CritiqueCore v1
CritiqueCore v1 is a compact Transformer model trained from scratch for sentiment analysis. Unlike models that use transfer learning, this model was initialized with random weights and learned the nuances of language (including sarcasm and basic cross-lingual sentiment) exclusively from the IMDb movie reviews dataset.
Model Description
Architecture: Custom Mini-Transformer (DistilBERT-based configuration)Parameters: ~9.06 MillionLayers: 2Attention Heads: 4Hidden Dimension: 256Training Data: IMDb Movie Reviews (25,000 samples)Training Duration: ~10 minutes on NVIDIA T4 GPU
Capabilities
Sentiment Detection: Strong performance on positive/negative English text.Sarcasm Awareness: Recognizes negative intent even when positive words are used (e.g., "CGI vomit").Robustness: Handles minor typos and maintains high confidence on structured feedback.
Limitations
Domain Specificity: Optimized for reviews. May struggle with complex multi-turn dialogues.Multilingual: While it shows some intuition for German, it was not explicitly trained on non-English data.
How to use (Inference Script)
First, you have to download CritiqueCore_v1_Model.zip and unpack it. Then, you can use inference.py from this repos' files list. Have fun :D
Examples
Example 1: Standard movie review
Input:
This movie was an absolute masterpiece! The acting was incredible and I loved every second.Output: POSITIVE (99.03% confidence)
Example 2: Sarcasm
Input:
Oh great, another superhero movie. Just what the world needed. I loved sitting through 3 hours of CGI vomit.Output: NEGATIVE (93.81% confidence)
Example 3: Negative question
Input:
Why did they even produce it?Output: NEGATIVE (99.37% confidence)
Training code
The full training code can be found in this repo as train.ipynb.
