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AIモデルライブラリ/Babelscape/rebel-large
モデル情報

rebel-large

Babelscape · rebel-large

This 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.

翻訳待ちのため原文を表示しています。

license:cc-by-nc-sa-4.0
コンテキスト長非公開
入力 → 出力待核验 → 待核验
モデルウェイト未確認
情報更新日時2026/11/2 19:15:59
OVERVIEW

モデル紹介

This 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.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

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INDEPENDENT BENCHMARKS

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よくある質問

オープンウェイトなら無料で商用利用できますか？

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出典と追跡記録

huggingface · Babelscape/rebel-large ↗2026/10/29 19:23:17
huggingface · enrich:6e71258e-62ab-4e7d-9a32-fb8d0fd999e9 ↗2026/10/30 07:07:02

许可证：cc-by-nc-sa-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。