|
--- |
|
license: other |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- AlekseyKorshuk/dalio-all-io |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: dalio-all-io-1.3b-3-epoch |
|
results: |
|
- task: |
|
name: Causal Language Modeling |
|
type: text-generation |
|
dataset: |
|
name: AlekseyKorshuk/dalio-all-io |
|
type: AlekseyKorshuk/dalio-all-io |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.05841094794583167 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# dalio-all-io-1.3b-3-epoch |
|
|
|
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/dalio-all-io dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.3008 |
|
- Accuracy: 0.0584 |
|
|
|
## 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: 3e-05 |
|
- train_batch_size: 2 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 8 |
|
- total_train_batch_size: 16 |
|
- total_eval_batch_size: 16 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- num_epochs: 3.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 2.6543 | 0.03 | 1 | 2.6113 | 0.0513 | |
|
| 2.6077 | 0.07 | 2 | 2.6113 | 0.0513 | |
|
| 2.5964 | 0.1 | 3 | 2.5605 | 0.0519 | |
|
| 2.7302 | 0.14 | 4 | 2.5234 | 0.0526 | |
|
| 2.7004 | 0.17 | 5 | 2.5078 | 0.0529 | |
|
| 2.5681 | 0.21 | 6 | 2.4941 | 0.0532 | |
|
| 2.6404 | 0.24 | 7 | 2.4883 | 0.0534 | |
|
| 2.5325 | 0.28 | 8 | 2.4805 | 0.0536 | |
|
| 2.7205 | 0.31 | 9 | 2.4746 | 0.0536 | |
|
| 2.5149 | 0.34 | 10 | 2.4648 | 0.0533 | |
|
| 2.5017 | 0.38 | 11 | 2.4512 | 0.0535 | |
|
| 2.7026 | 0.41 | 12 | 2.4395 | 0.0539 | |
|
| 2.5259 | 0.45 | 13 | 2.4316 | 0.0543 | |
|
| 2.563 | 0.48 | 14 | 2.4219 | 0.0546 | |
|
| 2.5679 | 0.52 | 15 | 2.4141 | 0.0550 | |
|
| 2.3701 | 0.55 | 16 | 2.4082 | 0.0551 | |
|
| 2.4739 | 0.59 | 17 | 2.4082 | 0.0551 | |
|
| 2.481 | 0.62 | 18 | 2.4023 | 0.0548 | |
|
| 2.5795 | 0.66 | 19 | 2.3945 | 0.0549 | |
|
| 2.4902 | 0.69 | 20 | 2.3867 | 0.0549 | |
|
| 2.4509 | 0.72 | 21 | 2.3809 | 0.0551 | |
|
| 2.6052 | 0.76 | 22 | 2.3730 | 0.0553 | |
|
| 2.3323 | 0.79 | 23 | 2.3633 | 0.0555 | |
|
| 2.5994 | 0.83 | 24 | 2.3555 | 0.0556 | |
|
| 2.3347 | 0.86 | 25 | 2.3477 | 0.0556 | |
|
| 2.421 | 0.9 | 26 | 2.3398 | 0.0559 | |
|
| 2.5337 | 0.93 | 27 | 2.3359 | 0.0560 | |
|
| 2.4102 | 0.97 | 28 | 2.3320 | 0.0563 | |
|
| 2.4309 | 1.0 | 29 | 2.3262 | 0.0564 | |
|
| 1.9305 | 1.03 | 30 | 2.3223 | 0.0564 | |
|
| 1.8601 | 1.07 | 31 | 2.3203 | 0.0567 | |
|
| 1.8682 | 1.1 | 32 | 2.3281 | 0.0564 | |
|
| 1.8657 | 1.14 | 33 | 2.3535 | 0.0564 | |
|
| 2.063 | 1.17 | 34 | 2.3398 | 0.0567 | |
|
| 1.6443 | 1.21 | 35 | 2.3242 | 0.0568 | |
|
| 1.7592 | 1.24 | 36 | 2.3164 | 0.0569 | |
|
| 1.8981 | 1.28 | 37 | 2.3105 | 0.0569 | |
|
| 1.9379 | 1.31 | 38 | 2.3047 | 0.0573 | |
|
| 1.6008 | 1.34 | 39 | 2.3027 | 0.0574 | |
|
| 1.595 | 1.38 | 40 | 2.3027 | 0.0575 | |
|
| 1.7096 | 1.41 | 41 | 2.3027 | 0.0575 | |
|
| 1.7245 | 1.45 | 42 | 2.3027 | 0.0576 | |
|
| 1.795 | 1.48 | 43 | 2.3008 | 0.0577 | |
|
| 1.7241 | 1.52 | 44 | 2.3008 | 0.0576 | |
|
| 1.6356 | 1.55 | 45 | 2.2988 | 0.0576 | |
|
| 1.77 | 1.59 | 46 | 2.2969 | 0.0576 | |
|
| 1.6675 | 1.62 | 47 | 2.2930 | 0.0577 | |
|
| 1.6929 | 1.66 | 48 | 2.2910 | 0.0577 | |
|
| 1.6635 | 1.69 | 49 | 2.2910 | 0.0576 | |
|
| 1.6093 | 1.72 | 50 | 2.2910 | 0.0578 | |
|
| 1.7362 | 1.76 | 51 | 2.2891 | 0.0580 | |
|
| 1.7015 | 1.79 | 52 | 2.2852 | 0.0581 | |
|
| 1.9515 | 1.83 | 53 | 2.2812 | 0.0582 | |
|
| 1.6494 | 1.86 | 54 | 2.2773 | 0.0580 | |
|
| 1.7522 | 1.9 | 55 | 2.2734 | 0.0580 | |
|
| 1.7369 | 1.93 | 56 | 2.2676 | 0.0581 | |
|
| 1.6528 | 1.97 | 57 | 2.2637 | 0.0581 | |
|
| 1.51 | 2.0 | 58 | 2.2617 | 0.0583 | |
|
| 1.4579 | 2.03 | 59 | 2.2637 | 0.0585 | |
|
| 1.2645 | 2.07 | 60 | 2.2695 | 0.0585 | |
|
| 1.2424 | 2.1 | 61 | 2.2773 | 0.0584 | |
|
| 1.2117 | 2.14 | 62 | 2.2891 | 0.0584 | |
|
| 1.4059 | 2.17 | 63 | 2.3008 | 0.0580 | |
|
| 1.328 | 2.21 | 64 | 2.3145 | 0.0581 | |
|
| 1.3436 | 2.24 | 65 | 2.3281 | 0.0580 | |
|
| 1.389 | 2.28 | 66 | 2.3379 | 0.0580 | |
|
| 1.2127 | 2.31 | 67 | 2.3398 | 0.0580 | |
|
| 1.3645 | 2.34 | 68 | 2.3418 | 0.0581 | |
|
| 1.3389 | 2.38 | 69 | 2.3379 | 0.0581 | |
|
| 1.2549 | 2.41 | 70 | 2.3320 | 0.0581 | |
|
| 1.2193 | 2.45 | 71 | 2.3281 | 0.0582 | |
|
| 1.3617 | 2.48 | 72 | 2.3223 | 0.0583 | |
|
| 1.2336 | 2.52 | 73 | 2.3184 | 0.0583 | |
|
| 1.179 | 2.55 | 74 | 2.3145 | 0.0583 | |
|
| 1.2468 | 2.59 | 75 | 2.3125 | 0.0583 | |
|
| 1.3325 | 2.62 | 76 | 2.3086 | 0.0583 | |
|
| 1.1471 | 2.66 | 77 | 2.3066 | 0.0583 | |
|
| 1.3123 | 2.69 | 78 | 2.3066 | 0.0583 | |
|
| 1.3285 | 2.72 | 79 | 2.3047 | 0.0585 | |
|
| 1.3232 | 2.76 | 80 | 2.3027 | 0.0584 | |
|
| 1.1228 | 2.79 | 81 | 2.3027 | 0.0584 | |
|
| 1.3524 | 2.83 | 82 | 2.3027 | 0.0584 | |
|
| 1.2042 | 2.86 | 83 | 2.3027 | 0.0583 | |
|
| 1.3588 | 2.9 | 84 | 2.3008 | 0.0583 | |
|
| 1.2982 | 2.93 | 85 | 2.3008 | 0.0584 | |
|
| 1.4373 | 2.97 | 86 | 2.3008 | 0.0585 | |
|
| 1.3562 | 3.0 | 87 | 2.3008 | 0.0584 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.25.0.dev0 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
