namaco

namaco is a library for character-based Named Entity Recognition.
namaco will especially focus on Japanese and Chinese named entity recognition.

Demo

The following demo shows Chinese Named Entity Recognition:

Feature Support

namaco would provide following features:

• learning model by your data.
• tagging sentences by learned model.

Install

To install namaco, simply run:

$ pip install namaco

Data format

The data must be in the following format(tsv):

安    B-PERSON
倍    E-PERSON
首    O
相    O
が    O
訪    O
米    S-LOC
し    O
た    O
本    B-DATE
日    E-DATE

Get Started

Import

First, import the necessary modules:

import os
import namaco
from namaco.data.reader import load_data_and_labels
from namaco.data.preprocess import prepare_preprocessor
from namaco.config import ModelConfig, TrainingConfig
from namaco.models import CharNER

They include loading modules, a preprocessor and configs.

Then, set parameters to use later:

DATA_ROOT = 'data/ja/ner'
SAVE_ROOT = './models'  # trained model
LOG_ROOT = './logs'     # checkpoint, tensorboard
model_file = os.path.join(SAVE_ROOT, 'model.h5')
model_config = ModelConfig()
training_config = TrainingConfig()

Loading data

After importing the modules, read data for training and validation:

train_path = os.path.join(DATA_ROOT, 'train.txt')
valid_path = os.path.join(DATA_ROOT, 'valid.txt')
x_train, y_train = load_data_and_labels(train_path)
x_valid, y_valid = load_data_and_labels(valid_path)

After reading the data, prepare preprocessor and model:

p = prepare_preprocessor(x_train, y_train)
model = CharNER(model_config, p.vocab_size(), p.tag_size())

Now we are ready for training :)

Training a model

Let’s train a model. For training a model, we can use Trainer.
Trainer manages everything about training.
Prepare an instance of Trainer class and give train data and valid data to train method:

trainer = namaco.Trainer(model,
                         model.loss,
                         training_config,
                         log_dir=LOG_ROOT,
                         save_path=model_file,
                         preprocessor=p)
trainer.train(x_train, y_train, x_valid, y_valid)

If training is progressing normally, progress bar would be displayed as follows:

...
Epoch 3/15
702/703 [============================>.] - ETA: 0s - loss: 60.0129 - f1: 89.70
703/703 [==============================] - 319s - loss: 59.9278   
Epoch 4/15
702/703 [============================>.] - ETA: 0s - loss: 59.9268 - f1: 90.03
703/703 [==============================] - 324s - loss: 59.8417   
Epoch 5/15
702/703 [============================>.] - ETA: 0s - loss: 58.9831 - f1: 90.67
703/703 [==============================] - 297s - loss: 58.8993   
...

Tagging a sentence

We can use Tagger for tagging text.
Prepare an instance of Tagger class and give text to tag method:

tagger = namaco.Tagger(model_file, preprocessor=p, tokenizer=list)

Let’s try to tag a sentence, 安倍首相が訪米した
We can do it as follows:

>>> sent = '安倍首相が訪米した'
>>> tagger.analyze(sent)
{
  "language": "jp",
  "text": "安倍首相が訪米した",
  "entities": [
    {
      "text": "安倍",
      "type": "Person",
      "score": 0.972231
      "beginOffset": 0,
      "endOffset": 2,
    },
    {
      "text": "米",
      "type": "Location",
      "score": 0.941431
      "beginOffset": 6,
      "endOffset": 7,
    }
  ]
}