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aequa-tech/
sentiment-it

Name: sentiment-it
Rating: 2.5 (22 reviews)

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Sentiment at aequa-tech

cite this work

@inproceedings{arthur2023debunker,
  title={Debunker Assistant: a support for detecting online misinformation},
  author={Arthur, Thomas Edward Capozzi Lupi and Cignarella, Alessandra Teresa and Frenda, Simona and Lai, Mirko and Stranisci, Marco Antonio and Urbinati, Alessandra and others},
  booktitle={Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023)},
  volume={3596},
  pages={1--5},
  year={2023},
  organization={Federico Boschetti, Gianluca E. Lebani, Bernardo Magnini, Nicole Novielli}
}

Model Description

Developed by: aequa-tech
Funded by: NGI-Search
Language(s) (NLP): Italian
License: apache-2.0
Finetuned from model: AlBERTo

This model is a fine-tuned version of AlBERTo Italian model on sentiment analysis

Training Details

Training Data

SENTIPOLC 2014/2016

Training Hyperparameters

learning_rate: 2e-5
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam

Evaluation

Testing Data

It was tested on SENTIPOLC 2016 test set

Framework versions

Transformers 4.30.2
Pytorch 2.1.2
Datasets 2.19.0
Accelerate 0.30.0

How to use this model:

Python

model = AutoModelForSequenceClassification.from_pretrained('aequa-tech/sentiment-it',num_labels=3, ignore_mismatched_sizes=True) 
tokenizer = AutoTokenizer.from_pretrained("m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0") 
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None)
classifier("L'insostenibile leggerezza dell'essere")