license: apache-2.0
language:
- it
widget:
- text: >-
una fantastica giornata di #calcio! grande prestazione del mister e della
squadra
example_title: Example 1
- text: 'il governo dovrebbe fare politica, non soltanto propaganda! #vergogna'
example_title: Example 2
- text: >-
che serata da sogno sul #redcarpet! grazie a tutti gli attori e registi
del cinema italiano #oscar #awards
example_title: Example 3
Task: Sentiment Analysis
Model: BERT-TWEET
Lang: IT
Model description
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.
Training and Performances
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:
| Accuracy | Recall | Precision | F1 |
|---|---|---|---|
| 83.67 | 83.15 | 80.48 | 81.49 |
The Recall, Precision and F1 metrics are averaged over the two classes
Quick usage
from transformers import BertTokenizerFast, BertForSequenceClassification
tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-tweet-italian-uncased-sentiment")
model = BertForSequenceClassification.from_pretrained("osiria/bert-tweet-italian-uncased-sentiment")
from transformers import pipeline
classifier = pipeline("text-classification", model = model, tokenizer = tokenizer)
classifier("una fantastica giornata di #calcio! grande prestazione del mister e della squadra")
# [{'label': 'POSITIVE', 'score': 0.9883694648742676}]
References
[1] https://arxiv.org/abs/1810.04805
Limitations
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.
License
The model is released under Apache-2.0 license