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A Text Classification Usage Example for pip users

Intro

In this tutorial, we demonstrate a text classification task with a demo mock dataset for users install by pip.

A complete process contains following steps:

  • Prepare the data set.
  • Develop custom modules (optional).
  • Set the config file.
  • Train a model.
  • Export a model

Please clone our demo repository:

git clone --depth 1 https://github.com/applenob/delta_demo.git
cd ./delta_demo

A quick review for installation

If you haven't install delta-nlp, please:

pip install delta-nlp

Requirements: You need tensorflow==2.0.0 and python==3.6 in MacOS or Linux.

Prepare the Data Set

run the script:

./gen_data.sh

The generated data are in directory: data.

The generated data for text classification should be in the standard format for text classification, which is "label\tdocument".

Develop custom modules (optional)

Please make sure we don't have modules you need before you decide to develop your own modules.

@registers.model.register
class TestHierarchicalAttentionModel(HierarchicalModel):
  """Hierarchical text classification model with attention."""

  def __init__(self, config, **kwargs):
    super().__init__(config, **kwargs)

    logging.info("Initialize HierarchicalAttentionModel...")

    self.vocab_size = config['data']['vocab_size']
    self.num_classes = config['data']['task']['classes']['num_classes']
    self.use_true_length = config['model'].get('use_true_length', False)
    if self.use_true_length:
      self.split_token = config['data']['split_token']
    self.padding_token = utils.PAD_IDX

You need to register this module file path in the config file config/han-cls.yml (relative to the current work directory).

custom_modules:
  - "test_model.py"

Set the Config File

The config file of this example is config/han-cls.yml

In the config file, we set the task to be TextClsTask and the model to be TestHierarchicalAttentionModel.

Config Details

The config is composed by 3 parts: data, model, solver.

Data related configs are under data. You can set the data path (including training set, dev set and test set). The data process configs can also be found here (mainly under task). For example, we set use_dense: false since no dense input was used here. We set language: chinese since it's a Chinese text.

Model parameters are under model. The most important config here is name: TestHierarchicalAttentionModel, which specifies the model to use. Detail structure configs are under net->structure. Here, the max_sen_len is 32 and max_doc_len is 32.

The configs under solver are used by solver class, including training optimizer, evaluation metrics and checkpoint saver. Here the class is RawSolver.

Train a Model

After setting the config file, you are ready to train a model.

delta --cmd train_and_eval --config config/han-cls.yml

The argument cmd tells the platform to train a model and also evaluate the dev set during the training process.

After enough steps of training, you would find the model checkpoints have been saved to the directory set by saver->model_path, which is exp/han-cls/ckpt in this case.

Export a Model

If you would like to export a specific checkpoint to be exported, please set infer_model_path in config file. Otherwise, platform will simply find the newest checkpoint under the directory set by saver->model_path.

delta --cmd export_model --config/han-cls.yml

The exported models are in the directory set by config service->model_path, which is exp/han-cls/service here.