--- pipeline_tag: zero-shot-classification datasets: - snli - anli - multi_nli - multi_nli_mismatch - fever --- # A2T Entailment model **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). 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. For more information please, take a look to the [Ask2Transformers]() library or the following published papers: - [Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (Sainz et al., EMNLP 2021)](https://aclanthology.org/2021.emnlp-main.92/) - [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (Sainz et al., Findings of NAACL-HLT 2022)]() ## About the model The model name describes the configuration used for training as follows:

HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]

- `pretrained_model`: The checkpoint used for initialization. For example: RoBERTalarge. - `NLI_datasets`: The NLI datasets used for pivot training. - `S`: Standford Natural Language Inference (SNLI) dataset. - `M`: Multi Natural Language Inference (MNLI) dataset. - `F`: Fever-nli dataset. - `A`: Adversarial Natural Language Inference (ANLI) dataset. - `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. 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. ## Cite If you use this model, consider citing the following publications: ```bibtex @inproceedings{sainz-etal-2021-label, title = "Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction", author = "Sainz, Oscar and Lopez de Lacalle, Oier and Labaka, Gorka and Barrena, Ander and Agirre, Eneko", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.92", doi = "10.18653/v1/2021.emnlp-main.92", pages = "1199--1212", } ```