Fine-Tuning BERT for Sentiment Analysis Task
precision recall f1-score support
negative 0.93 0.88 0.90 245
neutral 0.84 0.88 0.86 254
positive 0.92 0.92 0.92 289
accuracy 0.89 788
macro avg 0.90 0.89 0.89 788
weighted avg 0.90 0.89 0.90 788
precision recall f1-score support
negative 0.85 0.82 0.84 329
neutral 0.49 0.54 0.51 228
positive 0.81 0.80 0.80 443
accuracy 0.74 1000
macro avg 0.72 0.72 0.72 1000
weighted avg 0.75 0.74 0.75 1000
precision recall f1-score support
negative 0.76 0.86 0.81 88
positive 0.83 0.71 0.76 82
accuracy 0.79 170
macro avg 0.79 0.79 0.79 170
weighted avg 0.79 0.79 0.79 170
precision recall f1-score support
negative 0.80 0.61 0.69 146
positive 0.83 0.93 0.88 308
accuracy 0.83 454
macro avg 0.82 0.77 0.79 454
weighted avg 0.82 0.83 0.82 454
precision recall f1-score support
happy 0.88 0.95 0.91 56
not-relevant 0.64 0.50 0.56 14
angry 0.00 0.00 0.00 2
disgust 0.00 0.00 0.00 1
sad 0.50 0.50 0.50 2
surprise 0.00 0.00 0.00 0
accuracy 0.81 75
macro avg 0.34 0.32 0.33 75
weighted avg 0.79 0.81 0.80 75
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Sentiment Analysis with BERT and Transformers by Hugging Face using PyTorch and Python
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BERT Fine-Tuning Tutorial with PyTorch