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<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">    Task: Sentiment Analysis</span>
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<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">    Model: BERT-TWEET</span>
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<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">    Lang: IT</span>
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<h3>Model description</h3>

This is a <b>BERT</b> <b>[1]</b> uncased model for the <b>Italian</b> language, fine-tuned for <b>Sentiment Analysis</b> (<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


{"language": ["it"], "license": "apache-2.0", "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"}]}
transformers pytorch safetensors bert text-classification it arxiv:1810.04805 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #it #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 



---



 
  

    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:



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


### Quick usage


### References


[1] URL


### 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



### 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:



The Recall, Precision and F1 metrics are averaged over the two classes ### Quick usage ### References


[1] URL ### 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
TAGS
#transformers #pytorch #safetensors #bert #text-classification #it #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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:



The Recall, Precision and F1 metrics are averaged over the two classes ### Quick usage ### References


[1] URL ### 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