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://www.unioneprofessionisti.com

https://www.unideeplearning.com/

gbarone77/polibert_sa

🤗 + polibert_SA - POLItic BERT based Sentiment Analysis

Model description

Hands-on

Hyperparameters

Acknowledgments

gbarone77/
polibert_sa