Auto NLP (with Huggingface Transformers)

Work in progress. Currently supports training models for single/multiclass text classification and multilabel text classification.

Features:

• Saves the best Pytorch model at the end of training.
• Converts Pytorch model to ONNX for production.
• Quantizes ONNX model for reduced file size.
• Outputs confusion matrices and test set predictions to evaluate performance.

To use:

1. Place your training dataset into the input folder along with a spec YAML file containing information about the name of the text field, name of the label fields, any training parameter information, etc.
2. Run train.py in the src directory.
3. The trained models will be saved in the output directory.

Installing:

Install dependencies:

pip install -r requirements.txt

Install Pytorch:

pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio===0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

Notes

• For multilabel classification, I have only managed to get it to work if the dataset is saved as a JSON file and the labels are prepared with Scikit-Learn's MultiLabelBinarizer. The labels inside the list also need to be converted to floats rather than ints.

To do:

• Add support for token classification
• Add distillation
• Add hyperparameter optimization