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Update model card with v2 training results (98.87% type accuracy)

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@@ -38,15 +38,44 @@ outputs = model.generate(**inputs, max_length=128)
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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  ```
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  ## Training Data
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  Derived from STEPBible TAGNT (Translators Amalgamated Greek NT), which marks each NT word
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  with its presence across 6 critical editions: NA27/28, Textus Receptus, SBLGNT, Byzantine,
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  Westcott-Hort, and THGNT.
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  ## Part of bible.systems
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- This is model 4 of 5 in the [bible.systems](https://bible.systems) ML pipeline.
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  ---
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  *For God so loved the world...* — John 3:16
 
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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  ```
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+ ## Results (v2)
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+
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+ Trained on Apple M4 Pro (MPS), mT5-small (300M params), 9 epochs:
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+
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+ | Metric | Score |
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+ |---|---|
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+ | Eval loss (best) | **0.0868** (epoch 9) |
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+ | Variant type accuracy | **98.87%** |
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+ | Exact match (full output) | 37.5% |
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+ | Training steps | 9,558 |
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+
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+ ### Per-Epoch Progression
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+
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+ | Epoch | Loss | Exact Match | Type Accuracy |
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+ |-------|------|-------------|---------------|
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+ | 1 | 0.8033 | 0% | 69.3% |
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+ | 2 | 0.2577 | 1.0% | 67.5% |
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+ | 3 | 0.1465 | 12.1% | 94.5% |
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+ | 4 | 0.1159 | 25.9% | 96.0% |
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+ | 5 | 0.1039 | 29.3% | 98.3% |
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+ | 6 | 0.0963 | 32.7% | 98.8% |
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+ | 7 | 0.0914 | 34.7% | 97.8% |
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+ | 8 | 0.0888 | 35.6% | 98.8% |
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+ | 9 | 0.0868 | **37.5%** | **98.9%** |
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+
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  ## Training Data
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  Derived from STEPBible TAGNT (Translators Amalgamated Greek NT), which marks each NT word
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  with its presence across 6 critical editions: NA27/28, Textus Receptus, SBLGNT, Byzantine,
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  Westcott-Hort, and THGNT.
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+ - Training set: 8,497 examples
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+ - Validation set: 1,062 examples
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+ - Test set: 1,062 examples
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+
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  ## Part of bible.systems
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+ This is model 4 of 8 in the [bible.systems](https://bible.systems) ML pipeline.
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  ---
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  *For God so loved the world...* — John 3:16