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Awesome Open Source

NLP Made Easy

Simple code notes for explaining NLP building blocks

  • Subword Segmentation Techniques
    • Let's compare various tokenizers, i.e., nltk, BPE, SentencePiece, and Bert tokenizer.
  • Beam Decoding
    • Beam decoding is essential for seq2seq tasks. But it's notoriously complicated to implement. Here's a relatively easy one, batchfying candidates.
  • How to get the last hidden vector of rnns properly
    • We'll see how to get the last hidden states of Rnns in Tensorflow and PyTorch.
  • Tensorflow seq2seq template based on the g2p task
    • We'll write a simple template for seq2seq using Tensorflow. For demonstration, we attack the g2p task. G2p is a task of converting graphemes (spelling) to phonemes (pronunciation). It's a very good source for this purpose as it's simple enough for you to up and run.
  • PyTorch seq2seq template based on the g2p task
    • We'll write a simple template for seq2seq using PyTorch. For demonstration, we attack the g2p task. G2p is a task of converting graphemes (spelling) to phonemes (pronunciation). It's a very good source for this purpose as it's simple enough for you to up and run.
  • [Attention mechanism](Work in progress)
  • POS-tagging with BERT Fine-tuning
    • BERT is known to be good at Sequence tagging tasks like Named Entity Recognition. Let's see if it's true for POS-tagging.
  • Dropout in a minute
    • Dropout is arguably the most popular regularization technique in deep learning. Let's check again how it work.
  • Ngram LM vs. rnnlm(WIP)
  • Data Augmentation for Quora Question Pairs
    • Let's see if it's effective to augment training data in the task of quora question pairs.

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jupyter-notebook (6,027
nlp (1,062
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