#Relation Extraction

This open-source project implements relation extraction models using PyTorch, based on BERT and its variants, ALBERT, and BioBERT. Drawing from the 'Matching the Blanks' methodology, the project supports pre-training and fine-tuning for datasets such as CNN and SemEval2010 Task 8. Utilizing Spacy for entity recognition, it provides inference capabilities to predict relationships in annotated text. While unofficial, this project aligns with the referenced paper for effective relationship classification, with benchmark results from FewRel and SemEval2010 Task 8.

The REBEL project converts Relation Extraction into a seq2seq task, using BART-based autoregressive models for efficient extraction of relation triplets. By turning these triplets into linear sequences, it overcomes traditional pipeline limitations, and supports over 200 relation types. Integration possibilities with platforms like Hugging Face and spaCy enable easy adoption in various applications, achieving top performance in multiple benchmarks. The recent addition of mREBEL enhances multilingual extraction capabilities, covering a wide range of language datasets.