aequa-tech/stereotype-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")