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align=\"center\"\u003eAsk2Transformers\u003c/h1\u003e\n\u003ch3 align=\"center\"\u003eA Framework for Textual Entailment based Zero Shot text classification\u003c/h3\u003e\n\u003cp align=\"center\"\u003e\n \u003ca href=\"https://paperswithcode.com/sota/domain-labelling-on-babeldomains?p=ask2transformers-zero-shot-domain-labelling\"\u003e\n  \u003cimg align=\"center\" alt=\"Contributor Covenant\" src=\"https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ask2transformers-zero-shot-domain-labelling/domain-labelling-on-babeldomains\"\u003e\n \u003c/a\u003e\n\u003c/p\u003e\n\nThis repository contains the code for out of the box ready to use zero-shot classifiers among different tasks, such as Topic Labelling or Relation Extraction. It is built on top of 🤗 HuggingFace [Transformers](https://github.com/huggingface/transformers) library, so you are free to choose among hundreds of models. You can either, use a dataset specific classifier or define one yourself with just labels descriptions or templates! The repository contains the code for the following publications:\n\n- 📄 [Ask2Transformers - Zero Shot Domain Labelling with Pretrained Transformers](https://aclanthology.org/2021.gwc-1.6/) accepted in [GWC2021](http://globalwordnet.org/global-wordnet-conferences-2/).\n- 📄 [Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction](https://aclanthology.org/2021.emnlp-main.92/) accepted in [EMNLP2021](https://2021.emnlp.org/)\n- 📄 [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning](https://arxiv.org/abs/2205.01376) accepted as Findings in [NAACL2022](https://2022.naacl.org/)\n\n\u003c!-- ### Supported (and benchmarked) tasks:\nFollow the links to see some examples of how to use the library on each task.\n- [Topic classification](./a2t/topic_classification/) evaluated on BabelDomains (Camacho-\nCollados and Navigli, 2017)  dataset.\n- [Relation classification](./a2t/relation_classification/) evaluated on TACRED (Zhang et al., 2017) dataset. --\u003e\n\nTo get started with the repository consider reading the **new** [documentation](https://osainz59.github.io/Ask2Transformers)!\n\n# Demo 🕹️\n\nWe have realeased a demo on Zero-Shot Information Extraction using Textual Entailment ([ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations](https://arxiv.org/abs/2203.13602)) accepted in the [Demo Track of NAACL 2022](). The code is publicly availabe on its own GitHub repository: [ZS4IE](https://github.com/bbn-e/zs4ie).\n\n# Installation\n\nBy using Pip (check the last release)\n\n```shell script\npip install a2t\n```\n\nBy clonning the repository\n\n```shell script\ngit clone https://github.com/osainz59/Ask2Transformers.git\ncd Ask2Transformers\npip install .\n```\n\nOr directly by\n```shell script\npip install git+https://github.com/osainz59/Ask2Transformers\n```\n\n\u003c!-- [//]: \u003cimg src=\"./imgs/RE_NLI.svg\" style=\"background-color: white; border-radius: 15px\"\u003e --\u003e\n\n# Models \n## Available models\nBy default, `roberta-large-mnli` checkpoint is used to perform the inference. You can try different models to perform the zero-shot classification, but they need to be finetuned on a NLI task and be compatible with the `AutoModelForSequenceClassification` class from Transformers. For example:\n\n* `roberta-large-mnli`\n* `joeddav/xlm-roberta-large-xnli`\n* `facebook/bart-large-mnli`\n* `microsoft/deberta-v2-xlarge-mnli` \n\n**Coming soon:** `t5-large` like generative models support.\n\n## Pre-trained models 🆕\n\nWe now provide (task specific) pre-trained entailment models to: (1) **reproduce** the results of the papers and (2) **reuse** them for new schemas of the same tasks. The models are publicly available on the 🤗 HuggingFace Models Hub.\n\nThe model name describes the configuration used for training as follows:\n\n\u003c!-- $$\\text{HiTZ/A2T\\_[pretrained\\_model]\\_[NLI\\_datasets]\\_[finetune\\_datasets]}$$ --\u003e\n\n\u003ch3 align=\"center\"\u003eHiTZ/A2T_[pretrained_model]_[NLI_datasets]_[finetune_datasets]\u003c/h3\u003e\n\n\n- `pretrained_model`: The checkpoint used for initialization. For example: RoBERTa\u003csub\u003elarge\u003c/sub\u003e.\n- `NLI_datasets`: The NLI datasets used for pivot training.\n    - `S`: Standford Natural Language Inference (SNLI) dataset.\n    - `M`: Multi Natural Language Inference (MNLI) dataset.\n    - `F`: Fever-nli dataset.\n    - `A`: Adversarial Natural Language Inference (ANLI) dataset.\n- `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.\n\nSome 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.\n\n## Training your own models\nThere is no special script for fine-tuning your own entailment based models. In our experiments, we have used the publicly available [run_glue.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) python script (from HuggingFace Transformers). To train your own model, first, you will need to convert your actual dataset in some sort of NLI data, we recommend you to have a look to [tacred2mnli.py](https://github.com/osainz59/Ask2Transformers/blob/master/scripts/tacred2mnli.py) script that serves as an example.\n\n# Tutorials (Notebooks)\n\n**Coming soon!**\n\n# Results and evaluation\n\nTo obtain the results reported in the papers run the [`evaluation.py`](./a2t/evaluation.py) script with the corresponding configuration [files](./resources/predefined_configs/). A configuration file containing the task and evaluation information should look like this:\n\n```json\n{\n    \"name\": \"BabelDomains\",\n    \"task_name\": \"topic-classification\",\n    \"features_class\": \"a2t.tasks.text_classification.TopicClassificationFeatures\",\n    \"hypothesis_template\": \"The domain of the sentence is about {label}.\",\n    \"nli_models\": [\n        \"roberta-large-mnli\"\n    ],\n    \"labels\": [\n        \"Animals\",\n        \"Art, architecture, and archaeology\",\n        \"Biology\",\n        \"Business, economics, and finance\",\n        \"Chemistry and mineralogy\",\n        \"Computing\",\n        \"Culture and society\",\n        ...\n        \"Royalty and nobility\",\n        \"Sport and recreation\",\n        \"Textile and clothing\",\n        \"Transport and travel\",\n        \"Warfare and defense\"\n    ],\n    \"preprocess_labels\": true,\n    \"dataset\": \"babeldomains\",\n    \"test_path\": \"data/babeldomains.domain.gloss.tsv\",\n    \"use_cuda\": true,\n    \"half\": true\n}\n```\n\nConsider reading the papers to access the results.\n\n# About legacy code\n\nThe old code of this repository has been moved to [`a2t.legacy`](./a2t/legacy/) module and is only intended to be use for experimental reproducibility. Please, consider moving to the new code. If you need help read the new [documentation](https://osainz59.github.io/Ask2Transformers) or post an Issue on GitHub.\n\n# Citation\nIf you use this work, please consider citing at least one of the following papers. You can find the bibtex files in their corresponding [aclanthology](https://aclanthology.org/) page.\n\n\u003e Oscar Sainz, Haoling Qiu, Oier Lopez de Lacalle, Eneko Agirre, and Bonan Min. 2022. [ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations](https://aclanthology.org/2022.naacl-demo.4/). In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations, pages 27–38, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.\n\n\u003e Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min, and Eneko Agirre. 2022. [Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning](https://aclanthology.org/2022.findings-naacl.187/). In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2439–2455, Seattle, United States. Association for Computational Linguistics.\n\n\u003e Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, and Eneko Agirre. 2021. [Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction](https://aclanthology.org/2021.emnlp-main.92/). In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1199–1212, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.\n\n\u003e Oscar Sainz and German Rigau. 2021. [Ask2Transformers: Zero-Shot Domain labelling with Pretrained Language Models](https://aclanthology.org/2021.gwc-1.6/). In Proceedings of the 11th Global Wordnet Conference, pages 44–52, University of South Africa (UNISA). Global Wordnet Association.\n\n\u003c!--\n```bibtex\n@inproceedings{sainz-etal-2022-textual,\n  doi = {10.48550/ARXIV.2205.01376},\n  url = {https://arxiv.org/abs/2205.01376},\n  author = {Sainz, Oscar and Gonzalez-Dios, Itziar and de Lacalle, Oier Lopez and Min, Bonan and Agirre, Eneko},\n  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, \n  title = {Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution Share Alike 4.0 International}\n}\n\n```\n\nCite this paper if you want to cite stuff related to Relation Extraction, etc.\n```bibtex\n@inproceedings{sainz-etal-2021-label,\n    title = \"Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction\",\n    author = \"Sainz, Oscar  and\n      Lopez de Lacalle, Oier  and\n      Labaka, Gorka  and\n      Barrena, Ander  and\n      Agirre, Eneko\",\n    booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2021\",\n    address = \"Online and Punta Cana, Dominican Republic\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.emnlp-main.92\",\n    pages = \"1199--1212\",\n    abstract = \"Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained). In our experiments on TACRED we attain 63{\\%} F1 zero-shot, 69{\\%} with 16 examples per relation (17{\\%} points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data). We also show that the performance can be improved significantly with larger entailment models, up to 12 points in zero-shot, allowing to report the best results to date on TACRED when fully trained. The analysis shows that our few-shot systems are specially effective when discriminating between relations, and that the performance difference in low data regimes comes mainly from identifying no-relation cases.\",\n}\n``` \n\nCite this paper if you want to cite stuff related with topic labelling (A2TDomains or our paper results).\n```bibtex\n@inproceedings{sainz-rigau-2021-ask2transformers,\n    title = \"{A}sk2{T}ransformers: Zero-Shot Domain labelling with Pretrained Language Models\",\n    author = \"Sainz, Oscar  and\n      Rigau, German\",\n    booktitle = \"Proceedings of the 11th Global Wordnet Conference\",\n    month = jan,\n    year = \"2021\",\n    address = \"University of South Africa (UNISA)\",\n    publisher = \"Global Wordnet Association\",\n    url = \"https://www.aclweb.org/anthology/2021.gwc-1.6\",\n    pages = \"44--52\",\n    abstract = \"In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels. We exploit the knowledge encoded within different off-the-shelf pre-trained Language Models and task formulations to infer the domain label of a particular WordNet definition. The proposed zero-shot system achieves a new state-of-the-art on the English dataset used in the evaluation.\",\n}\n```\n--\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fosainz59%2Fask2transformers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fosainz59%2Fask2transformers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fosainz59%2Fask2transformers/lists"}