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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 dataset

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_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
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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
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695fb1b373628fa861fe84cf
HuggingFaceFW/finetranslations
HuggingFaceFW
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2026-01-09T16:45:58
248
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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
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'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 Hub
  • spaces.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
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