa-4.0", "task_categories": ["image-to-text"], "extra_gated_heading": "Request Access to This Dataset", "extra_gated_description": "Please complete the required fields below to request access. Access will be automatically granted upon submission.", "extra_gated_fields": {"Full Name": {"type": "text"}, "Email": {"type": "text"}, "Affiliation (Company / University)": {"type": "text"}, "I agree this dataset is for non-commercial use ONLY": {"type": "checkbox"}}, "extra_gated_button_content": "Submit Access Request"}
| false
|
auto
| 2026-01-22T02:55:43
| 73
| 41
| false
|
9c9fdac9ea87b36e3889330463cd4aee2e81ce95
|
OmniScience: A Large-scale Dataset for Scientific Image Understanding
🚀 2026-01-21: The OmniScience dataset ranked Top 8 on Hugging Face Datasets Trending (Top 1 on Image Caption Filed). 🚀 2026-01-17: The OmniScience dataset surpassed 5,000 downloads within 5 days of its release. 🚀 2026-01-12: Official release of the OmniScience dataset. 🚀 2025-06-01: Completion of the original dataset collection.
📘 Dataset Summary
OmniScience is an ultra-large-scale… See the full description on the dataset page: https://huggingface.co/datasets/UniParser/OmniScience.
| 7,703
| 7,710
|
['task_categories:image-to-text' 'license:cc-by-nc-sa-4.0'
'size_categories:1M<n<10M' 'format:parquet' 'format:optimized-parquet'
'modality:image' 'modality:text' 'library:datasets' 'library:dask'
'library:polars' 'library:mlcroissant' 'arxiv:2512.15098' 'region:us']
| 2026-01-07T05:55:38
| null | null |
[
0.7002298831939697,
0.6913377046585083,
0.28462982177734375,
0.07918864488601685,
0.4246640205383301,
0.22101019322872162,
0.31037789583206177,
0.22279313206672668,
0.6234893202781677,
0.33018070459365845,
0.2684820592403412,
0.2061517983675003,
0.5017613172531128,
0.3566916584968567,
0.7928217053413391,
0.3375627398490906,
0.948652446269989,
0.3032640218734741,
0.3912525475025177,
0.5104259848594666,
0.2658320665359497,
0.14498846232891083,
0.5663023591041565,
0.8215346336364746,
0.9239692091941833,
0.7520247101783752,
0.9117278456687927,
0.08351858705282211,
0.7555398344993591,
0.625757098197937,
0.14478322863578796,
0.6977948546409607,
0.5003620386123657,
0.5878759026527405,
0.029064353555440903,
0.8030285239219666,
0.43763452768325806,
0.023602981120347977,
0.5883778929710388,
0.2418387085199356,
0.9495572447776794,
0.8303340673446655,
0.2942137122154236,
0.7063578367233276,
0.9800190925598145,
0.35802528262138367,
0.3078705668449402,
0.37001916766166687,
0.8462343811988831,
0.09422328323125839,
0.6291758418083191,
0.10067223012447357,
0.6896462440490723,
0.8160650730133057,
0.5695043802261353,
0.9731095433235168,
0.180853471159935,
0.050328727811574936,
0.15738114714622498,
0.06720813363790512,
0.08242251724004745,
0.5342674851417542,
0.005368136800825596,
0.06233527138829231,
0.9710041284561157,
0.4263882040977478,
0.6718450784683228,
0.5595147609710693,
0.0301688089966774,
0.860681414604187,
0.3318241834640503,
0.8330184817314148,
0.27536389231681824,
0.785151481628418,
0.8803387880325317,
0.14664430916309357,
0.054812852293252945,
0.7702968120574951,
0.8199305534362793,
0.7719488143920898,
0.6234108209609985,
0.8391738533973694,
0.03363136947154999,
0.8123155236244202,
0.682273268699646,
0.927149772644043,
0.1013626828789711,
0.9932480454444885,
0.32137149572372437,
0.14307008683681488,
0.34334322810173035,
0.016790306195616722,
0.3714950978755951,
0.5971284508705139,
0.9313204884529114,
0.9960381388664246,
0.6887592077255249,
0.003005716484040022,
0.4349311888217926,
0.022393958643078804,
0.7449952960014343,
0.5309765934944153,
0.6062058210372925,
0.18819929659366608,
0.3148803114891052,
0.011966168880462646,
0.16071847081184387,
0.5694673657417297,
0.24513927102088928,
0.9780240654945374,
0.39046624302864075,
0.6224943399429321,
0.9467357397079468,
0.07080229371786118,
0.5853633880615234,
0.3693235218524933,
0.5110101699829102,
0.1316596269607544,
0.508954644203186,
0.04378000646829605,
0.5165079832077026,
0.21039265394210815,
0.2732948064804077,
0.29311391711235046,
0.8102161884307861,
0.6428850293159485,
0.8823056817054749,
0.8325544595718384
] |
68bb43410b54503c335cb3d8
|
HuggingFaceFW/finepdfs
|
HuggingFaceFW
| "{\"license\": \"odc-by\", \"task_categories\": [\"text-generation\"], \"pretty_name\": \"\\ud83d\\u(...TRUNCATED)
| false
|
False
| 2026-01-09T10:37:26
| 790
| 39
| false
|
89f5411afb089ee310a09df61e7a58a1bf6d081c
| "\n\nLiberating 3T of the finest tokens from PDFs\n\n\n\t\n\t\t\n\t\tWhat is this?\n\t\n\nAs we run (...TRUNCATED)
| 23,756
| 250,875
| "['task_categories:text-generation' 'language:aai' 'language:aak' ...\n 'arxiv:2506.18421' 'arxiv:21(...TRUNCATED)
| 2025-09-05T20:08:33
| null | null | [0.0881318747997284,0.8725446462631226,0.9592418074607849,0.767154335975647,0.7906309962272644,0.931(...TRUNCATED)
|
69314c12930718bfbd732f22
|
LEMAS-Project/LEMAS-Dataset-train
|
LEMAS-Project
| "{\"license\": \"cc-by-nc-4.0\", \"language\": [\"it\", \"pt\", \"es\", \"fr\", \"de\", \"vi\", \"id(...TRUNCATED)
| false
|
False
| 2026-01-09T03:33:49
| 71
| 29
| false
|
e8bc66643f59bb55097203529022ff809de69c5d
| "\n\t\n\t\t\n\t\tOverview\n\t\n\nThis dataset is part of LEMAS-Project (lemas-project.github.io/LEMA(...TRUNCATED)
| 16,603
| 17,784
| "['task_categories:text-to-speech'\n 'task_categories:automatic-speech-recognition' 'language:it'\n (...TRUNCATED)
| 2025-12-04T08:53:38
| null | null | [0.6445622444152832,0.7504190802574158,0.5153056979179382,0.656548261642456,0.1625341773033142,0.197(...TRUNCATED)
|
End of preview.
Hub Stats (Lance format)
This dataset contains Hugging Face Hub statistics in Lance format, converted from the original cfahlgren1/hub-stats dataset.
Files
models.lance- Statistics for all models on the Hub (~2.5M rows)datasets.lance- Statistics for all datasets on the Hubspaces.lance- Statistics for all spaces on the Hub
Usage
import lance
# Load a dataset remotely
ds = lance.dataset("hf://datasets/julien-c/hub-stats-lance/datasets.lance")
# Convert to pandas
df = ds.to_table().to_pandas()
# Or query with SQL-like filters
table = ds.to_table(filter="downloads > 1000")
Example: Query datasets by author
import lance
ds = lance.dataset("hf://datasets/julien-c/hub-stats-lance/datasets.lance")
results = ds.to_table(filter="author = 'microsoft'").to_pandas()
# Sort by downloads
top = results.sort_values("downloads", ascending=False).head(10)
print(top[["id", "likes", "downloads"]])
Output:
id likes downloads
microsoft/ms_marco 221 11120
microsoft/orca-math-word-problems-200k 468 6499
microsoft/bing_coronavirus_query_set 0 6002
microsoft/wiki_qa 69 5737
microsoft/rStar-Coder 225 3492
microsoft/Updesh_beta 8 3223
microsoft/Dayhoff 7 2922
microsoft/meta_woz 6 2801
microsoft/cats_vs_dogs 61 1883
microsoft/IMAGE_UNDERSTANDING 6 1833
Example: Vector similarity search
import lance
import numpy as np
ds = lance.dataset("hf://datasets/julien-c/hub-stats-lance/datasets.lance")
# Get an embedding to use as query (e.g., from microsoft/ms_marco)
query_row = ds.to_table(filter="id = 'microsoft/ms_marco'").to_pandas()
query_embedding = np.array(query_row["embedding"].iloc[0])
# Find 10 nearest neighbors
results = ds.to_table(
nearest={"column": "embedding", "q": query_embedding, "k": 10}
).to_pandas()
print(results[["id", "likes", "downloads", "_distance"]])
Output:
id likes downloads _distance
microsoft/ms_marco 221 11120 2.23
jiwonii97/atalk_as3 0 0 10.61
AI-Art-Collab/ae5 0 1 10.85
wgwgwgwgw/dbbdbbd 0 9 10.90
1FDSFS/56803 0 8 10.94
Why Lance?
Lance is a modern columnar data format optimized for ML workflows:
- Fast random access and filtering
- Efficient for large datasets
- Native support for vector search
- Zero-copy integration with PyArrow/Pandas
- Downloads last month
- 31