pytorch_text_classification

Text Classifier fine tuning with PyTorch

This notebook demonstrates fine tuning pretrained models from Hugging Face using text classification datasets from the Hugging Face Datasets catalog or a custom dataset. The IMDb Larget Movie Review dataset is used from the Hugging Face Datasets catalog, and the SMS Spam Collection dataset is used as an example of a custom dataset being loaded from a csv file.

The notebook uses Intel® Extension for PyTorch* which extends PyTorch with optimizations for extra performance boost on Intel hardware.

The notebook performs the following steps:

1. Import dependencies and setup parameters
2. Prepare the dataset
3. Prepare the Model for Fine Tuning and Evaluation
4. Export the model
5. Reload the model and make predictions

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.