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Updates #4

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c1f5229
Make compatible with torch >= 0.5
cimeister May 24, 2019
b8f4488
Removing .py extension since using module flag
cimeister May 24, 2019
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4 changes: 2 additions & 2 deletions Models/webaskb_ptrnet.py
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Expand Up @@ -225,7 +225,7 @@ def forward(self,input_variable, target_variable, loss=0, DO_TECHER_FORCING=Fals
result.append(np.argmax(decoder_attention.data[0].tolist()))

if type(loss)!=int:
loss_value = loss.data[0] / target_length
loss_value = loss.item() / target_length
else:
loss_value = 0
return loss_value , result, loss
return loss_value , result, loss
10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -72,11 +72,11 @@ can be downloaded from [ComplexWebQuestions](http://nlp.cs.tau.ac.il/compwebq)

Now you can do any of the following:

* Generate the noisy supervision data for training `python -m webaskb_run.py gen_noisy_sup`.
* Run a pointer network to generate split points in the question `python -m webaskb_run.py run_ptrnet`.
* Train the pointer network `python -m webaskb_run.py train_ptrnet`.
* Create final predication and calculate p@1 scores `python -m webaskb_run.py splitqa`.
* NEW! Run evaluation script for dev set `python -m eval_script.py Data/complex_web_questions/ComplexWebQuestions_dev.json Data/predictions_dev.json`.
* Generate the noisy supervision data for training `python -m webaskb_run gen_noisy_sup`.
* Run a pointer network to generate split points in the question `python -m webaskb_run run_ptrnet`.
* Train the pointer network `python -m webaskb_run train_ptrnet`.
* Create final predication and calculate p@1 scores `python -m webaskb_run splitqa`.
* NEW! Run evaluation script for dev set `python -m eval_script Data/complex_web_questions/ComplexWebQuestions_dev.json Data/predictions_dev.json`.

Options: ‘—eval_set dev’ or ‘—eval_set test’ to choose between the development and test set.

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