Tensorflow-Keras port of https://github.com/fastnlp/TENER.

The motivation behind this port is:

• To check the claim that this architecture can perrform well in small dataset
• To build the basic data preprocessing from scratch
• Implement the Tener architecture in Tensorflow
• Tryout different position strategy after mimiking the basic metrics

• Dataset preparation and test cases
• Gin-config
• Plug and play trainer for datasets and model architecture
• Vanilla transformer model integration
• Tener transformer model
  • Embeddings
    • Sinusoidal
    • RelativeSinusoidal
    • Character Embedding
  • Attention
    • MultiHeadNaive
    • MiltiHeadRelative
• Tuning and Debugging
  • Vanilla Transformer Model
  • Tener Transformer Model

pip install -r requirements.txt

• Google Gin-Config based configuration
• Trainer script that reads the config and selects the dataset and model to be used
• Model class that encapsulates the Keras model layers, loss, metrics and train step

gin config file ---> trainer ---> dataset and model ---> Keras Model 

• Train

cd tener/
# To use vanilla transformer architecture from Tensorflow Tutorial
python bin/trainer.py --config_file=config/vanilla_transformer.gin
# Tener absed architecture 
python bin/trainer.py --config_file=config/tener.gin

• Test

cd tener/src/

pytest -s

# some times pytest can be picked from global installation
# breaking the pytest to use your environment related packages
/home/{user_home}/anaconda3/envs/{env}/bin/pytest -s

• Transformers: Attention Is All You Need - 2017

  • https://arxiv.org/pdf/1706.03762.pdf
  • Original Code from Google Brain team : https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
  • https://datascience.stackexchange.com/questions/51065/what-is-the-positional-encoding-in-the-transformer-model
  • https://machinelearningmastery.com/beam-search-decoder-natural-language-processing/
  • https://www.analyticsvidhya.com/blog/2019/06/understanding-transformers-nlp-state-of-the-art-models/?utm_source=blog&utm_medium=comprehensive-guide-attention-mechanism-deep-learning
  • http://jalammar.github.io/illustrated-transformer/
  • https://datascience.stackexchange.com/questions/51065/what-is-the-positional-encoding-in-the-transformer-model
  • https://github.com/tensorflow/nmt
  • https://www.youtube.com/watch?v=53YvP6gdD7U
  • https://www.analyticsvidhya.com/blog/2019/11/comprehensive-guide-attention-mechanism-deep-learning/
  • http://mlexplained.com/2017/12/29/attention-is-all-you-need-explained/
  • https://medium.com/@_init_/how-self-attention-with-relative-position-representations-works-28173b8c245a
  • https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/#.XeDjgtHhVXM

• Pytorch:

  • https://github.com/huggingface/transformers

• Tensorflow:

  • https://www.tensorflow.org/tutorials/text/transformer (Basics)

• Information Extraction with Transformer:

  • TENER: Adapting Transformer Encoder for Name Entity Recognition: https://arxiv.org/pdf/1911.04474.pdf
  • Chargrid + Transformer : https://www.groundai.com/project/bertgrid-contextualized-embedding-for-2d-document-representation-and-understanding/1
  • Bert Grid : https://arxiv.org/pdf/1909.04948.pdf