transfer_learning_text_classification

Explaining Fine Tuned Text Classifier 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
6. Get Explainations with Intel Explainable AI Tools

Running the notebook

To run PyTorch_Text_Classifier_fine_tuning_with_Attributions.ipynb, install the following dependencies:

1. Intel® Explainable AI
2. pip install intel-transfer-learning-tool==0.6

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}
}