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--- |
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datasets: |
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- snli |
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- facebook/anli |
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- multi_nli |
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- multi_nli_mismatch |
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- fever |
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pipeline_tag: zero-shot-classification |
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--- |
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# A2T Entailment model |
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**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatible with the `ZeroShotTextClassificationPipeline` from [Transformers](https://github.com/huggingface/Transformers). |
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Textual Entailment (or Natural Language Inference) has turned out to be a good choice for zero-shot text classification problems [(Yin et al., 2019](https://aclanthology.org/D19-1404/); [Wang et al., 2021](https://arxiv.org/abs/2104.14690); [Sainz and Rigau, 2021)](https://aclanthology.org/2021.gwc-1.6/). Recent research addressed Information Extraction problems with the same idea [(Lyu et al., 2021](https://aclanthology.org/2021.acl-short.42/); [Sainz et al., 2021](https://aclanthology.org/2021.emnlp-main.92/); [Sainz et al., 2022a](), [Sainz et al., 2022b)](https://arxiv.org/abs/2203.13602). The A2T entailment models are first trained with NLI datasets such as MNLI [(Williams et al., 2018)](), SNLI [(Bowman et al., 2015)]() or/and ANLI [(Nie et al., 2020)]() and then fine-tuned to specific tasks that were previously converted to textual entailment format. |
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For more information please, take a look to the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library or the following published papers: |
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- [Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (Sainz et al., EMNLP 2021)](https://aclanthology.org/2021.emnlp-main.92/) |
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- [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (Sainz et al., Findings of NAACL-HLT 2022)]() |
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## About the model |
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The model name describes the configuration used for training as follows: |
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<!-- $$\text{HiTZ/A2T\_[pretrained\_model]\_[NLI\_datasets]\_[finetune\_datasets]}$$ --> |
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<h3 align="center">HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]</h3> |
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- `pretrained_model`: The checkpoint used for initialization. For example: RoBERTa<sub>large</sub>. |
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- `NLI_datasets`: The NLI datasets used for pivot training. |
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- `S`: Standford Natural Language Inference (SNLI) dataset. |
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- `M`: Multi Natural Language Inference (MNLI) dataset. |
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- `F`: Fever-nli dataset. |
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- `A`: Adversarial Natural Language Inference (ANLI) dataset. |
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- `finetune_datasets`: The datasets used for fine tuning the entailment model. Note that for more than 1 dataset the training was performed sequentially. For example: ACE-arg. |
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Some models like `HiTZ/A2T_RoBERTa_SMFA_ACE-arg` have been trained marking some information between square brackets (`'[['` and `']]'`) like the event trigger span. Make sure you follow the same preprocessing in order to obtain the best results. |
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## Cite |
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If you use this model, consider citing the following publications: |
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```bibtex |
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@inproceedings{sainz-etal-2021-label, |
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title = "Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction", |
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author = "Sainz, Oscar and |
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Lopez de Lacalle, Oier and |
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Labaka, Gorka and |
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Barrena, Ander and |
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Agirre, Eneko", |
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booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
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month = nov, |
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year = "2021", |
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address = "Online and Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.emnlp-main.92", |
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doi = "10.18653/v1/2021.emnlp-main.92", |
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pages = "1199--1212", |
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} |
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``` |
