A Text Classification Usage Example for pip users¶
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
pip install delta-nlp
Requirements: You need
MacOS or Linux.
Prepare the Data Set¶
run the script:
The generated data are in directory:
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
In the config file, we set the task to be
TextClsTask and the model to be
The config is composed by 3 parts:
Data related configs are under
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
For example, we set
use_dense: false since no dense input was used here.
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
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
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
delta --cmd export_model --config/han-cls.yml
The exported models are in the directory set by config
service->model_path, which is