typo fix (#323)

ch06/02_bonus_additional-experiments/README.md

@@ -63,6 +63,6 @@ I've kept the LLM and dataset small on purpose, so you can run the training on a
 6. **Using a Model with Random Weights vs. Pretrained Weights (Row 1 and 5 vs. 9)**: Utilizing a model with random weights yields results that are only slightly worse (by 3% and 1.3%) compared to using pretrained weights.
 7. **Using LoRA (Low-Rank Adaptation) vs Training All Layers (Row 10 vs. 5)**: Keeping the model frozen and adding trainable LoRA layers (see [Appendix E](../../appendix-E/01_main-chapter-code/appendix-E.ipynb) for details) is a viable alternative to training all model parameters and even improves the performance by 1% point. As it can be seen by the ~1% lower gap between the training and validation accuracy when using LoRA, this is likely due to less overfitting. Moreover, using LoRA is also more memory-efficient because fewer parameters have to be updated.
 8. **Padding Input to Full Context Length vs. Longest Training Example (Row 1 vs. 11)**: Padding the input to the full supported context length results is significantly worse.
-9. **Padding vs no padding (Row 1 vs. 12 and 13)**: The `--no_padding` option disables the padding in the dataset, which requires training the model with a batch size of 1 since the inputs have variable lengths. This results in a better test accuracy but takes longer to train. In row 12, we additionally enable gradient accumulation with 8 steps to achieve the same batch size as in the other experiments, which helps reduce overfitting and slightly boost the test set accuracy.
+9. **Padding vs no padding (Row 1 vs. 12 and 13)**: The `--no_padding` option disables the padding in the dataset, which requires training the model with a batch size of 1 since the inputs have variable lengths. This results in a better test accuracy but takes longer to train. In row 13, we additionally enable gradient accumulation with 8 steps to achieve the same batch size as in the other experiments, which helps reduce overfitting and slightly boost the test set accuracy.
 10. **Disabling the causal attention mask (Row 1 vs. 14)**: Disables the causal attention mask used in the multi-head attention module. This means all tokens can attend all other tokens. The model accuracy is slightly improved compared to the GPT model with causal mask.
 11. **Ignoring the padding indices in the loss and backpropagation (Row 1 vs. 15)**: Setting `--ignore_index 50256` excludes the `|endoftext|` padding tokens in the `cross_entropy` loss function in PyTorch. In this case, it does not have any effect because we replaced the output layers so that the token IDs are either 0 or 1 for the binary classification example. However, this setting is useful when instruction finetuning models in chapter 7.