pytorch tf jax bert text-classification it transformers sentiment Italian license:mit
.gitattributes README.md config.json flax_model.msgpack pytorch_model.bin special_tokens_map.json tf_model.h5 tokenizer_config.json vocab.txt
---
language: it
tags:
- sentiment
- Italian
license: MIT
widget:
- text: 'Giuseppe Rossi è un ottimo politico'
---

# 🤗 + polibert_SA - POLItic BERT based Sentiment Analysis
  
## Model description  
  
This model performs sentiment analysis on Italian political twitter sentences. It was trained starting from an instance of "bert-base-italian-uncased-xxl" and fine-tuned on an Italian dataset of tweets. You can try it out at https://www.unideeplearning.com/twitter_sa/ (in italian!)
  
#### Hands-on  
  
```python
import torch
from torch import nn 
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("unideeplearning/polibert_sa")
model = AutoModelForSequenceClassification.from_pretrained("unideeplearning/polibert_sa")
			



text = "Giuseppe Rossi è un pessimo politico"
input_ids = tokenizer.encode(text, add_special_tokens=True, return_tensors= 'pt')

logits, = model(input_ids)
logits = logits.squeeze(0)

prob = nn.functional.softmax(logits, dim=0)

# 0 Negative, 1 Neutral, 2 Positive 
print(prob.argmax().tolist())
```  
  
#### Hyperparameters

- Optimizer: **AdamW** with learning rate of **2e-5**, epsilon of **1e-8**
- Max epochs: **2**
- Batch size: **16**

## Acknowledgments

Thanks to the support from: 
the [Hugging Face](https://huggingface.co/), https://www.unioneprofessionisti.com

https://www.unideeplearning.com/
