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According to the model finetuning pipeline, the input tokens are tokenized with other tokenizer (e.g. PyThaiNLp's newmm for thainer dataset) and then retokenizer with SentencePiece tokenizer. However, the input tokens fed to the finetuned model is the tokens tokenized with SentencePiece only (not newmm first, and then followed by SentencePiece)
Proposed solution:
Pretokenize with PyThaiNLP's newmm tokenizer
Retokenize with the subword tokenizer (SentencePiece)
Map the prediction results of the subword tokens to the tokens tokenized with newmm
Return the prediction results in word-level and chunk-level
According to the model finetuning pipeline, the input tokens are tokenized with other tokenizer (e.g. PyThaiNLp's newmm for
thainer
dataset) and then retokenizer with SentencePiece tokenizer. However, the input tokens fed to the finetuned model is the tokens tokenized with SentencePiece only (not newmm first, and then followed by SentencePiece)Proposed solution:
Branch name:
feature/ner_pipeline
NER Pipeline Demo (via Colab): https://colab.research.google.com/drive/1-54NeM_wsjitaiSXfMBpcnqzbPMR0a9R#scrollTo=VzSGZbwWaiOI
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