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language: en
license: mit
library_name: transformers
tags:
- sentiment-analysis
- classification
- from-scratch
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: CritiqueCore-v1
results:
- task:
type: text-classification
name: Sentiment Analysis
dataset:
name: imdb
type: imdb
metrics:
- type: accuracy
value: 0.9
pipeline_tag: text-classification
---
# 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 Million
- **Layers:** 2
- **Attention Heads:** 4
- **Hidden Dimension:** 256
- **Training 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)
Use `inference.py` from this repos' files list. Have fun :D |