add BERT experiment results (#333)

* add BERT experiment results

* cleanup

* formatting

ch06/02_bonus_additional-experiments/README.md

@@ -18,7 +18,7 @@ For example,
 | 5    | gpt2-small (124M)  | pretrained | last                     | all              | longest train ex. (120)                                | 99.62%       | 96.64%         | 96.67%   | 0.69 min      | A100    |
 | 6    | gpt2-medium (355M) | pretrained | last                     | last_block       | longest train ex. (120)                                | 87.50%       | 91.28%         | 84.67%   | 0.75 min      | A100    |
 | 7    | gpt2-large (774M)  | pretrained | last                     | last_block       | longest train ex. (120)                                | 99.52%       | 98.66%         | 96.67%   | 1.50 min      | A100    |
-| 8    | gpt2-xl (1558M)    | pretrained | last                     | last_block       | longest train ex. (120)                                | 99.81%       | 99.33%         | 98.33%   | 2.83 min      | A100    |
+| 8    | gpt2-xl (1558M)    | pretrained | last                     | last_block       | longest train ex. (120)                                | 99.81%       | 99.81%         | 98.33%   | 2.83 min      | A100    |
 | 9    | gpt2-xl (1558M)    | pretrained | last                     | all              | longest train ex. (120)                                | 100.00%      | 98.66%         | 98.67%   | 8.12 min      | A100    |
 | 10   | gpt2-small (124M)  | random     | last                     | all              | longest train ex. (120)                                | 100.00%      | 96.64%         | 93.67%   | 0.69 min      | A100    |
 | 11   | gpt2-small (124M)  | pretrained | last                     | LoRA             | longest train ex. (120)                                | 100.00%      | 97.32%         | 96.67%   | 0.75 min      | A100    |
@@ -35,23 +35,23 @@ For example,
 
 You can use the following code to reproduce the experiments:
 
-- Row 1: `python additional-experiments.py`
-- Row 2: `python additional-experiments.py --trainable_token_pos first`
-- Row 3: `python additional-experiments.py --trainable_layers last_layer`
-- Row 4: `python additional-experiments.py --trainable_layers last_two_blocks`
-- Row 5: `python additional-experiments.py --trainable_layers all`
-- Row 6: `python additional-experiments.py --model_size "gpt2-medium (355M)"`
-- Row 7: `python additional-experiments.py --model_size "gpt2-large (774M)"`
-- Row 8: `python additional-experiments.py --model_size "gpt2-xl (1558M)"`
-- Row 9: `python additional-experiments.py --model_size "gpt2-xl (1558M)"--trainable_layers all`
-- Row 10: `python additional-experiments.py --weights random --trainable_layers all`
-- Row 11: `python additional-experiments.py --trainable_layers lora --lora_rank 16 --lora_alpha 16`
-- Row 12: `python additional-experiments.py --trainable_layers lora --lora_rank 16 --lora_alpha 8 --model_size "gpt2-xl (1558M)"`
-- Row 13: `python additional-experiments.py --context_length "model_context_length"`
-- Row 14: `python additional-experiments.py --no_padding --batch_size 1`
-- Row 15: `python additional-experiments.py --no_padding --batch_size 1 --accumulation_steps 8`
-- Row 16: `python additional-experiments.py --disable_causal_mask`
-- Row 17: `python additional-experiments.py --ignore_index 50256`
+- Row 1: `python additional_experiments.py`
+- Row 2: `python additional_experiments.py --trainable_token_pos first`
+- Row 3: `python additional_experiments.py --trainable_layers last_layer`
+- Row 4: `python additional_experiments.py --trainable_layers last_two_blocks`
+- Row 5: `python additional_experiments.py --trainable_layers all`
+- Row 6: `python additional_experiments.py --model_size "gpt2-medium (355M)"`
+- Row 7: `python additional_experiments.py --model_size "gpt2-large (774M)"`
+- Row 8: `python additional_experiments.py --model_size "gpt2-xl (1558M)"`
+- Row 9: `python additional_experiments.py --model_size "gpt2-xl (1558M)"--trainable_layers all`
+- Row 10: `python additional_experiments.py --weights random --trainable_layers all`
+- Row 11: `python additional_experiments.py --trainable_layers lora --lora_rank 16 --lora_alpha 16`
+- Row 12: `python additional_experiments.py --trainable_layers lora --lora_rank 16 --lora_alpha 8 --model_size "gpt2-xl (1558M)"`
+- Row 13: `python additional_experiments.py --context_length "model_context_length"`
+- Row 14: `python additional_experiments.py --no_padding --batch_size 1`
+- Row 15: `python additional_experiments.py --no_padding --batch_size 1 --accumulation_steps 8`
+- Row 16: `python additional_experiments.py --disable_causal_mask`
+- Row 17: `python additional_experiments.py --ignore_index 50256`
 
 I've kept the LLM and dataset small on purpose, so you can run the training on a regular laptop like a MacBook Air M3 in about 15 minutes (for the default setting) in case you don't have access to a GPU.
 
@@ -60,7 +60,7 @@ I've kept the LLM and dataset small on purpose, so you can run the training on a
 ### Interpretation
 
 1. **Training the Last vs. First Output Token Position (Row 1 vs. 2)**: Training the last output token position results in substantially better performance compared to the first. This improvement is expected due to the causal self-attention mask.
-2. **Training the Last Transformer Block vs. Last Layer (Row 1 vs. 3)**: Training the entire last transformer block also results in substantially better results than training only the last layer.
+2. **Training the Last Transformer Block vs. Last Layer (Row 1 vs. 3)**: Training the entire last transformer block is also results in substantially better results than training only the last layer.
 3. **Training the Last vs. Last Two Last Transformer Blocks (Row 1 vs. 4)**: Training the two last transformer blocks instead of only the last block results in a noticeable 3.33% accuracy boost.
 4. **Training Last Transformer Block vs All Layers (Row 1 vs. 5)**: Training all layers shows a modest improvement of ~2% over just training the last transformer block, but it requires almost three times longer in terms of training duration. Also, it does not perform as well as training only the last two out of 12 transformer blocks.
 5. **Using Larger Pretrained Models (Row 1 vs 6, and Row 1 vs. 7 and 8)**: Employing a 3x larger pretrained model leads to worse results. However, using a 5x larger model improves performance compared to the initial model, as was anticipated. Similarly, the 12x larger model improves the predictive performance even further. (The medium model was perhaps not well pretrained or the particular finetuning configuration works not as well for this model.)