# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("aequa-tech/irony-it")
model = AutoModelForSequenceClassification.from_pretrained("aequa-tech/irony-it", device_map="auto")Quick Links
Irony 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 irony detection
Training Details
Training Data
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 IronITA test set obtaining the following results:
Metrics and Results
- macro F1: 0.79
- accuracy: 0.79
- precision of positive class: 0.77
- recall of positive class: 0.84
- F1 of positive class: 0.80
Framework versions
- Transformers 4.30.2
- Pytorch 2.1.2
- Datasets 2.19.0
- Accelerate 0.30.0
How to use this model:
model = AutoModelForSequenceClassification.from_pretrained('aequa-tech/irony-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("Prendi una gioia. Ora posala, che non è tua.")
- Downloads last month
- 12
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aequa-tech/irony-it")