Kaniblu Pytorch-BiLSTM-CRF

A Kaniblu Pytorch-BiLSTM-CRF is a Bidirectional LSTM/CRF (BiLTSM-CRF) Training System that leverages mini-batch operations on multiple GPUs.

• Context:
  • Source Code available at: https://github.com/kaniblu/pytorch-bilstmcrf
  • It was first developed by Yoo, (2018).
• Example(s):
  • pytorch-bilstmcrf
  • …
• Counter-Example(s):
  • an EMNLP 2017 BiLSTM-CNN-CRF Training System.
  • an Unidirectional LSTM Training System,
  • a BiLSTM-CRF Training System,
  • a CNN Training System,
  • a Bidirectional LSTM-CNN Training System that uses a Softmax classifier.
• See: Bidirectional Neural Network, Convolutional Neural Network, Conditional Random Field, Bidirectional Recurrent Neural Network, Dynamic Neural Network.

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References

2018

• (Yoo, 2018) ⇒ BiLSTM-CRF on PyTorch repository: https://github.com/kaniblu/pytorch-bilstmcrf Retrieved 2018-06-10
  • QUOTE: An efficient BiLSTM-CRF implementation that leverages mini-batch operations on multiple GPUs (...)

    Training

    Prepare data first. Data must be supplied with separate text files for each input or target label type. Each line contains a single sequence, and each pair of tokens are separated with a space. For example, for the task of Named Entity Recognition using words and Part-of-Speech tags, the input and label files might be prepared as follows:

(sents.txt)

the fat rat sat on a mat

...

(post.txt)

det adj noun verb prep det noun

...

(labels.txt)

O O B-Animal O O O B-Object

...

Then above input and label files are provided to train.py using --input-path and --label-path respectively.

python train.py --input-path sents.txt --input-path pos.txt --label-path labels.txt

You might need to setup several more parameters in order to make it work. Checkout examples/atis for an example of training a simple BiLSTM-CRF model with ATIS dataset. Run python preprocess.py at the example directory to convert to the dataset to train.py-friendly format, then run

python ../../train.py --config train-atis.yml

to see a running example. The example configuration assumes that standalone tensorboard is installed (you could turn it off in the configuration file).

For more information on the configurations, check out python train.py --help.

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