A-tf.estimator-implimentation-of-text-classification

A Tensorflow pipeline for text classification task. the model is based on Text CNN with attention and position encoder. A pipeline is created: raw_data --> tf.record --> tf.dataset -- distributed tf.esitmator.

Training and evalutate process:

1. Make a instance of Dataset and generate tf_record files from raw data. This step is not in pipeline. You can import Dataset.class in another python file to do this.
2. This pipeline contains 2 channels, which means that both word level and character level tokens are used as input.
3. Once you have tf.record files ready, just run distributed_esitmator.py. default config is used for training and exporting.

exporting your saved_model

1. once you finish training, esitmator you auto export best model, just load this model so that you can predict.