Kashgari

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Overview | Performance | Quick start | Documentation | 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

Quick start

Requirements and Installation

🎉🎉🎉 We renamed again for consistency and clarity. From now on, it is all kashgari. 🎉🎉🎉

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

Backend pypi version desc
TensorFlow 2.x pip install 'kashgari>=2.0.0' coming soon
TensorFlow 1.14+ pip install 'kashgari>=1.0.0,<2.0.0' current version
Keras pip install 'kashgari<1.0.0' legacy version

Find more info about the name changing.

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)

Sponsors

Support this project by becoming a sponsor. Your issues and feature request will be prioritized.[Become a sponsor]

Contributors ✨

Thanks goes to these wonderful people. And there are many ways to get involved. Start with the contributor guidelines and then check these open issues for specific tasks.


Eliyar Eziz

📖 ⚠️ 💻

Alex Wang

💻

Yusup

💻

Adline

💻

Feel free to join the Slack group if you want to more involved in Kashgari’s development.

Slack Group Link