|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
base_model: distilbert-base-uncased |
|
model-index: |
|
- name: distilbert-base-uncased_fold_1_ternary |
|
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. --> |
|
|
|
# distilbert-base-uncased_fold_1_ternary |
|
|
|
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: 2.0582 |
|
- F1: 0.7326 |
|
|
|
## 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: 2e-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: 25 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| No log | 1.0 | 290 | 0.5524 | 0.6755 | |
|
| 0.5648 | 2.0 | 580 | 0.5654 | 0.7124 | |
|
| 0.5648 | 3.0 | 870 | 0.6547 | 0.6896 | |
|
| 0.2712 | 4.0 | 1160 | 0.8916 | 0.7263 | |
|
| 0.2712 | 5.0 | 1450 | 1.1187 | 0.7120 | |
|
| 0.1147 | 6.0 | 1740 | 1.2778 | 0.7114 | |
|
| 0.0476 | 7.0 | 2030 | 1.4441 | 0.7151 | |
|
| 0.0476 | 8.0 | 2320 | 1.5535 | 0.7133 | |
|
| 0.0187 | 9.0 | 2610 | 1.6439 | 0.7212 | |
|
| 0.0187 | 10.0 | 2900 | 1.7261 | 0.7313 | |
|
| 0.0138 | 11.0 | 3190 | 1.6806 | 0.7292 | |
|
| 0.0138 | 12.0 | 3480 | 1.8425 | 0.7111 | |
|
| 0.009 | 13.0 | 3770 | 1.9207 | 0.7213 | |
|
| 0.0045 | 14.0 | 4060 | 1.8900 | 0.7202 | |
|
| 0.0045 | 15.0 | 4350 | 1.9730 | 0.7216 | |
|
| 0.0042 | 16.0 | 4640 | 2.0775 | 0.7041 | |
|
| 0.0042 | 17.0 | 4930 | 2.0514 | 0.7106 | |
|
| 0.0019 | 18.0 | 5220 | 2.0582 | 0.7326 | |
|
| 0.0039 | 19.0 | 5510 | 2.1010 | 0.7081 | |
|
| 0.0039 | 20.0 | 5800 | 2.0487 | 0.7273 | |
|
| 0.0025 | 21.0 | 6090 | 2.0415 | 0.7254 | |
|
| 0.0025 | 22.0 | 6380 | 2.0753 | 0.7157 | |
|
| 0.0017 | 23.0 | 6670 | 2.0554 | 0.7246 | |
|
| 0.0017 | 24.0 | 6960 | 2.0644 | 0.7290 | |
|
| 0.0001 | 25.0 | 7250 | 2.0711 | 0.7310 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.21.0 |
|
- Pytorch 1.12.0+cu113 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
|
|
