๐๐๐ We are proud to announce that we entirely rewrote Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! ๐๐๐
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
SavedModelformat for tensorflow serving, you could directly deploy it on the 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:
- Tutorial 1: Text Classification
- Tutorial 2: Text Labeling
- Tutorial 3: Text Scoring
- Tutorial 4: Language Embedding
There are also articles and posts that illustrate how to use Kashgari:
- 15 ๅ้ๆญๅปบไธญๆๆๆฌๅ็ฑปๆจกๅ
- ๅบไบ BERT ็ไธญๆๅฝๅๅฎไฝ่ฏๅซ๏ผNER)
- BERT/ERNIE ๆๆฌๅ็ฑปๅ้จ็ฝฒ
- ไบๅ้ๆญๅปบไธไธชๅบไบBERT็NERๆจกๅ
- Multi-Class Text Classification with Kashgari in 15 minutes
๐๐๐ We renamed again for consistency and clarity. From now on, it is all kashgari. ๐๐๐
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.
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
"""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)Support this project by becoming a sponsor. Your issues and feature request will be prioritized.[Become a sponsor]
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 ๐ |
 Alex Wang ๐ป |
 Yusup ๐ป |
Feel free to join the Slack group if you want to more involved in Kashgari's development.
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
This project follows the all-contributors specification. Contributions of any kind welcome!
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