mrebel-large
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rebel-large
BabelscapeThis is the model card for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By End-to-end Language generation. We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found here. If you use the code, please reference this work in your paper: @inproceedings{huguet-cabot-navigli-2021-rebel-relation, title = "{REBEL}: Relation Extraction By End-to-end Language generation", author = "Huguet Cabot, Pere-Llu{\'\i}s and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.204", pages = "2370--2381", abstract = "Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model{'}s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.", } Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the Spaces demo.
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詳細を見る ↗t5-base-summarization-claim-extractor
Babelscape基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 未確認→ 出力 未確認
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詳細を見る ↗wikineural-multilingual-ner
BabelscapeThis is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. The original repository for the paper can be found at https://github.com/Babelscape/wikineural. This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL).
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