readme: ---
  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](https://github.com/osainz59/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:
  
  <!-- $$\text{HiTZ/A2T\_[pretrained\_model]\_[NLI\_datasets]\_[finetune\_datasets]}$$ -->
  
  <h3 align="center">HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]</h3>
  
  
  - `pretrained_model`: The checkpoint used for initialization. For example: RoBERTa<sub>large</sub>.
  - `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",
  }
  ```