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]
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.
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2### from models.py
3from models import *
4tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two")
5model = Model_Rational_Label.from_pretrained("Hate-speech-CNERG/bert-base-uncased-hatexplain-rationale-two")
6inputs = tokenizer("He is a great guy", return_tensors="pt")
7prediction_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
Testing Data, Factors & Metrics
Testing Data
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Factors
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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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- Hours used: [More Information Needed]
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Technical Specifications [optional]
Model Architecture and Objective
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Software
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