osiria/

bert-tweet-italian-uncased-sentiment

This is a BERT [1] uncased model for the Italian language, fine-tuned for Sentiment Analysis (positive and negative classes only) on the SENTIPOLC-16 dataset, using BERT-TWEET-ITALIAN (bert-tweet-base-italian-uncased) as a pre-trained model.

The model is trained to perform binary sentiment classification (positive vs negative) and it's meant to be used primarily on tweets or other social media posts. It has been fine-tuned for Sentiment Analysis, using the SENTIPOLC-16 dataset, for 3 epochs with a constant learning rate of 1e-5 and exploiting class weighting to compensate for the class imbalance. Instances having both positive and negative sentiment have been excluded, resulting in 4154 training instances and 1050 test instances

The performances on the test set are reported in the following table:

The Recall, Precision and F1 metrics are averaged over the two classes

[1] https://arxiv.org/abs/1810.04805

This model was trained on tweets, so it's mainly suitable for general-purpose social media text processing, involving short texts written in a social network style. It might show limitations when it comes to longer and more structured text, or domain-specific text.

The model is released under Apache-2.0 license