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video-game-sentiment-model
emiemimi/video-game-sentiment-model
video-game-sentiment-model is a text classification model from emiemimi. Use it when you need a label for a piece of text. It is set up for pytorch. The card lists the license as mit.
This repository contains PyTorch classifiers trained on Qwen3 0.6B embeddings for gaming-community sentiment classification.
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Public
Updated May 17, 2026
Repo size
1.1 MB
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At a glance
- Task
- Text Classification
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- May 12, 2026
- Updated
- May 17, 2026
- SHA
- 84a2f112
Try a prompt
Model card
From the Hugging Face model README
Video Game Sentiment Model
This repository contains PyTorch classifiers trained on Qwen3 0.6B embeddings for gaming-community sentiment classification.
Live Hugging Face model page: emiemimi/video-game-sentiment-model
Task
Input: a gaming-related comment.
Output sentiment:
positivenegativemixedneutral
Training
- Dataset: balanced 10k gaming Reddit comments
- Embedding model:
qwen3-embedding:0.6b - Embedding dimension: 1,024
- Split: 70% train, 15% dev, 15% test
- Classifiers:
- Gaussian Naive Bayes implemented with PyTorch tensors
- Feed-forward neural network implemented with vanilla PyTorch
Results
| Model | Test Accuracy | Test Macro F1 | Test Weighted F1 |
|---|---|---|---|
| Gaussian Naive Bayes | 0.5200 | 0.5176 | 0.5179 |
| Neural Network | 0.6000 | 0.6006 | 0.6016 |
The neural network checkpoint is the recommended model.
Limitations
Labels were created with LLM assistance and only minimal human review. The model may have learned from noisy labels, which likely contributes to modest performance.
- Task
- Text Classification
- Library
- pytorch
- License
- mit
- Created
- May 12, 2026
- Updated
- May 17, 2026