datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
transformers_circleci_workflow_runstransformers.js-docstransformersstsb
Dataset Card for STSB
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data.
Each pair is human-annotated with a similarity score from 1 to 5. However, for this variant, the similarity scores are normalized to between 0 and 1.
Dataset Details
Columns: "sentence1", "sentence2", "score"
Column types: str, str, float
Examples:{
'sentence1': 'A… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/stsb.transformers-gh-memory
huggingface/transformers issues and pull requests, as a funes memory
Every issue and pull request of huggingface/transformers
with activity since 2018-01-01 — opening bodies, comments, reviews, inline review comments and PR
diffs — chunked, embedded and written to a Lance table by
funes, so the tracker can be searched by meaning and read
back thread by thread. Kept fresh every few minutes by the
funes-github Space.
Use it
Set funes up for your agent the usual way… See the full description on the dataset page: https://huggingface.co/datasets/dacorvo/transformers-gh-memory.transformers_flash_attn_citransformers-pr
Transformers PR Dataset
Normalized snapshots of issues, pull requests, comments, reviews, and linkage data from huggingface/transformers.
Files:
issues.parquet
pull_requests.parquet
comments.parquet
issue_comments.parquet (derived view of issue discussion comments)
pr_comments.parquet (derived view of pull request discussion comments)
reviews.parquet
pr_files.parquet
pr_diffs.parquet
review_comments.parquet
links.parquet
events.parquet
new_contributors.parquet… See the full description on the dataset page: https://huggingface.co/datasets/evalstate/transformers-pr.transformers-synthetic-assets
transformers-synthetic-assets
Synthetic media fixtures for Transformers tests. These assets are generated from prompts or deterministic code and are not derived from third-party source files.
transformers_pr_cimiracl
Dataset Card for MIRACL
This is a reformatting of the MIRACL dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data.
Dataset Subsets
...-triplet subset
Columns: "anchor", "positive", "negative"
Column types: str, str, str
Examples:{
'anchor': '月球到地球的距离是多少？',
'positive': '月球距離\n月球距離 (LD) 是天文學上從地球到月球的距離，從地球到月球的平均距離是384,401公里 (238,856英里)。因為月球在橢圓軌道上運動，實際的距離隨時都在變化著。',
'negative':… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/miracl.transformers_daily_ciparallel-sentences-ccmatrix
Dataset Card for Parallel Sentences - CCMatrix
This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. The texts originate from the CCMatrix dataset.
Related Datasets
The following datasets are also a part of the Parallel Sentences collection:
parallel-sentences-europarl
parallel-sentences-global-voices
parallel-sentences-muse
parallel-sentences-jw300
parallel-sentences-news-commentary… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/parallel-sentences-ccmatrix.NanoBEIR-encircleci-test-resultstransformers_daily_cisplitted_PretrainGiftEvaleli5
Dataset Card for ELI5
This dataset is a collection of question-answer pairs, collected from the Explain Like I'm 5 subreddit. See ELI5 for additional information.
This dataset can be used directly with Sentence Transformers to train embedding models.
Dataset Subsets
pair subset
Columns: "question", "answer"
Column types: str, str
Examples:{
'question': 'Why chemical weapons considered more indiscriminate than conventional weapons?',
'answer': "Well, any… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/eli5.msmarco-distilbert-margin-mse-mean-dot-v1
MS MARCO with hard negatives from distilbert-margin-mse-mean-dot-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-mean-dot-v1.s2orc
Dataset Card for S2ORC
This dataset contains titles, abstracts, and citations from scientific papers from the Semantic Scholar Open Research Corpus (S2ORC).
This dataset can and has been used to train embedding models, and works out of the box to train or finetune Sentence Transformer models.
In our experiments, title-abstract pairs result in the highest performance, followed by titles-citations and then abstract-citations pairs.
Dataset Subsets… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/s2orc.parallel-sentences-talks
Dataset Card for Parallel Sentences - Talks
This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentences originate from the OPUS website.
In particular, this dataset contains the Talks dataset.
Related Datasets
The following datasets are also a part of the Parallel Sentences collection:
parallel-sentences-europarl
parallel-sentences-global-voices
parallel-sentences-muse… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks.all-nli
Dataset Card for AllNLI
This dataset is a concatenation of the SNLI and MultiNLI datasets.
Despite originally being intended for Natural Language Inference (NLI), this dataset can be used for training/finetuning an embedding model for semantic textual similarity.
Dataset Subsets
pair-class subset
Columns: "premise", "hypothesis", "label"
Column types: str, str, class with {"0": "entailment", "1": "neutral", "2", "contradiction"}
Examples:{
'premise': 'A… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/all-nli.msmarco-bm25
MS MARCO with hard negatives from bm25
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:
msmarco-bm25… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-bm25.parallel-sentences-wikimatrix
Dataset Card for Parallel Sentences - WikiMatrix
This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentences originate from the OPUS website.
In particular, this dataset contains the WikiMatrix dataset.
Related Datasets
The following datasets are also a part of the Parallel Sentences collection:
parallel-sentences-europarl
parallel-sentences-global-voices… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/parallel-sentences-wikimatrix.transformers_image_docmsmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
MS MARCO with hard negatives from co-condenser-margin-mse-sym-mnrl-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1.smiles-transformers
smiles-transformers dataset
TODO: Add references to the datasets we curated
dataset features
name: text
Molecule SMILES : string
name: formula
Molecular formula : string
name: NumHDonors
Number of hidrogen bond donors : int
name: NumHAcceptors
Number of hidrogen bond acceptors : int
name: MolLogP
Wildman-Crippen LogP : float
name: NumHeteroatoms
Number of hetero atoms: int
name: RingCount
Number of rings : int
name: NumRotatableBonds
Number of rotable… See the full description on the dataset page: https://huggingface.co/datasets/maykcaldas/smiles-transformers.mldr
Dataset Card for MLDR
This is a reformatting of the MLDR dataset used to train the BGE-M3 model. See the full BGE-M3 dataset in Shitao/bge-m3-data.
Dataset Subsets
...-triplet subset
Columns: "anchor", "positive", "negative"
Column types: str, str, str
Examples:{
'anchor': '¿Cuál es el efecto de la dilución crema simple en los pelajes básicos de los caballos?',
'positive': 'Gen crema\n\nPelajes resultantes\n\nTodos los caballos tienen dos copias del gen… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/mldr.parallel-sentences-opensubtitles
Dataset Card for Parallel Sentences - OpenSubtitles
This dataset contains parallel sentences (i.e. English sentence + the same sentences in another language) for numerous other languages. Most of the sentences originate from the OPUS website.
In particular, this dataset contains the OpenSubtitles dataset.
Warning! The quality of this dataset is not great; many of the english and non-english texts don't match well, or are fully empty.
Related Datasets
The following… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/parallel-sentences-opensubtitles.msmarco-distilbert-margin-mse-sym-mnrl-mean-v2
MS MARCO with hard negatives from distilbert-margin-mse-sym-mnrl-mean-v2
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-sym-mnrl-mean-v2.natural-questions
Dataset Card for Natural Questions
This dataset is a collection of question-answer pairs from the Natural Questions dataset. See Natural Questions for additional information.
This dataset can be used directly with Sentence Transformers to train embedding models.
Dataset Subsets
pair subset
Columns: "question", "answer"
Column types: str, str
Examples:{
'query': 'the si unit of the electric field is',
'answer': 'Electric field An electric field… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/natural-questions.