Evaluating Neural Network model using Errant
See original GitHub issueDear @all
I have an overview of your documentation, I’m still confused about how to evaluate my Neural Network model (GEC). As I understood that, I have to translate the test set (correcting), then build a new (M2) file using errant_parallel
command. The last step is to use errant_compare
with the span-based correction to get F0.5
score.
Is this correct? What is the optimal way to evaluate my NN model using Errant?
Regards,
Issue Analytics
- State:
- Created 3 years ago
- Comments:6 (3 by maintainers)
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Looks like you forgot to install a spacy model.
You need to run
python3 -m spacy download en
after you pip install errant. It’s the last line in the installation instructions.Hi,
You don’t need to be confused because you are correct!
errant_parallel
to build a new M2 file from the original sentences and your corrected sentences.errant_compare
to compare your system M2 file with the gold standard M2 file.That’s all there is to it. 😃