Custom Classifier on Top of Bert-like Language Model
Take pre-trained language model and build custom classifier on top of it.
bert language-modeling pytorch pytorch-lightning sentiment-analysis transformers natural-language-processing polberta attention tutorial article code

  • Taking existing pre-trained language model and understanding it’s output - here I use PolBERTa trained for Polish language.
  • Building custom classification head on top of the LM.
  • Using fast tokenizers to efficiently tokenize and pad input text as well as prepare attention masks.
  • Preparing reproducible training code with PyTorch Lightning.
  • Finding good starting learning rate for the model.
  • Validating the trained model on PolEmo 2.0 dataset (benchmark for Polish language sentiment analysis with 4 classes).

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Authors
Data Engineer / Machine Learning Engineer @ Egnyte
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