--- license: apache-2.0 pipeline_tag: text-classification tags: - sentiment language: - it --- # 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](https://aequa-tech.com/) - **Funded by:** [NGI-Search](https://www.ngi.eu/ngi-projects/ngi-search/) - **Language(s) (NLP):** Italian - **License:** apache-2.0 - **Finetuned from model:** [AlBERTo](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alberto) This model is a fine-tuned version of [AlBERTo](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alberto) Italian model on **sentiment analysis** # Training Details ## Training Data - SENTIPOLC [2014](https://live.european-language-grid.eu/catalogue/corpus/7480)/[2016](https://live.european-language-grid.eu/catalogue/corpus/7479) ## 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") ```