/bert-ner

This is a named entity recognizer based on pytorch-pretrained-bert.

Primary LanguagePythonMIT LicenseMIT

BERT-NER

This is a named entity recognizer based on pytorch-pretrained-bert.

Requirements

  • python 3.5+
  • pytorch 0.4.1
  • pytorch-pretrained-bert 0.6.1
  • tqdm
  • PyYAML

Overview

  • njuner
    • A NER package, more details in section NJUNER.
  • preprocess_msra.py
    • A tool for preprocessing the MSRA NER dataset.
  • preprocess_pd98.py
  • run_ner.py
    • A tool for training and testing bert-ner model on different datasets.
    • Working with task_config.yaml.

NJUNER

A NER tool which recognizes predefined entities like PERSONs, LOCATIONs and ORGANIZATIONs in texts. It is completely end-2-end and does not require word segmentation or part-of-speech information.

Installation

pip install njuner

Usage

  • As a package

    from njuner import NJUNER
ner = NJUNER(model_dir=model_path)
ner.label(['李雷和韩梅梅去上海迪斯尼乐园。'])
# [[('B-PER', '李'), ('I-PER', '雷'), ('O', '和'), ('B-PER', '韩'), ('I-PER', '梅'), ('I-PER', '梅'), ('O', '去'), ('B-ORG', '上'), ('I-ORG', '海'), ('I-ORG', '迪'), ('I-ORG', '斯'), ('I-ORG', '尼'), ('I-ORG', '乐'), ('I-ORG', '园'), ('O', '。') ]]

  • As a command line tool

    • Manual 
      njuner -h
    • An example 
      njuner --model_dir model_path --input_file input.txt --output_dir ./
      This will produce there files, which are "tokens.txt", "predictions.txt" and "summary.txt", in the output directory.

  • Pretrained model

    You can get several pretrained models from the NJUNER releases page. Uncompress the model archive and pass the directory to the parameter "model_dir".

Performance

Metrics: Span-based F1

  • Training and testing on the corresponding dataset.

    Item MSRA Weibo-NE Resume CoNLL-2003
    Baseline 93.18 55.28 94.46 92.4
    NJUNER 95.02 66.95 95.62 91.7
    • Baselines of MSRA, Weibo-NE and Resume are reported in the paper Chinese NER Using Lattice LSTM, which are SOTA.
    • The baseline of CoNLL-2003 is reported in the original paper BERT, which we find is hard to reproduce.

  • Comparison of different Chinese NER tools.

    Item MSRA Weibo-NE
    HanLP 72.65 38.66
    LTP 73.34 43.97
    NJUNER 81.58 63.08
    • The NER modules of HanLP and LTP are both trained on the People's Daily 1998.1 dataset. For comparison, NJUNER is also trained on the same dataset and achieve the above results on MSRA and Weibo-NE respectively. Also, their target entity types are same with our tool's, which are "PER", "LOC" and "ORG".
    • There is another entity type "GPE" in Weibo-NE dataset. For comparison, we uniformly refer to "GPE" as "LOC".