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a crf layer for tensorflow 2 keras

Project description

tf2crf

  • a simple CRF layer for tensorflow 2 keras
  • support keras masking

Install

$ pip install tf2crf

Features

  • easy to use CRF layer with tensorflow
  • support mixed precision training
  • support the ModelWithCRFLossDSCLoss with DSC loss, which increases f1 score with unbalanced data (refer the paper Dice Loss for Data-imbalanced NLP Tasks)

Attention

  • Add internal kernel like CRF in keras_contrib, so now there is no need to stack a Dense layer before the CRF layer.
  • I have changed the previous way that putting loss function and accuracy function in the CRF layer. Instead I choose to use ModelWappers (refered to jaspersjsun), which is more clean and flexible.

Tips

tensorflow >= 2.1.0 Recommmend use the latest tensorflow-addons which is compatiable with your tf version.

Example

import tensorflow as tf
from tf2CRF import CRF
from tensorflow.keras.layers import Input, Embedding, Bidirectional, GRU, Dense
from tensorflow.keras.models import Model
from tf2crf import CRF, ModelWithCRFLoss

inputs = Input(shape=(None,), dtype='int32')
output = Embedding(100, 40, trainable=True, mask_zero=True)(inputs)
output = Bidirectional(GRU(64, return_sequences=True))(output)
crf = CRF(units=9, type='float32')
output = crf(output)
base_model = Model(inputs, output)
model = ModelWithCRFLoss(base_model, sparse_target=True)
model.compile(optimizer='adam')

x = [[5, 2, 3] * 3] * 10
y = [[1, 2, 3] * 3] * 10

model.fit(x=x, y=y, epochs=2, batch_size=2)
model.save('tests/1')

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