A Fast Sequence Transducer Implementation with PyTorch Bindings. The corresponding publication is Sequence Transduction with Recurrent Neural Networks.
Tested with Python 3.7 and PyTorch 1.3
First install PyTorch then from the top level of the repo run
python setup.py install
And test with
python test.py
Creates random strings and checks if any of them are a real words. Mostly a waste of time ngl but it is cool to see it work and the fact that it can generate a real random word within10sec
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Sequential Transformer This is a code for training Transformers on sequential tasks such as language modeling. Unlike the original Transformer archite
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Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge This is an implementation of the paper,
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BiLSTM-CRF for Name Entity Recognition PyTorch version A PyTorch implemention of Bi-LSTM-CRF model for Chinese Named Entity Recognition. 使用 PyTorch 实现
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