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.
Model Details
Model Description
- Developed by: Punyajoy Saha
- Model type: bert-base-uncased
- Language(s) (NLP): english
- Finetuned from model [optional]: bert-base-uncased using model.py folder's class "Model_Rationale_Label"
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
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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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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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).
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- Hours used: [More Information Needed]
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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]
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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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