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I have a paragraph and I want to get the probability (p(word | context) ) of each word, given previous words, for various models (e.g. pre-trained LSTM).

Where can pretrained models would allow me to do this? Can someone provide an example as to how to fo this in Python?

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  • $\begingroup$ What do you mean by 'predictability'? $\endgroup$ – jkm Dec 25 '19 at 9:56
  • $\begingroup$ @jkm sorry - p(word | context) $\endgroup$ – okuoub Dec 25 '19 at 10:54
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Without having a model trained for this specific task, I don't think this is possible. However, if you allow the following assumptions to be true, one approach would be as discussed below.

Assumptions

  • The tokenizer of the pre-trained model supports your new corpus
  • You are allowed (re-)train/fine-tune using a subset of your corpus

Approach Summary

After splitting your corpus into train/validation, create the following using your train dataset - for every sentence in the train corpus, randomly drop tokens with probability p and build a classifier to predict the dropped token. Use validation corpus to pick the best model like usual.

The classifier can be another neural network attached to whatever pre-trained network which acts like a black-box embedding layer.

References

BERT (https://arxiv.org/abs/1810.04805) and variants explores this idea in detail to build Masked LMs and have at least performed very well on benchmarks.

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