NLP Named Entity Recognition (NER)
It is necessary to write code to calculate metrics for the test dataset and conduct experiments with architectures (find the architecture that gives the best score).
Jupyter Notebook is attached.
Criteria:
send a link to Google Colab/Kaggle with the solution (with saved cell outputs);
add code to calculate metrics on the test dataset;
try other architectures
select optimizer/lr-scheduler parameters that would speed up the convergence of the model to a good solution
the result of the work - the notebook should contain the entire model training pipeline with comments and highlight the best model
Notes:
To speed up training, choose architectures with fewer parameters.
The code may fail with a CUDA OUT OF MEMORY ERROR - reduce the batch size.
Applications 1
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