Hate-speech-CNERG/Rationale_predictor

Model Card for Rationale Predictor

This model provides the class labels either toxic or not toxic as well as the rationales predicted which indicates the explanation of why something as toxic. This model is part of the ECAI paper - "Rationale-Guided Few-Shot Classification to Detect Abusive Language "

Model Details

Model Description

• Developed by: Punyajoy Saha
• Model type: bert-base-uncased
• Language(s) (NLP): english
• Finetuned from model [optional]: See the BERT base uncased model for more information about the BERT base model.

Model Sources [optional]

• Repository: https://github.com/punyajoy/RGFS_ECAI
• Paper [optional]: https://arxiv.org/abs/2211.17046

Uses

Direct Use

This model can be directly used to predict some post as toxic/non-toxic and predicting the rationales behind it

How to Get Started with the Model

Use the code below to get started with the model.

Please use the Model_Rational_Label class inside models.py to load the models. The default prediction in this hosted inference API may be wrong due to the use of different class initialisations.

from transformers import AutoTokenizer, AutoModelForSequenceClassification
### from models.py
from models import *
tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two")
model = Model_Rational_Label.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two")
inputs = tokenizer("He is a great guy", return_tensors="pt")
prediction_logits, _ = model(input_ids=inputs['input_ids'],attention_mask=inputs['attention_mask'])

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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