Model: psyrishi/affectivelens-emotion-models
Author: psyrishi
Pipeline: text-classification
Library: scikit-learn
Tags: scikit-learn, emotion-classification, text-classification, affective-computing, xgboost, lightgbm, en, dataset:psyrishi/MoodPulse, license:mit, region:us
Card metadata: license: mit
language: en
library_name: scikit-learn
tags: emotion-classification, text-classification, affective-computing, scikit-learn, xgboost, lightgbm
model_description: title: Pre-Trained Emotion Classifiers for AffectiveLens
text: This repository contains a collection of 6 machine learning models trained for the AffectiveLens Project. Each model is a classifier designed to predict the emotional valence (Positive, Negative, or Neutral) of a given text.
These models were trained on 768-dimensional sentence embeddings generated by the distilbert-base-uncased model. The source text data comes from the GoEmotions dataset, which was processed and is available at psyrishi/MoodPulse.
This repository allows you to easily download and use the final, trained artifacts of the study without needing to retrain them.

models: name: LightGBM_MicroF1_0.6240.pkl
description: Champion Model, name: XGBoost_MicroF1_0.6205.pkl, name: Random_Forest_MicroF1_0.6192.pkl, name: CatBoost_MicroF1_0.6175.pkl, name: Logistic_Regression_MicroF1_0.6026.pkl, name: Linear_SVM_MicroF1_0.6005.pkl
training_data: source: GoEmotions dataset
repository: psyrishi/MoodPulse
evaluation: champion_model: LightGBM
f1_score_micro: 0.6223
accuracy: 62.23%
citation: paper: authors: Demszky, Dorottya, Movshovitz-Attias, Dana, Ko, Jeongwoo, Cowen, Alan, Nemade, Gaurav, Ravi, Sujith
title: GoEmotions:A Dataset of Fine-Grained Emotions
conference: 58th Annual Meeting of the Association for Computational Linguistics (ACL)
year: 2020
datasets: psyrishi/MoodPulse
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Created: 2025-08-06 16:37:15