🎉🎉🎉 We are proud to announce that we entirely rewritten Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! 🎉🎉🎉
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 provide 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
SavedModelformat for tensorflow serving, you could directly deploy it on cloud.
- 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.
| Task | Language | Dataset | Score | Detail |
|---|---|---|---|---|
| Named Entity Recognition | Chinese | People's Daily Ner Corpus | 94.46 (F1) | Text Labeling Performance Report |
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:
- 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
🎉🎉🎉 We renamed the tf.keras version as kashgari-tf 🎉🎉🎉
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==1.14.0lets 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
"""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)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)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.
This library is inspired by and references following frameworks and papers.