Create README.md

README.md

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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