Text Models

There are a number of built-in compiled text models in Analytics Zoo TFPark for Natural Language Processing (NLP) tasks based on KerasModel.

See this page for more details about how to construct built-in models for intent extraction, named entity extraction and pos tagging. etc.

In this page, we show the general steps how to train and evaluate an NER model in a distributed fashion and use this model for distributed inference. For other models, the steps are more or less quite similar.


Model Construction

You can easily construct a model for named entity recognition using the following API.

from zoo.tfpark.text.keras import NER

model = NER(num_entities, word_vocab_size, char_vocab_size, word_length)

Data Preparation

The NER model has two inputs: word indices and character indices.

Thus, each raw text record needs to go through word-wise tokenization, character-wise segmentation and alignment to the same target length for preprocessing.

If you are using numpy arrays, then the input x should be a list of two numpy arrays:

If there are labels (for training and evaluation), y should be another numpy array of shape (batch, sequence_length, word_length) for entity tags.

Alternatively, you can construct a TFDataSet directly if you are dealing with RDD. Each record in TFDataSet should contain word indices, character indices and labels (if any) as well.

Model Training

You can easily call fit to train the NER model in a distributed fashion. You don't need to specify y if x is already a TFDataSet.

model.fit(x, y, batch_size, epochs, distributed=True)

Model Evaluation

You can easily call evaluate to evaluate the NER model in a distributed fashion. You don't need to specify y if x is already a TFDataSet.

result = model.evaluate(x, y, distributed=True)

Model Save and Load

After training, you can save the NER model to a single HDF5 file.


For inference, you can load a directly trained NER model (with weights) from HDF5 file.

from zoo.tfpark.text.keras import NER

model = NER.load_model(path)

Model Inference

You can easily call predict to use the trained NER model for distributed inference. Note that you don't necessarily need labels for prediction.

predictions = model.predict(x, distributed=True)