---
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)
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:**
```plaintext
This movie was an absolute masterpiece! The acting was incredible and I loved every second.
```
**Output:** POSITIVE (99.03% confidence)

### Example 2: Sarcasm
**Input:**
```plaintext
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:**
```plaintext
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`.