---
datasets:
- google/jigsaw_toxicity_pred
language:
- en
base_model:
- FacebookAI/roberta-base
metrics:
- accuracy
pipeline_tag: text-classification
library_name: transformers
---
# Model Card for Roberta-toxic

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



**RoBERTa-toxic: A Robust Toxicity Prediction Model**

RoBERTa-toxic leverages the power of the RoBERTa (Robustly Optimized BERT Pretraining Approach) transformer model to analyze text inputs and predict an array of toxicity categories. Fine-tuned for identifying nuanced toxic behaviors such as hate speech, harassment, profanity, and harmful stereotypes, it delivers accurate, context-aware predictions. The model is tailored for applications like content moderation, social media analysis, and safe online interactions, providing multi-label outputs for comprehensive toxicity profiling.


## Model Details

### Model Description

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- **Developed by:** ESIEA Students 
- **Shared by [optional]:** ESIEA Students
- **Model type:** Roberta with additionnal layer to predict array of booleans
- **Language(s) (NLP):** English
- **Finetuned from model [optional]:** Roberta

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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The model can be used to classify text based on their toxicities

### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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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We did train the model on the googleJigSaw toxic dataset as mentionned above on the 150k comments


[More Information Needed]

### Training Procedure

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we trained

#### Preprocessing [optional]

we only did some basic data-cleaning

[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]

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training time 4hours on a gtx 1050TI GPU on 3 epochs

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

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#### Metrics

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Accuracy of : **90%**

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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:** GTX 1050 TI
- **Hours used:** 4 HOURS

## 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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We did use torch

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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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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