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README.md

@@ -24,15 +24,15 @@ This model is a fine-tuned version of [microsoft/Florence-2-base](https://huggin
 - **High Recall**: Fine-tuned for 7 epochs with a focus on capturing every word in dense action scenes.
 
 ## 📊 Evaluation Results
-The model was evaluated against the base `Florence-2-base` model on a test set of 150 One Piece manga panels.
+The model was evaluated against the base `Florence-2-base` model on a test set of 150 One Piece manga panels. This version uses **Full Fine-Tuning (FFT)**.
 
-| Metric | Base Model | Fine-Tuned Model | Improvement |
+| Metric | Base Model | **Fine-Tuned (FFT)** | Total Improvement |
 | :--- | :--- | :--- | :--- |
-| **CER** (Character Error Rate) | 78.77% | **5.31%** | **+73.46 pts** |
-| **WER** (Word Error Rate) | 99.57% | **30.00%** | **+69.57 pts** |
+| **CER** (Character Error Rate) | 78.77% | **3.13%** | **+75.64 pts** |
+| **WER** (Word Error Rate) | 99.57% | **22.34%** | **+77.23 pts** |
 
-### Why the massive improvement?
-The base model frequently outputs text in uppercase and struggles with spacing in manga bubbles (e.g., `VOILA UNERÉACTIONDIGNE`). The fine-tuned model has learned the specific typography and layout of One Piece scans, correctly handling casing, punctuation, and word boundaries.
+### Why the upgrade to FFT?
+While LoRA was a good start, **Full Fine-Tuning** allowed the model to adapt its vision encoder specifically to the One Piece typography. This resulted in a significantly more robust model with near-perfect accuracy on speech bubbles.
 
 ## 🛠️ Usage (Python)
 

added_tokens.json

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config.json

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