tfhub_text_classification

Text Classifier fine tuning with TensorFlow

These notebooks demonstrate fine tuning using various BERT models from TF Hub using Intel® Optimization for TensorFlow for text classification.

The notebook performs the following steps:

1. Install dependencies and setup parameters
2. Prepare the dataset
3. Build the model
4. Fine tuning and evaluation
5. Export the model
6. Reload the model and make predictions

Running the notebook

To run the notebook, follow the instructions to setup the TensorFlow 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{zhang2015characterlevel,
    title={Character-level Convolutional Networks for Text Classification},
    author={Xiang Zhang and Junbo Zhao and Yann LeCun},
    year={2015},
    eprint={1509.01626},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@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.