metadata
license: apache-2.0
metrics:
- accuracy
- f1
pipeline_tag: text-classification
tags:
- stereotype
language:
- it
Stereotype detection at aequa-tech
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 stereotypes detection
Training Details
Training Data
- HaSpeeDe 2020
- Sarcastic Hate Speech dataset
- Racial stereotypes corpus available upon request to the authors of A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads
- Debunker-Assistant corpus
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 test sets (tweets and news headlines) obtaining the following results:
Metrics and Results
Tweets:
- macro F1: 0.75
- accuracy: 0.75
- precision of positive class: 0.66
- recall of positive class: 0.94
- F1 of positive class: 0.78
News Headlines:
- macro F1: 0.72
- accuracy: 0.77
- precision of positive class: 0.73
- recall of positive class: 0.52
- F1 of positive class: 0.61
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/stereotype-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")