nner 0.1.3

Nested Named Entity Recognition

model name

xxx is our method used in CBLUE (Chinese Biomedical Language Understanding Evaluation), a benchmark of Nested Named Entity Recognition. We got the 2nd price of the benchmark by 2021/12/06.

Approach

TODO:

picture or paper

Usage

First, install PyTorch>=1.7.0. There's no restriction on GPU or CUDA.

Then, install this repo as a Python package:

$ pip install nner

API

The nner package provides the following methods:

nner.load_NNER(model_save_path='./checkpoint/macbert-large_dict.pth', maxlen=512, c_size=9, id2c=_id2c)

Returns the pretrained model. It will download the model as necessary. The model would use the first CUDA device if there's any, otherwise using CPU instead.

The model_save_path argument specifies the path of the pretrained model weight.

The maxlen argument specifies the max length of input sentences. The sentences longer than maxlen would be cut off.

The c_size argument specifies the number of entity class. Here is 9 for CBLUE.

The id2c argument specifies the mapping between id and entity class. By default, the id2c argument for CBLUE is:

_id2c = {0: 'dis', 1: 'sym', 2: 'pro', 3: 'equ', 4: 'dru', 5: 'ite', 6: 'bod', 7: 'dep', 8: 'mic'}

---

The model returned by nner.load_NNER() supports the following methods:

model.recognize(text: str, threshold=0)

Given a sentence, returns a list of tuples with recognized entity, the format of the tuple is [(start_index, end_index, entity_class), ...]. The threshold argument specifies that the returned list only contains the recognized entity with confidence score higher than threshold.

model.predict_to_file(in_file: str, out_file: str)

Given input and output .json file path, the model would do inference according in_file, and the recognized entity would be saved in out_file. The output file can be submitted to CBLUE. The format of input file is like:

[
  {
    "text": "..."
  },
  {
    "text": "..."
  },
  ...
]

Examples

import nner

NNER = nner.load_NNER()
in_file = './CMeEE_test.json'
out_file = './CMeEE_test_answer.json'
NNER.predict_to_file(in_file, out_file)