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--- |
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license: apache-2.0 |
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pipeline_tag: text-classification |
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tags: |
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- sentiment |
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language: |
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- it |
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--- |
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# Sentiment at aequa-tech |
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## cite this work |
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``` |
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@inproceedings{arthur2023debunker, |
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title={Debunker Assistant: a support for detecting online misinformation}, |
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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}, |
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booktitle={Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023)}, |
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volume={3596}, |
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pages={1--5}, |
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year={2023}, |
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organization={Federico Boschetti, Gianluca E. Lebani, Bernardo Magnini, Nicole Novielli} |
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} |
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``` |
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## Model Description |
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- **Developed by:** [aequa-tech](https://aequa-tech.com/) |
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- **Funded by:** [NGI-Search](https://www.ngi.eu/ngi-projects/ngi-search/) |
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- **Language(s) (NLP):** Italian |
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- **License:** apache-2.0 |
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- **Finetuned from model:** [AlBERTo](https://huggingface.co/m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alberto) |
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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** |
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# Training Details |
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## Training Data |
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- SENTIPOLC [2014](https://live.european-language-grid.eu/catalogue/corpus/7480)/[2016](https://live.european-language-grid.eu/catalogue/corpus/7479) |
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## Training Hyperparameters |
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- learning_rate: 2e-5 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam |
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# Evaluation |
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## Testing Data |
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It was tested on SENTIPOLC 2016 test set |
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# Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.0 |
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- Accelerate 0.30.0 |
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# How to use this model: |
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```Python |
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model = AutoModelForSequenceClassification.from_pretrained('aequa-tech/sentiment-it',num_labels=3, ignore_mismatched_sizes=True) |
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tokenizer = AutoTokenizer.from_pretrained("m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0") |
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classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None) |
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classifier("L'insostenibile leggerezza dell'essere") |
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``` |
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