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Hate-speech-CNERG
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Rationale_predictor

Transformers
PyTorch
English
bert
social science
hatespeech
rationale_predictions
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Rationale_predictor / README.md
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metadata
license: bigscience-openrail-m
datasets:
  - hatexplain
language:
  - en
metrics:
  - f1
  - accuracy
tags:
  - social science
  - hatespeech
  - rationale_predictions

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.

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'])

[More Information Needed]

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

  • Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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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).

  • Hardware Type: [More Information Needed]
  • Hours used: [More Information Needed]
  • Cloud Provider: [More Information Needed]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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