anaGo

anaGo is a Keras implementation of sequence labeling.

anaGo can perform Named Entity Recognition (NER), Part-of-Speech tagging (POS tagging), semantic role labeling (SRL) and so on for many languages. For example, the following picture shows Named Entity Recognition in English:

The following picture shows Named Entity Recognition in Japanese:

Similarly, you can solve your task (NER, POS,...) for your language. You don't have to define features. You have only to prepare input and output data. :)

anaGo Support Features

anaGo supports following features:

training the model without any features.
defining the custom model.
downloading pre-trained models.

Install

To install anaGo, simply run:

$ pip install anago

or install from the repository:

$ git clone https://github.com/Hironsan/anago.git
$ cd anago
$ pip install -r requirements.txt

Data and Word Vectors

Training data takes a tsv format. The following text is an example of training data:

EU	B-ORG
rejects	O
German	B-MISC
call	O
to	O
boycott	O
British	B-MISC
lamb	O
.	O

Peter	B-PER
Blackburn	I-PER

anaGo supports pre-trained word embeddings like GloVe vectors.

Get Started

Import

First, import the necessary modules:

import anago
from anago.reader import load_data_and_labels

Loading data

After importing the modules, load training, validation and test dataset:

x_train, y_train = load_data_and_labels('train.txt')
x_valid, y_valid = load_data_and_labels('valid.txt')
x_test, y_test = load_data_and_labels('test.txt')

Now we are ready for training :)

Training a model

Let's train a model. To train a model, call train method:

model = anago.Sequence()
model.train(x_train, y_train, x_valid, y_valid)

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

...
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   
...

Evaluating a model

To evaluate the trained model, call eval method:

model.eval(x_test, y_test)

After evaluation, F1 value is output:

- f1: 90.67

Tagging a sentence

Let's try tagging a sentence, "President Obama is speaking at the White House." To tag a sentence, call analyze method:

>>> words = 'President Obama is speaking at the White House.'.split()
>>> model.analyze(words)
{
    "words": [
        "President",
        "Obama",
        "is",
        "speaking",
        "at",
        "the",
        "White",
        "House."
    ],
    "entities": [
        {
            "beginOffset": 1,
            "endOffset": 2,
            "score": 1,
            "text": "Obama",
            "type": "PER"
        },
        {
            "beginOffset": 6,
            "endOffset": 8,
            "score": 1,
            "text": "White House.",
            "type": "ORG"
        }
    ]
}

Downloading pre-trained models

To download a pre-trained model, call download function:

from anago.utils import download

dir_path = 'models'
url = 'https://storage.googleapis.com/chakki/datasets/public/models.zip'
download(url, dir_path)
model = anago.Sequence.load(dir_path)

Reference

This library uses bidirectional LSTM + CRF model based on Neural Architectures for Named Entity Recognition by Lample, Guillaume, et al., NAACL 2016.

huandalu/anago