Metadata-Version: 2.1
Name: tf2crf
Version: 0.1.28
Summary: a crf layer for tensorflow 2 keras
Home-page: https://github.com/xuxingya/tf2crf
Author: xingya.xu
Author-email: xingya.xu@gmail.com
License: MIT License
Description: # tf2crf
        * a simple CRF layer for tensorflow 2 keras
        * support keras masking
        
        ## Install
        ```python
        $ pip install tf2crf
        ```
        
        ## Features
        * easy to use CRF layer with tensorflow
        * now support the ModelWithCRFLossDSCLoss with DSC loss, which increases f1 score with unbalanced data (refer the paper [Dice Loss for Data-imbalanced NLP Tasks](https://arxiv.org/pdf/1911.02855.pdf))
        ## Attention
        * A dense layer is needed before the CRF layer to convert inputs to shape (batch_size, timesteps, num_classes).
        The 'num_class' is how many tags or catogories the model predicts.
        * 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
        ```python
        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)
        output = Dense(9, activation=None)(output)
        crf = CRF(dtype='float32')
        output = crf(output)
        base_model = Model(inputs, output)
        model = ModelWithCRFLoss(base_model)
        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)
        tf.keras.models.save_model(model, 'test/1')
        
        ```
        
        ## Supoort for tensorflow mixed precision training
        Currently these is a bug in tensorflow-addons.text.crf, which causes a dtype error when using miex precision. To correctly use mixed precison, you need to modify the line 488 of tensorflow_addons/text/crf.py to:
        ```python
        crf_fwd_cell = CrfDecodeForwardRnnCell(transition_params, dtype=inputs.dtype)
        ```
        ## Example
        ```python
        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
        from tensorflow.keras.mixed_precision import experimental as mixed_precision
        policy = mixed_precision.Policy('mixed_float16')
        mixed_precision.set_policy(policy)
        
        inputs = Input(shape=(None,), dtype='int32')
        output = Embedding(100, 40, trainable=True, mask_zero=True)(inputs)
        output = Bidirectional(GRU(64, return_sequences=True))(output)
        output = Dense(9, activation=None)(output)
        crf = CRF(dtype='float32')
        output = crf(output)
        base_model = Model(inputs, output)
        model = ModelWithCRFLoss(base_model)
        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)
        tf.keras.models.save_model(model, 'test/1')
        ```
        ## How to save the model
        Currently, Loading the model directly is not supported. So you need to load the model weights instead.
        For example:
        ```python
        tf.keras.models.save_model(model, '1')
        model.load_weights('1/variables/variables')
        ```
        or
        ```
        model.save_weights('model.h5')
        model.load_weights('model.h5')
        ```
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