| --- |
| 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`. |
