| | --- |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - toy_graph |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: output_toy |
| | results: |
| | - task: |
| | name: Causal Language Modeling |
| | type: text-generation |
| | dataset: |
| | name: toy_graph |
| | type: toy_graph |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.4525254617525837 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # output_toy |
| | |
| | This model is a fine-tuned version of [toy/model](https://huggingface.co/toy/model) on the toy_graph dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.2691 |
| | - Accuracy: 0.4525 |
| | - Transition Accuracy: 0.5634 |
| | - First Transition Accuracy: 0.88 |
| | - Multicode K: 1 |
| | - Dead Code Fraction/layer0: 0.9969 |
| | - Mse/layer0: 220380.4595 |
| | - Input Norm/layer0: 333.7717 |
| | - Output Norm/layer0: 12.9360 |
| | - Dead Code Fraction/layer1: 0.9535 |
| | - Mse/layer1: 132.7843 |
| | - Input Norm/layer1: 6.5450 |
| | - Output Norm/layer1: 13.1449 |
| | - Dead Code Fraction/layer2: 0.9349 |
| | - Mse/layer2: 365.9396 |
| | - Input Norm/layer2: 6.1370 |
| | - Output Norm/layer2: 18.3248 |
| | - Dead Code Fraction/layer3: 0.9819 |
| | - Mse/layer3: 415.9804 |
| | - Input Norm/layer3: 7.4097 |
| | - Output Norm/layer3: 18.4665 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 0.001 |
| | - train_batch_size: 1024 |
| | - eval_batch_size: 512 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: constant |
| | - training_steps: 20000 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Transition Accuracy | First Transition Accuracy | Multicode K | Dead Code Fraction/layer0 | Mse/layer0 | Input Norm/layer0 | Output Norm/layer0 | Dead Code Fraction/layer1 | Mse/layer1 | Input Norm/layer1 | Output Norm/layer1 | Dead Code Fraction/layer2 | Mse/layer2 | Input Norm/layer2 | Output Norm/layer2 | Dead Code Fraction/layer3 | Mse/layer3 | Input Norm/layer3 | Output Norm/layer3 | |
| | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------------:|:-------------------------:|:-----------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:| |
| | | 2.2465 | 0.03 | 500 | 1.8386 | 0.3565 | 0.3555 | 0.31 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.5981 | 0.05 | 1000 | 1.4652 | 0.4204 | 0.5015 | 0.58 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.3928 | 0.07 | 1500 | 1.3541 | 0.4378 | 0.555 | 0.79 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.3405 | 0.1 | 2000 | 1.3264 | 0.4427 | 0.5756 | 0.82 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.3189 | 0.12 | 2500 | 1.3187 | 0.4446 | 0.5576 | 0.86 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.308 | 0.15 | 3000 | 1.3064 | 0.4468 | 0.5573 | 0.82 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.3009 | 0.17 | 3500 | 1.2963 | 0.4493 | 0.5763 | 0.87 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2965 | 0.2 | 4000 | 1.2922 | 0.4494 | 0.5677 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2919 | 0.23 | 4500 | 1.2880 | 0.4499 | 0.5821 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2889 | 0.25 | 5000 | 1.2856 | 0.4501 | 0.56 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2855 | 0.28 | 5500 | 1.2816 | 0.4503 | 0.6016 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2828 | 0.3 | 6000 | 1.2844 | 0.4502 | 0.5734 | 0.87 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2805 | 0.33 | 6500 | 1.2777 | 0.4516 | 0.6084 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2793 | 0.35 | 7000 | 1.2796 | 0.4511 | 0.5681 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2785 | 0.38 | 7500 | 1.2748 | 0.4519 | 0.5919 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2764 | 0.4 | 8000 | 1.2767 | 0.4518 | 0.5760 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2763 | 0.42 | 8500 | 1.2801 | 0.4507 | 0.5827 | 0.94 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2755 | 0.45 | 9000 | 1.2755 | 0.4516 | 0.5765 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2746 | 0.47 | 9500 | 1.2736 | 0.4523 | 0.5865 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2734 | 0.5 | 10000 | 1.2740 | 0.4519 | 0.5779 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2732 | 0.53 | 10500 | 1.2744 | 0.4516 | 0.5879 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2723 | 0.55 | 11000 | 1.2690 | 0.4525 | 0.5811 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2712 | 0.57 | 11500 | 1.2705 | 0.4526 | 0.5779 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2716 | 0.6 | 12000 | 1.2701 | 0.4527 | 0.5760 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2708 | 0.62 | 12500 | 1.2716 | 0.4522 | 0.5485 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2705 | 0.65 | 13000 | 1.2676 | 0.4529 | 0.5734 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2696 | 0.68 | 13500 | 1.2717 | 0.4519 | 0.5994 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2687 | 0.7 | 14000 | 1.2687 | 0.4524 | 0.5756 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2685 | 0.72 | 14500 | 1.2709 | 0.4521 | 0.6127 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2685 | 0.75 | 15000 | 1.2706 | 0.4519 | 0.5873 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2675 | 0.78 | 15500 | 1.2691 | 0.4527 | 0.6365 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2677 | 0.8 | 16000 | 1.2686 | 0.4526 | 0.5589 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2676 | 0.82 | 16500 | 1.2639 | 0.4529 | 0.5940 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2662 | 0.85 | 17000 | 1.2655 | 0.4530 | 0.5955 | 0.94 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2666 | 0.88 | 17500 | 1.2636 | 0.4526 | 0.6013 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2664 | 0.9 | 18000 | 1.2681 | 0.4526 | 0.6034 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.266 | 0.93 | 18500 | 1.2624 | 0.4527 | 0.5839 | 0.88 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2653 | 0.95 | 19000 | 1.2688 | 0.4519 | 0.5837 | 0.92 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2654 | 0.97 | 19500 | 1.2619 | 0.4534 | 0.5973 | 0.92 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| | | 1.2649 | 1.0 | 20000 | 1.2647 | 0.4525 | 0.59 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.1 |
| | - Pytorch 2.0.1+cu117 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| |
|
