Kashgari

Simple and powerful NLP framework, build your own state-of-art model in 5 minutes.

Kashgare is:

Human-friendly framework. Kashgare's code is very simple, well documented and tested, which makes it very easy to understand and modify.
Powerful and simple NLP library. Kashgare allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification.
A Keras NLP framework. Kashgare builds directly on Keras, making it easy to train your own models and experiment with new approaches using different embeddings and model structure.

Feature List

Embedding support
- Classic word2vec embedding
- BERT embedding
Text Classification Models
- CNN Classification Model
- CNN LSTM Classification Model
- Bidirectional LSTM Classification Model
Text Labeling Models (NER, PoS)
- Bidirectional LSTM Labeling Model
- Bidirectional LSTM CRF Labeling Model
- CNN LSTM Labeling Model
Model Training
Model Evaluate
GPU Support
Customize Model

Roadmap

ELMo Embedding
Pre-trained models
More model structure

Tutorials

Chinese

15分钟搭建中文文本分类模型

Quick start

Requirements and Installation

The project is based on Keras 2.2.0+ and Python 3.6+, because it is 2019 and type hints is cool.

pip install kashgari
# CPU
pip install tensorflow
# GPU
pip install tensorflow-gpu

Example Usage

lets run a text classification with CNN model over SMP 2017 ECDT Task1.

>>> from kashgari.corpus import SMP2017ECDTClassificationCorpus
>>> from kashgari.tasks.classification import CNNLSTMModel

>>> x_data, y_data = SMP2017ECDTClassificationCorpus.get_classification_data()
>>> x_data[0]
['你', '知', '道', '我', '几', '岁']
>>> y_data[0]
'chat'

# provided classification models `CNNModel`, `BLSTMModel`, `CNNLSTMModel` 
>>> classifier = CNNLSTMModel()
>>> classifier.fit(x_data, y_data)
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 10)                0         
_________________________________________________________________
embedding_1 (Embedding)      (None, 10, 100)           87500     
_________________________________________________________________
conv1d_1 (Conv1D)            (None, 10, 32)            9632      
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 5, 32)             0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 100)               53200     
_________________________________________________________________
dense_1 (Dense)              (None, 32)                3232      
=================================================================
Total params: 153,564
Trainable params: 153,564
Non-trainable params: 0
_________________________________________________________________
Epoch 1/5
 1/35 [..............................] - ETA: 32s - loss: 3.4652 - acc: 0.0469

... 

>>> x_test, y_test = SMP2017ECDTClassificationCorpus.get_classification_data('test')
>>> classifier.evaluate(x_test, y_test)
              precision    recall  f1-score   support
         
        calc       0.75      0.75      0.75         8
        chat       0.83      0.86      0.85       154
    contacts       0.54      0.70      0.61        10
    cookbook       0.97      0.94      0.95        89
    datetime       0.67      0.67      0.67         6
       email       1.00      0.88      0.93         8
         epg       0.61      0.56      0.58        36
      flight       1.00      0.90      0.95        21
...

Run with Bert Embedding

from kashgari.embeddings import BERTEmbedding
from kashgari.tasks.classification import CNNLSTMModel
from kashgari.corpus import SMP2017ECDTClassificationCorpus

bert_embedding = BERTEmbedding('bert-base-chinese', sequence_length=30)                                   
model = CNNLSTMModel(bert_embedding)

train_x, train_y = SMP2017ECDTClassificationCorpus.get_classification_data()
model.fit(train_x, train_y)

Run with Word2vec embedded

from kashgari.embeddings import WordEmbeddings
from kashgari.tasks.classification import CNNLSTMModel
from kashgari.corpus import SMP2017ECDTClassificationCorpus

bert_embedding = WordEmbeddings('sgns.weibo.bigram', sequence_length=30)                                  
model = CNNLSTMModel(bert_embedding)
train_x, train_y = SMP2017ECDTClassificationCorpus.get_classification_data()
model.fit(train_x, train_y)

Reference

This library is inspired and reference following framework and papers.

epirus / Kashgari