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.
Project description
Kashgari
Overview | Performance | Quick start | Documentation | Contributing
Overview
Kashgare is simple and powerful NLP framework, build your state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks.
- Human-friendly. Kashgare's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
- Powerful and simple. 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.
- Keras based. Kashgare builds directly on Keras, making it easy to train your models and experiment with new approaches using different embeddings and model structure.
- Buildin transfer learning. Kashgare build-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
- Fully scalable. Kashgare provide a simple, fast, and scalable environment for fast experimentation.
Performance
Task | Language | Dataset | Score | Detail |
---|---|---|---|---|
Named Entity Recognition | Chinese | People's Daily Ner Corpus | 92.20 (F1) | 基于 BERT 的中文命名实体识别 |
Tutorials
Here is a set of quick tutorials to get you started with the library:
There are also articles and posts that illustrate how to use Kashgari:
Quick start
Requirements and Installation
The project is based on TenorFlow 1.14.0 and Python 3.6+, because it is 2019 and type hints is cool.
pip install kashgari-tf
# CPU
pip install tensorflow==1.14.0
# GPU
pip install tensorflow-gpu==1.14.0
Example Usage
lets run a 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)
Run with Word2vec Embedding
from kashgari.embeddings import WordEmbedding
from kashgari.tasks.labeling import BiLSTM_CRF_Model
from kashgari.corpus import ChineseDailyNerCorpus
bert_embedding = WordEmbedding('<Gensim embedding file>', sequence_length=30)
model = BiLSTM_CRF_Model(bert_embedding)
train_x, train_y = ChineseDailyNerCorpus.load_data()
model.fit(train_x, train_y)
Support for Training on Multiple GPUs
from kashgari.tasks.labeling import BiGRU_Model
from kashgari.corpus import ChineseDailyNerCorpus
model = BiGRU_Model()
train_x, train_y = ChineseDailyNerCorpus.load_data()
model.build_multi_gpu_model(gpus=2,
cpu_merge=False,
cpu_relocation=False,
x_train=train_x,
y_train=train_y)
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.
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