kashgari-tf 0.5.2

Simple, Keras-powered multilingual NLP framework, allows you to build your models in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks. Includes BERT, GPT-2 and word2vec embedding.

Kashgari

Overview | Performance | Quick start | Documentation | 中文文档 | Contributing

🎉🎉🎉 We are proud to announce that we entirely rewrote Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! 🎉🎉🎉

Overview

Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.

• Human-friendly. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
• Powerful and simple. Kashgari 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.
• Built-in transfer learning. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
• Fully scalable. Kashgari provides a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure.
• Production Ready. Kashgari could export model with SavedModel format for tensorflow serving, you could directly deploy it on the cloud.

Our Goal

• Academic users Easier experimentation to prove their hypothesis without coding from scratch.
• NLP beginners Learn how to build an NLP project with production level code quality.
• NLP developers Build a production level classification/labeling model within minutes.

Performance

Task	Language	Dataset	Score	Detail
Named Entity Recognition	Chinese	People's Daily Ner Corpus	94.46 (F1)	Text Labeling Performance Report

Tutorials

Here is a set of quick tutorials to get you started with the library:

• Tutorial 1: Text Classification
• Tutorial 2: Text Labeling
• Tutorial 3: Language Embedding

There are also articles and posts that illustrate how to use Kashgari:

• 15 分钟搭建中文文本分类模型
• 基于 BERT 的中文命名实体识别（NER)
• BERT/ERNIE 文本分类和部署
• 五分钟搭建一个基于BERT的NER模型
• Multi-Class Text Classification with Kashgari in 15 minutes

Quick start

Requirements and Installation

🎉🎉🎉 We renamed the tf.keras version as kashgari-tf 🎉🎉🎉

The project is based on TensorFlow 1.14.0 and Python 3.6+, because it is 2019 and type hinting is cool.

pip install kashgari-tf
# CPU
pip install tensorflow==1.14.0
# GPU
pip install tensorflow-gpu==1.14.0

Example Usage

Let's run an NER labeling model with Bi_LSTM Model.

from kashgari.corpus import ChineseDailyNerCorpus
from kashgari.tasks.labeling import BiLSTM_Model

train_x, train_y = ChineseDailyNerCorpus.load_data('train')
test_x, test_y = ChineseDailyNerCorpus.load_data('test')
valid_x, valid_y = ChineseDailyNerCorpus.load_data('valid')

model = BiLSTM_Model()
model.fit(train_x, train_y, valid_x, valid_y, epochs=50)

"""
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
input (InputLayer)           (None, 97)                0
_________________________________________________________________
layer_embedding (Embedding)  (None, 97, 100)           320600
_________________________________________________________________
layer_blstm (Bidirectional)  (None, 97, 256)           235520
_________________________________________________________________
layer_dropout (Dropout)      (None, 97, 256)           0
_________________________________________________________________
layer_time_distributed (Time (None, 97, 8)             2056
_________________________________________________________________
activation_7 (Activation)    (None, 97, 8)             0
=================================================================
Total params: 558,176
Trainable params: 558,176
Non-trainable params: 0
_________________________________________________________________
Train on 20864 samples, validate on 2318 samples
Epoch 1/50
20864/20864 [==============================] - 9s 417us/sample - loss: 0.2508 - acc: 0.9333 - val_loss: 0.1240 - val_acc: 0.9607

"""

Run with GPT-2 Embedding

from kashgari.embeddings import GPT2Embedding
from kashgari.corpus import ChineseDailyNerCorpus
from kashgari.tasks.labeling import BiGRU_Model

train_x, train_y = ChineseDailyNerCorpus.load_data('train')
valid_x, valid_y = ChineseDailyNerCorpus.load_data('valid')

gpt2_embedding = GPT2Embedding('<path-to-gpt-model-folder>', sequence_length=30)
model = BiGRU_Model(gpt2_embedding)
model.fit(train_x, train_y, valid_x, valid_y, epochs=50)

Run with Bert Embedding

from kashgari.embeddings import BERTEmbedding
from kashgari.tasks.labeling import BiGRU_Model
from kashgari.corpus import ChineseDailyNerCorpus

bert_embedding = BERTEmbedding('<bert-model-folder>', sequence_length=30)
model = BiGRU_Model(bert_embedding)

train_x, train_y = ChineseDailyNerCorpus.load_data()
model.fit(train_x, train_y)

Contributing

Thanks for your interest in contributing! There are many ways to get involved; start with the contributor guidelines and then check these open issues for specific tasks.

Reference

This library is inspired by and references following frameworks and papers.

• flair - A very simple framework for state-of-the-art Natural Language Processing (NLP)
• anago - Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging
• Chinese-Word-Vectors