| --- |
| license: apache-2.0 |
| metrics: |
| - accuracy |
| - f1 |
| pipeline_tag: text-classification |
| tags: |
| - flame |
| language: |
| - it |
| --- |
| # Flame detection 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 **flame detection** |
|
|
| # Training Details |
|
|
| ## Training Data |
|
|
| - [HaSpeeDe 2020](https://live.european-language-grid.eu/catalogue/corpus/7498) |
| - [Sarcastic Hate Speech dataset](https://github.com/simonasnow/Sarcastic-Hate-Speech) |
| - [HaSpeeDe 2023](https://github.com/mirkolai/EVALITA2023-HaSpeeDe3) |
| - [Debunker-Assistant corpus](https://github.com/AequaTech/DebunkerAssistant/tree/main/evaluation/training_datasets) |
|
|
| ## 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 HaSpeeDe 2020 test sets (tweets and news headlines) obtaining the following results: |
| |
| ## Metrics and Results |
| Tweets: |
| - macro F1: 0.81 |
| - accuracy: 0.81 |
| - precision of positive class: 0.77 |
| - recall of positive class: 0.88 |
| - F1 of positive class: 0.82 |
| |
| News Headlines: |
| - macro F1: 0.64 |
| - accuracy: 0.73 |
| - precision of positive class: 0.79 |
| - recall of positive class: 0.33 |
| - F1 of positive class: 0.47 |
| |
| # 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/flame-it',num_labels=2) |
| 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) |
| classifier("text") |
| ``` |
