Update README.md

README.md

@@ -61,28 +61,6 @@ The model optimizes two matrices: an input embedding matrix $E$ and an output co
 
 > **Note:** The best mean training loss achieved a ~50% reduction from the initial loss. Epoch 49 was selected via early checkpointing as epoch 50 yielded a slightly higher loss value. These metrics validate implementation correctness on the training split and do not reflect generalization performance on unseen text.
 
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-
-## Implementation & Correctness Checks
-
-The model passed a rigorous suite of mathematical and algorithmic unit tests:
-
-- [x] **Data Processing:** Correct context-window pair generation with strict sentence-boundary protection.
-- [x] **Forward & Loss Verification:** Forward scores, softmax probabilities, and cross-entropy loss validated against manual reference calculations.
-- [x] **Analytical Gradients:** Exact match with expected symbolic gradients.
-- [x] **SGD Updates:** Verified parameter updates, ensuring non-context input embedding rows remain unmodified during sparse updates.
-- [x] **Numerical Stability:** Verified stability under extreme score magnitudes and shift invariance ($\text{softmax}(z) = \text{softmax}(z + c)$).
-- [x] **Gradient Checking:** Finite-difference numerical gradient verification ($\epsilon = 10^{-5}$).
-- [x] **Edge Cases:** Safe cosine similarity evaluation for zero-norm vectors.
-- [x] **Serialization:** Full JSON round-trip consistency for vocabulary mapping, weight arrays, and nearest-neighbor outputs.
-
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-
-## Usage
-
-This model uses a custom Python execution format rather than Hugging Face `transformers`. Download `amharic_skipgram.json` alongside your `model.py` script to run inference.
-
-### Python Code Example
+### Training Loss Curve
 
-```python
-from model import SkipGramModel
+The following plot illustrates the trajectory of the mean training loss recorded before training and after each epoch. The best model checkpoint was saved at epoch 49: