GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('glue', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

SeaEval/mnli dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for "mnli-amr"

More Information needed

Dataset

This dataset was created by Khương Trần

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This Zenodo repository contains 100 copies of the model BERT fine-tuned on the MNLI dataset, created for the paper "BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance." Please see the project GitHub page for more details about using these models and how to cite any such usage: https://github.com/tommccoy1/hans/tree/master/berts_of_a_feather

XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('xnli', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Dataset

This dataset was created by Udbhav Bamba

Released under CC0: Public Domain

Contents

Dataset Card for AutoTrain Evaluator

This repository contains model predictions generated by AutoTrain for the following task and dataset:

Task: Natural Language Inference Model: Jiva/xlm-roberta-large-it-mnli Dataset: glue Config: mnli Split: validation_matched

To run new evaluation jobs, visit Hugging Face's automatic model evaluator.

  Contributions

Thanks to @lewtun for evaluating this model.

Dataset

This dataset was created by Equilan

Contents

The Multi-Genre Natural Language Inference (MultiNLI) corpus is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus is modeled on the SNLI corpus, but differs in that covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation. The corpus served as the basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('multi_nli', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Dataset Description

This dataset provides easier accessibility to the original MNLI dataset. We randomly choose 10% of the original validation_matched split and use it as the validation split. The remaining 90% are used for the test split. The train split remains unchanged.

autoevaluate/autoeval-staging-eval-glue-mnli-026a6e-14686016 dataset hosted on Hugging Face and contributed by the HF Datasets community

BurgerTruck/mnli-all dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Neranjhana

Contents

OCNLI stands for Original Chinese Natural Language Inference. It is corpus for Chinese Natural Language Inference, collected following closely the procedures of MNLI, but with enhanced strategies aiming for more challenging inference pairs. No human/machine translation is used in creating the dataset, and thus the Chinese texts are original and not translated.

OCNLI has roughly 50k pairs for training, 3k for development and 3k for test. Only the test data is released but not its labels.

OCNLI is part of the CLUE benchmark.

Dataset

This dataset was created by Ahmad Zidan

Contents

llama-lang-adapt/Translated-MNLI-2 dataset hosted on Hugging Face and contributed by the HF Datasets community

OntoLAMA Datasets (anonymised access because the paper is under review)

This pre-release version is missing the complex SI dataset constructed from GO.

Dataset

This dataset was created by Sandiago

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Dataset

This dataset was created by Splend1dChan（燦爛）

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