Model Card for Roberta-toxic

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

• 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]

• Repository: [More Information Needed]
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• Demo [optional]: [More Information Needed]

Uses

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

Direct Use

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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

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

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Training Procedure

we trained

Preprocessing [optional]

we only did some basic data-cleaning

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

Accuracy of : 90%

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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: 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]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Contact

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