tonytan48/Re-DocRED
Re-DocRED Dataset This repository contains the dataset of our EMNLP 2022 research paper Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction. DocRED is a widely used benchmark for document-level relation extraction. However, the DocRED dataset contains a significant percentage of false negative examples (incomplete annotation). We revised 4,053 documents in the DocRED dataset and resolved its problems. We released this dataset as: Re-DocRED dataset.… See the full description on the dataset page: https://huggingface.co/datasets/tonytan48/Re-DocRED.
| Author / organization | tonytan48 |
|---|---|
| License (as declared) | mit |
| Downloads | 471 (as reported by Hugging Face) |
| Likes | 5 |
| Created | 25 November 2022 |
| Last updated at source | 25 November 2022 |
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Connected in the knowledge graph
- vllm-sr/Vela-2.0-9BModel · used to train
- vllm-sr/Vela-2.0-4BModel · used to train
- vllm-sr/Vela-2.0-0.8BModel · used to train
Recent changes
- 17 Oct 2026 Description: Re-DocRED Dataset This repository contains the dataset of our EMNLP 2022 research paper Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction. DocRED is a widely used benchmark for document-level relation extracti → Re-DocRED Dataset This repository contains the dataset of our EMNLP 2022 research paper Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction. DocRED is a widely used benchmark for document-level relation extracti
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