jnadal14/Detoxification_LLM_Dataflow

Code to detect and translate African languages to English, perform sentiment analysis and detoxification if necessary. # COLX 565 Project – Agentic Detoxification Dataflow ## Group Repository: Tim and Jacob This repository contains the code, data, and analysis for our COLX 565 Final project focused on **agentic workflows for text sentiment analysis and detoxification**. The goal of this project is to explore and evaluate the effectiveness of an agentic dataflow approach to reduce toxicity in text while preserving meaning and sentiment. ## Project Structure ```plaintext detoxification_agentic_dataflow/ ├── agentic_workflow.ipynb # Main notebook outlining the detoxification workflow ├── Annotation_Statstics_notebook.ipynb # Analysis of annotation statistics ├── Detoxification Annotations.csv # Annotated detoxification dataset ├── results/ │ ├── sentiment_results.csv # Sentiment analysis results │ └── toxicity_results.csv # Toxicity analysis results ├── data/ │ ├── multilingual-sentiment-test-solutions.csv # Gold standard test set for multilingual sentiment │ └── toxic-test-solutions.csv # Gold standard test set for toxicity classification ``` ## Notebooks - **`agentic_workflow.ipynb`** Contains scripts to load and process the datasets. Employs UBC-NLP/toucan-base for text translation from African languages to English and ibm-granite/granite-3.2-2b-instruct for sentiment/toxicity classification, explanation generation, and detoxification. - **`Annotation_Statstics_notebook.ipynb`** Contains scripts to process `Detoxification Annotations.csv`, calculating different metrics for agreement, discussed further in `Final_Report.pdf`. ## Dataset - `multilingual-sentiment-test-solutions.csv`: Ground-truth data for evaluating multilingual sentiment analysis. - `toxic-test-solutions.csv`: Ground-truth data for evaluating toxicity classification accuracy. - `Detoxification Annotations.csv`: A subset of toxicity_results.csv containing rows identified as "toxic", with three appended columns; an LLM-generated detoxified version …