Kashgari

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.

Kashgare is:

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.
Easy to fine-tune. Kashgare build-in pre-trained BERT and Word2vec embedding models, which makes it very simple to fine-tune your model based on this embeddings.
Fully scalable. Kashgare provide a simple, fast, and scalable environment for fast experimentation.

Feature List

Embedding support
- Classic word2vec embedding
- BERT embedding
- GPT-2 embedding
Sequence(Text) Classification Models
- CNNModel
- BLSTMModel
- CNNLSTMModel
- AVCNNModel
- KMaxCNNModel
- RCNNModel
- AVRNNModel
- DropoutBGRUModel
- DropoutAVRNNModel
Sequence(Text) Labeling Models (NER, PoS)
- CNNLSTMModel
- BLSTMModel
- BLSTMCRFModel
Model Training
Model Evaluate
GPU Support / Multi GPU Support
Customize Model

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

Roadmap

Migrate to tf.keras
ELMo Embedding
Pre-trained models
More model structure

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 Keras 2.2.0+ and Python 3.6+, because it is 2019 and type hints is cool.

pip install kashgari
# CPU
pip install tensorflow==1.12.0
# GPU
pip install tensorflow-gpu==1.12.0

Example Usage

lets run a text classification with CNN model over SMP 2017 ECDT Task1.

>>> from kashgari.corpus import SMP2017ECDTClassificationCorpus
>>> from kashgari.tasks.classification import CNNLSTMModel

>>> x_data, y_data = SMP2017ECDTClassificationCorpus.get_classification_data()
>>> x_data[0]
['你', '知', '道', '我', '几', '岁']
>>> y_data[0]
'chat'

# provided classification models `CNNModel`, `BLSTMModel`, `CNNLSTMModel` 
>>> classifier = CNNLSTMModel()
>>> classifier.fit(x_data, y_data)
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 10)                0         
_________________________________________________________________
embedding_1 (Embedding)      (None, 10, 100)           87500     
_________________________________________________________________
conv1d_1 (Conv1D)            (None, 10, 32)            9632      
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 5, 32)             0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 100)               53200     
_________________________________________________________________
dense_1 (Dense)              (None, 32)                3232      
=================================================================
Total params: 153,564
Trainable params: 153,564
Non-trainable params: 0
_________________________________________________________________
Epoch 1/5
 1/35 [..............................] - ETA: 32s - loss: 3.4652 - acc: 0.0469

... 

>>> x_test, y_test = SMP2017ECDTClassificationCorpus.get_classification_data('test')
>>> classifier.evaluate(x_test, y_test)
              precision    recall  f1-score   support
         
        calc       0.75      0.75      0.75         8
        chat       0.83      0.86      0.85       154
    contacts       0.54      0.70      0.61        10
    cookbook       0.97      0.94      0.95        89
    datetime       0.67      0.67      0.67         6
       email       1.00      0.88      0.93         8
         epg       0.61      0.56      0.58        36
      flight       1.00      0.90      0.95        21
...

Run with GPT-2 Embedding

from kashgari.embeddings import GPT2Embedding
from kashgari.tasks.classification import CNNLSTMModel
from kashgari.corpus import SMP2017ECDTClassificationCorpus

gpt2_embedding = GPT2Embedding('<path-to-gpt-model-folder>', sequence_length=30)                                 
model = CNNLSTMModel(gpt2_embedding)

train_x, train_y = SMP2017ECDTClassificationCorpus.get_classification_data()
model.fit(train_x, train_y)

Run with Bert Embedding

from kashgari.embeddings import BERTEmbedding
from kashgari.tasks.classification import CNNLSTMModel
from kashgari.corpus import SMP2017ECDTClassificationCorpus

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

train_x, train_y = SMP2017ECDTClassificationCorpus.get_classification_data()
model.fit(train_x, train_y)

Run with Word2vec Embedding

from kashgari.embeddings import WordEmbeddings
from kashgari.tasks.classification import CNNLSTMModel
from kashgari.corpus import SMP2017ECDTClassificationCorpus

bert_embedding = WordEmbeddings('sgns.weibo.bigram', sequence_length=30)                                  
model = CNNLSTMModel(bert_embedding)
train_x, train_y = SMP2017ECDTClassificationCorpus.get_classification_data()
model.fit(train_x, train_y)

Support for Training on Multiple GPUs

from kashgari.embeddings import BERTEmbedding
from kashgari.tasks.classification import CNNLSTMModel

train_x, train_y = prepare_your_classification_data()

# build model with embedding
bert_embedding = BERTEmbedding('bert-large-cased', sequence_length=128)
model = CNNLSTMModel(bert_embedding)

# or without pre-trained embedding
model = CNNLSTMModel()

# Build model with your corpus
model.build_model(train_x, train_y)

# Add multi gpu support
model.build_multi_gpu_model(gpus=8)

# Train, 256 / 8 = 32 samples for every GPU per batch
model.fit(train_x, train_y, batch_size=256)

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.

shahariarrabby / kashgari Goto Github PK

kashgari's Introduction

Kashgari

Feature List

Performance

Roadmap

Tutorials

Quick start

Requirements and Installation

Example Usage

Run with GPT-2 Embedding

Run with Bert Embedding

Run with Word2vec Embedding

Support for Training on Multiple GPUs

Contributing

Reference

License

kashgari's People

Contributors

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