|
|
--- |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
datasets: |
|
|
- roneneldan/TinyStories |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: gpt2_u100_tiny-stories_1024_dpos |
|
|
results: |
|
|
- task: |
|
|
name: Causal Language Modeling |
|
|
type: text-generation |
|
|
dataset: |
|
|
name: roneneldan/TinyStories |
|
|
type: roneneldan/TinyStories |
|
|
metrics: |
|
|
- name: Accuracy |
|
|
type: accuracy |
|
|
value: 0.6900891972857721 |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/scads-nlp/morph-gpt_gpt2_tiny-stories_dpos/runs/q6cikc5d) |
|
|
# gpt2_u100_tiny-stories_1024_dpos |
|
|
|
|
|
This model is a fine-tuned version of [](https://huggingface.co/) on the roneneldan/TinyStories dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 1.1688 |
|
|
- Accuracy: 0.6901 |
|
|
|
|
|
## 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: 5e-05 |
|
|
- train_batch_size: 32 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 42 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 1.0 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|
|:-------------:|:------:|:-----:|:---------------:|:--------:| |
|
|
| 2.8305 | 0.0505 | 1000 | 2.3790 | 0.4650 | |
|
|
| 1.9272 | 0.1009 | 2000 | 1.7501 | 0.5809 | |
|
|
| 1.6793 | 0.1514 | 3000 | 1.5689 | 0.6136 | |
|
|
| 1.5605 | 0.2018 | 4000 | 1.4699 | 0.6316 | |
|
|
| 1.4891 | 0.2523 | 5000 | 1.4112 | 0.6422 | |
|
|
| 1.4391 | 0.3028 | 6000 | 1.3646 | 0.6514 | |
|
|
| 1.3995 | 0.3532 | 7000 | 1.3317 | 0.6575 | |
|
|
| 1.3707 | 0.4037 | 8000 | 1.3021 | 0.6631 | |
|
|
| 1.3424 | 0.4541 | 9000 | 1.2806 | 0.6675 | |
|
|
| 1.3242 | 0.5046 | 10000 | 1.2613 | 0.6714 | |
|
|
| 1.3058 | 0.5551 | 11000 | 1.2435 | 0.6748 | |
|
|
| 1.2888 | 0.6055 | 12000 | 1.2291 | 0.6777 | |
|
|
| 1.2748 | 0.6560 | 13000 | 1.2178 | 0.6801 | |
|
|
| 1.2654 | 0.7064 | 14000 | 1.2077 | 0.6821 | |
|
|
| 1.2549 | 0.7569 | 15000 | 1.1964 | 0.6843 | |
|
|
| 1.2459 | 0.8073 | 16000 | 1.1878 | 0.6860 | |
|
|
| 1.2385 | 0.8578 | 17000 | 1.1819 | 0.6873 | |
|
|
| 1.2286 | 0.9083 | 18000 | 1.1756 | 0.6886 | |
|
|
| 1.2246 | 0.9587 | 19000 | 1.1708 | 0.6896 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.42.3 |
|
|
- Pytorch 2.2.2+cu121 |
|
|
- Datasets 2.20.0 |
|
|
- Tokenizers 0.19.1 |
|
|
|
