self-attentive-parser

self-attentive-parser

High-accuracy NLP parser with models for 11 languages.

A high-accuracy parser with models for 11 languages, implemented in Python. Based on Constituency Parsing with a Self-Attentive Encoder from ACL 2018, with additional changes described in Multilingual Constituency Parsing with Self-Attention and Pre-Training.

New February 2021: Version 0.2.0 of the Berkeley Neural Parser is now out, with higher-quality pre-trained models for all languages. Inference now uses PyTorch instead of TensorFlow (training has always been PyTorch-only). Drops support for Python 2.7 and 3.5. Includes updated support for training and using your own parsers, based on your choice of pre-trained model.