francesco-zatto/hateBERT-freeze-all-sexism-detector

HateBERT Sexism Classifier (Linear Probing / Freeze All)

This model is a fine-tuned version of GroNLP/hateBERT, trained for multi-class sexism detection on the EXIST 2023 Task 2 dataset.

Experiment Details: freeze_all

This repository contains the Linear Probing variant of our ablation study.

• —All parameters in the base BERT model (model.bert.parameters()) were frozen during training.
• —Only the final classification head was trained.
• —This approach protects the pre-trained weights from catastrophic forgetting and speeds up training, though it relies entirely on the base model's existing feature representations.

Intended Use

Categorizes English tweets into one of four sexist intentions:

1. 1.- (Non-sexist)
2. 2.DIRECT (Directly sexist messages)
3. 3.JUDGEMENTAL (Messages condemning sexist behaviors)
4. 4.REPORTED (Messages reporting a sexist situation)

Preprocessing

Because HateBERT is built on bert-base-uncased, it automatically lowercases text. If you are keeping preprocessing consistent across your ablation study, ensure your inputs are cleaned accordingly:

• —Replace user mentions (@user) with the token @user
• —Replace URLs with the token http
• —(Handled by tokenizer) Lowercase all text

Evaluation Results (Test Set)

• —Macro F1: 0.1950
• —Precision: 0.1598
• —Recall: 0.2500

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo_id = "francesco-zatto/hateBERT-freeze-all-sexism-detector"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)

inputs = tokenizer("Your cleaned tweet text here", return_tensors="pt")
outputs = model(**inputs)