---
license: bigscience-openrail-m
datasets:
- hatexplain
language:
- en
metrics:
- f1
- accuracy
tags:
- social science
- hatespeech
- rationale_predictions
---
# Model Card for Rationale Predictor

<!-- Provide a quick summary of what the model is/does. -->

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

<!-- Provide a longer summary of what this model is. -->


- **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]

<!-- Provide the basic links for the model. -->

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

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->




[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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