|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: my_awesome_model |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# my_awesome_model |
|
|
|
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.8419 |
|
- Accuracy: 0.8623 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| No log | 1.0 | 84 | 0.4239 | 0.8353 | |
|
| No log | 2.0 | 168 | 0.4692 | 0.8443 | |
|
| No log | 3.0 | 252 | 0.5098 | 0.8503 | |
|
| No log | 4.0 | 336 | 0.6306 | 0.8533 | |
|
| No log | 5.0 | 420 | 0.6482 | 0.8503 | |
|
| 0.0947 | 6.0 | 504 | 0.6836 | 0.8713 | |
|
| 0.0947 | 7.0 | 588 | 0.7543 | 0.8563 | |
|
| 0.0947 | 8.0 | 672 | 0.7751 | 0.8683 | |
|
| 0.0947 | 9.0 | 756 | 0.8317 | 0.8593 | |
|
| 0.0947 | 10.0 | 840 | 0.8653 | 0.8593 | |
|
| 0.0947 | 11.0 | 924 | 0.8512 | 0.8683 | |
|
| 0.0031 | 12.0 | 1008 | 0.8374 | 0.8713 | |
|
| 0.0031 | 13.0 | 1092 | 0.8369 | 0.8653 | |
|
| 0.0031 | 14.0 | 1176 | 0.8415 | 0.8653 | |
|
| 0.0031 | 15.0 | 1260 | 0.8419 | 0.8623 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.28.1 |
|
- Pytorch 2.0.0+cu118 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
|
|
