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How to train and evaluate the TASK2_A? #12

@agn-7

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@agn-7

Hello

I've run the TASK2_A with several embeddings file via its configuration (model/params.py) through command below:

python3 model.task2.train.py

And I've got the following result:

Running on:cuda
loading word embeddings...
Didn't find embeddings cache file /content/gdrive/My Drive/app/sentiment/glove.twitter.27B.200d.txt
Indexing file /content/gdrive/My Drive/app/sentiment/glove.twitter.27B.200d.txt ...
{200}
Found 1193516 word vectors.
Reading twitter_2018 - 1grams ...
Reading twitter_2018 - 2grams ...
Reading twitter_2018 - 1grams ...
Building word-level datasets...
Loading TASK2_A_train from cache!
Total words: 8286649, Total unks:918613 (11.09%)
Unique words: 160897, Unique unks:81783 (50.83%)
Labels statistics:
{0: '21.72%', 1: '10.47%', 2: '10.28%', 3: '5.53%', 4: '4.98%', 5: '4.71%', 6: '4.31%', 7: '3.70%', 8: '3.44%', 9: '3.26%', 10: '3.25%', 11: '3.10%', 12: '2.80%', 13: '2.62%', 14: '2.73%', 15: '2.71%', 16: '2.64%', 17: '2.59%', 18: '2.68%', 19: '2.49%'}

Loading TASK2_A_trial from cache!
Total words: 814679, Total unks:77013 (9.45%)
Unique words: 40332, Unique unks:10207 (25.31%)
Labels statistics:
{0: '21.52%', 1: '10.56%', 2: '10.48%', 3: '5.77%', 4: '5.03%', 5: '4.63%', 6: '4.10%', 7: '3.79%', 8: '3.59%', 9: '3.34%', 10: '3.09%', 11: '3.06%', 12: '2.92%', 13: '2.69%', 14: '2.75%', 15: '2.50%', 16: '2.61%', 17: '2.56%', 18: '2.57%', 19: '2.43%'}

Loading TASK2_A_gold from cache!
Total words: 895048, Total unks:103962 (11.62%)
Unique words: 42546, Unique unks:11668 (27.42%)
Labels statistics:
{0: '21.60%', 1: '9.66%', 2: '9.07%', 3: '5.21%', 4: '7.43%', 5: '3.23%', 6: '3.99%', 7: '5.50%', 8: '3.10%', 9: '2.35%', 10: '2.86%', 11: '3.90%', 12: '2.53%', 13: '2.23%', 14: '2.61%', 15: '2.49%', 16: '2.31%', 17: '3.09%', 18: '4.83%', 19: '2.02%'}

Where is the F1-score result?
What did I miss?

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