torchTextClassifiers Wrapper#

The main wrapper class for text classification tasks.

Main Class#

class torchTextClassifiers.torchTextClassifiers.torchTextClassifiers(tokenizer, model_config, ragged_multilabel=False)[source]#

Bases: object

Generic text classifier framework supporting multiple architectures.

Given a tokenizer and model configuration, this class initializes: - Text embedding layer (if needed) - Categorical variable embedding network (if categorical variables are provided) - Classification head The resulting model can be trained using PyTorch Lightning and used for predictions.

Methods

train

Train the classifier using PyTorch Lightning.

predict

__init__(tokenizer, model_config, ragged_multilabel=False)[source]#

Initialize the torchTextClassifiers instance.

Parameters:

  • tokenizer (BaseTokenizer) – A tokenizer instance for text preprocessing

  • model_config (ModelConfig) – Configuration parameters for the text classification model

Example

>>> from torchTextClassifiers import ModelConfig, TrainingConfig, torchTextClassifiers
>>>  # Assume tokenizer is a trained BaseTokenizer instance
>>> model_config = ModelConfig(
...     embedding_dim=10,
...     categorical_vocabulary_sizes=[30, 25],
...     categorical_embedding_dims=[10, 5],
...     num_classes=10,
... )
>>> ttc = torchTextClassifiers(
...     tokenizer=tokenizer,
...     model_config=model_config,
... )
train(X_train, y_train, training_config, X_val=None, y_val=None, verbose=False)[source]#

Train the classifier using PyTorch Lightning.

This method handles the complete training process including: - Data validation and preprocessing - Dataset and DataLoader creation - PyTorch Lightning trainer setup with callbacks - Model training with early stopping - Best model loading after training

Parameters:

  • X_train (ndarray) – Training input data

  • y_train (ndarray) – Training labels

  • X_val (Optional[ndarray]) – Validation input data

  • y_val (Optional[ndarray]) – Validation labels

  • training_config (TrainingConfig) – Configuration parameters for training

  • verbose (bool) – Whether to print training progress information

Return type:

None

Example

>>> training_config = TrainingConfig(
...     lr=1e-3,
...     batch_size=4,
...     num_epochs=1,
... )
>>> ttc.train(
...     X_train=X,
...     y_train=Y,
...     X_val=X,
...     y_val=Y,
...     training_config=training_config,
... )
predict(X_test, top_k=1, explain=False)[source]#
Parameters:

  • X_test (np.ndarray) – input data to predict on, shape (N,d) where the first column is text and the rest are categorical variables

  • top_k (int) – for each sentence, return the top_k most likely predictions (default: 1)

  • explain (bool) – launch gradient integration to have an explanation of the prediction (default: False)

Returns: A dictionary containing the following fields:

  • predictions (torch.Tensor, shape (len(text), top_k)): A tensor containing the top_k most likely codes to the query.

  • confidence (torch.Tensor, shape (len(text), top_k)): A tensor array containing the corresponding confidence scores.

  • if explain is True:

    • attributions (torch.Tensor, shape (len(text), top_k, seq_len)): A tensor containing the attributions for each token in the text.

Usage Example#

from torchTextClassifiers import torchTextClassifiers, ModelConfig, TrainingConfig
from torchTextClassifiers.tokenizers import WordPieceTokenizer

# Create tokenizer
tokenizer = WordPieceTokenizer()
tokenizer.train(texts, vocab_size=1000)

# Configure model
model_config = ModelConfig(embedding_dim=64, num_classes=2)
training_config = TrainingConfig(num_epochs=10, batch_size=16, lr=1e-3)

# Create and train classifier
classifier = torchTextClassifiers(tokenizer=tokenizer, model_config=model_config)
classifier.train(X_text=texts, y=labels, training_config=training_config)

# Make predictions
predictions = classifier.predict(new_texts)
probabilities = classifier.predict_proba(new_texts)

See Also#