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unideeplearning/polibert_sa unideeplearning/polibert_sa
295 downloads
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pytorch

tf

Contributed by

unideeplearning Gianfranco Barone
1 model

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unideeplearning/polibert_sa") model = AutoModelForSequenceClassification.from_pretrained("unideeplearning/polibert_sa")

πŸ€— + 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

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 Γ¨ 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/