tlt_api_pyt_text_classification

Text Classifier fine tuning with PyTorch and the Intel® Transfer Learning Tool API

This notebook demonstrates how to use the Intel Transfer Learning Tool API to do fine tuning for text classification using various BERT models from Hugging Face.

The notebook performs the following steps:

1. Install dependencies and setup parameters
2. Get the model
3. Get the dataset
4. Prepare the dataset
5. Fine tuning
6. Predict
7. Export the saved model
8. Quantization

Running the notebook

To run the notebook, follow the instructions to setup the PyTorch notebook environment.

References

Dataset citations:

@InProceedings{maas-EtAl:2011:ACL-HLT2011,
  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
  title     = {Learning Word Vectors for Sentiment Analysis},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  month     = {June},
  year      = {2011},
  address   = {Portland, Oregon, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {142--150},
  url       = {http://www.aclweb.org/anthology/P11-1015}
}

@misc{misc_sms_spam_collection_228,
  author       = {Almeida, Tiago},
  title        = {{SMS Spam Collection}},
  year         = {2012},
  howpublished = {UCI Machine Learning Repository}
}

Please see this dataset's applicable license for terms and conditions. Intel Corporation does not own the rights to this data set and does not confer any rights to it.