datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sprintduplicatequestions-pairclassification
SprintDuplicateQuestions
An MTEB dataset
Massive Text Embedding Benchmark
Duplicate questions from the Sprint community.
Task category
t2t
Domains
Programming, Written
Reference
https://www.aclweb.org/anthology/D18-1131/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["SprintDuplicateQuestions"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/sprintduplicatequestions-pairclassification.twittersemeval2015-pairclassification
TwitterSemEval2015
An MTEB dataset
Massive Text Embedding Benchmark
Paraphrase-Pairs of Tweets from the SemEval 2015 workshop.
Task category
t2t
Domains
Social, Written
Reference
https://alt.qcri.org/semeval2015/task1/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["TwitterSemEval2015"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/twittersemeval2015-pairclassification.twitterurlcorpus-pairclassification
TwitterURLCorpus
An MTEB dataset
Massive Text Embedding Benchmark
Paraphrase-Pairs of Tweets.
Task category
t2t
Domains
Social, Written
Reference
https://languagenet.github.io/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["TwitterURLCorpus"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run… See the full description on the dataset page: https://huggingface.co/datasets/mteb/twitterurlcorpus-pairclassification.task1645_medical_question_pair_dataset_text_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1645_medical_question_pair_dataset_text_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1645_medical_question_pair_dataset_text_classification.newsph-nli-fil-pairclassification
NewsPHNLI_fil_PairClassification
Deduplicated copy of kornwtp/newsph-nli-fil-pairclassification.
Splits
split
rows
test
8,995
train
418,705
validation
89,836
squad-nli-ind-pairclassification
SQuADNLI_ind_PairClassification
Deduplicated copy of kornwtp/squad-nli-ind-pairclassification.
Splits
split
rows
test
22,190
train
217,873
validation
22,128
xnli-vie-pairclassification
XNLI_vie_PairClassification
Deduplicated copy of kornwtp/xnli-vie-pairclassification.
Splits
split
rows
test
5,010
train
392,417
validation
2,490
terra-pairclassificationxnli-khm-pairclassification
XNLI_khm_PairClassification
Deduplicated copy of kornwtp/xnli-khm-pairclassification.
Splits
split
rows
test
3,340
validation
1,660
burmese-xnli-mya-pairclassification
BurmeseXNLI_mya_PairClassification
Deduplicated copy of kornwtp/burmese-xnli-mya-pairclassification.
Splits
split
rows
test
3,338
validation
1,660
sprintduplicatequestions-pairclassification-vn
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["SprintDuplicateQuestions-VN"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repitory.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/sprintduplicatequestions-pairclassification-vn.indic-xnli-tam-pairclassification
IndicXNLI_tam_PairClassification
Deduplicated copy of kornwtp/indic-xnli-tam-pairclassification.
Splits
split
rows
test
5,007
train
392,286
validation
2,489
idnli-ind-pairclassification
IndoNLI_ind_PairClassification
Deduplicated copy of kornwtp/idnli-ind-pairclassification.
Splits
split
rows
test
1,569
train
6,913
validation
1,556
myxnli-mya-pairclassification
MyXNLI_mya_PairClassification
Deduplicated copy of kornwtp/myxnli-mya-pairclassification.
Splits
split
rows
test
5,009
train
392,437
validation
2,490
xnli-tha-pairclassification
XNLI_tha_PairClassification
Deduplicated copy of kornwtp/xnli-tha-pairclassification.
Splits
split
rows
test
5,010
train
392,482
validation
2,490
tydiqa-nli-ind-pairclassification
TyDIQANLI_ind_PairClassification
Deduplicated copy of kornwtp/tydiqa-nli-ind-pairclassification.
Splits
split
rows
test
1,575
train
8,764
validation
1,040
idkmrc-nli-ind-pairclassification
IDKMRCNLI_ind_PairClassification
Deduplicated copy of kornwtp/idkmrc-nli-ind-pairclassification.
Splits
split
rows
test
1,539
train
17,074
validation
1,403
wrete-ind-pairclassification
WReTE_ind_PairClassification
Deduplicated copy of kornwtp/wrete-ind-pairclassification.
Splits
split
rows
test
100
train
300
validation
50
xnli-zsm-pairclassification
XNLI_zsm_PairClassification
Deduplicated copy of kornwtp/xnli-zsm-pairclassification.
Splits
split
rows
test
3,339
validation
1,660
twittersemeval2015-pairclassification_CS-MTEB
TwitterSemEval2015 CS-MTEB
Code-switching version of mteb/twittersemeval2015-pairclassification, with sentence pairs rewritten in Chinese-English, Japanese-English, German-English, Spanish-English, Korean-English, French-English, Italian-English, Portuguese-English, Dutch-English code-switching styles.
Dataset Structure
From original dataset (unchanged):
default: Original test split (batched format)
Code-switching test sets (flat format):
test_zh_en: Chinese-English… See the full description on the dataset page: https://huggingface.co/datasets/UTokyo-Yokoya-Lab/twittersemeval2015-pairclassification_CS-MTEB.xnli-lao-pairclassification
XNLITranslated_lao_PairClassification
Deduplicated copy of kornwtp/xnli-lao-pairclassification.
Splits
split
rows
test
3,340
multilingual-nli-26lang-2mil7-vie-pairclassification
MultilingualNLI26lang2mil7_vie_PairClassification
Deduplicated copy of kornwtp/multilingual-nli-26lang-2mil7-vie-pairclassification.
Splits
split
rows
vi_anli
24,968
spamid-pair-ind-classification
SpamidPair_ind_Classification
Deduplicated copy of kornwtp/spamid-pair-ind-classification.
Splits
split
rows
train
1,911
synthetic-persian-chatbot-rag-faq-pair-classification
Dataset Summary
Synthetic Persian Chatbot RAG FAQ Pair Classification (SynPerChatbotRAGFAQPC) is a Persian (Farsi) dataset for the Pair Classification task, specifically designed for evaluating Retrieval-Augmented Generation (RAG) chatbot systems. It is part of the FaMTEB (Farsi Massive Text Embedding Benchmark) and was synthetically generated using GPT-4o-mini.
The dataset measures a model’s ability to assess whether a given FAQ (question–answer pair) is relevant to a new user… See the full description on the dataset page: https://huggingface.co/datasets/MCINext/synthetic-persian-chatbot-rag-faq-pair-classification.synthetic-persian-text-keyword-pair-classification
Dataset Summary
Synthetic Persian Text-Keywords Pair Classification (SynPerTextKeywordsPC) is a Persian (Farsi) dataset developed for the Pair Classification task. The dataset focuses on determining whether a keyword or short phrase is relevant to a longer Persian text passage. It is a component of the FaMTEB (Farsi Massive Text Embedding Benchmark) and was synthetically created using GPT-4o-mini.
Language(s): Persian (Farsi)
Task(s): Pair Classification (Text–Keyword Relevance)… See the full description on the dataset page: https://huggingface.co/datasets/MCINext/synthetic-persian-text-keyword-pair-classification.synthetic-persian-qa-pair-classification
Dataset Summary
Synthetic Persian QA Pair Classification (SynPerQAPC) is a Persian (Farsi) dataset created for the Pair Classification task, focusing on determining the correctness or relevance of an answer to a given question. It is part of the FaMTEB (Farsi Massive Text Embedding Benchmark). This dataset was synthetically generated using GPT-4o-mini to simulate real-world question–answering scenarios.
Language(s): Persian (Farsi)
Task(s): Pair Classification (QA Answer… See the full description on the dataset page: https://huggingface.co/datasets/MCINext/synthetic-persian-qa-pair-classification.xnli-tha-pairclassificationref: https://github.com/facebookresearch/XNLI
idkmrc-nli-ind-pairclassificationindic-xnli-tam-pairclassificationref: https://huggingface.co/datasets/Divyanshu/indicxnli
squad-nli-ind-pairclassification