Oscar Sainz commited on
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First model version

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README.md CHANGED
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- license: cc-by-4.0
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ pipeline_tag: zero-shot-classification
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+ datasets:
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+ - snli
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+ - anli
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+ - multi_nli
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+ - multi_nli_mismatch
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+ - fever
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  ---
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+
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+ # A2T Entailment model
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+
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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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+
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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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+
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+ For more information please, take a look to the [Ask2Transformers]() library or the following published papers:
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+
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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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+
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+ ## About the model
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+
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+ The model name describes the configuration used for training as follows:
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+
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+ <!-- $$\text{HiTZ/A2T\_[pretrained\_model]\_[NLI\_datasets]\_[finetune\_datasets]}$$ -->
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+
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+ <h3 align="center">HiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]</h3>
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+
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+
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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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+
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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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+
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+ ## Cite
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+
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+ If you use this model, consider citing the following publications:
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+
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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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+ ```
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+ "architectures": [
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+ "num_hidden_layers": 24,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.9.0.dev0",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 50266
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+ }
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