osiria
/

bert-tweet-italian-uncased-sentiment

 ---
 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"
 ---
+--------------------------------------------------------------------------------------------------
+<body>
+<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
+<br>
+<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">    Task: Sentiment Analysis</span>
+<br>
+<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">    Model: BERT-TWEET</span>
+<br>
+<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">    Lang: IT</span>
+<br>
+<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;">  </span>
+<br>
+<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
+</body>
+--------------------------------------------------------------------------------------------------
+<h3>Model description</h3>
+This is a <b>BERT</b> <b>[1]</b> uncased model for the <b>Italian</b> language, fine-tuned for Sentiment Analysis (<b>positive</b> and <b>negative</b> classes only) on the [SENTIPOLC-16](https://www.evalita.it/campaigns/evalita-2016/tasks-challenge/sentipolc/) dataset, using <b>BERT-TWEET-ITALIAN</b> ([bert-tweet-base-italian-uncased](https://huggingface.co/osiria/bert-tweet-base-italian-uncased)) as a pre-trained model.
+<h3>Training and Performances</h3>
+The model is trained to perform binary sentiment classification (<b>positive</b> vs <b>negative</b>) 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
+<h3>Quick usage</h3>
+```python
+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}]
+```
+<h3>References</h3>
+[1] https://arxiv.org/abs/1810.04805
+<h3>Limitations</h3>
+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.
+<h3>License</h3>
+The model is released under <b>Apache-2.0</b> license