---
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. This model is part of the ECAI paper - "Rationale-Guided Few-Shot Classification to Detect Abusive Language
" 


## 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]:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
### 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

## 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](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.

```python
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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[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]

[More Information Needed]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

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

[More Information Needed]

#### Metrics

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

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

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

[More Information Needed]

## 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

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## 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:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

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

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]