|
--- |
|
license: mit |
|
base_model: microsoft/deberta-v3-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: deberta-v3-base_finetuned_nostalgia |
|
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. --> |
|
|
|
# deberta-v3-base_finetuned_nostalgia |
|
|
|
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3772 |
|
- Accuracy: 0.9379 |
|
- F1 Macro: 0.9288 |
|
- Accuracy Balanced: 0.9264 |
|
- F1 Micro: 0.9379 |
|
- Precision Macro: 0.9313 |
|
- Recall Macro: 0.9264 |
|
- Precision Micro: 0.9379 |
|
- Recall Micro: 0.9379 |
|
|
|
## 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: 64 |
|
- seed: 1984 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.06 |
|
- num_epochs: 10 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
|
| No log | 1.0 | 73 | 0.3903 | 0.8759 | 0.8470 | 0.8251 | 0.8759 | 0.8890 | 0.8251 | 0.8759 | 0.8759 | |
|
| No log | 2.0 | 146 | 0.2130 | 0.9103 | 0.8933 | 0.8783 | 0.9103 | 0.9149 | 0.8783 | 0.9103 | 0.9103 | |
|
| No log | 3.0 | 219 | 0.1253 | 0.9379 | 0.9288 | 0.9264 | 0.9379 | 0.9313 | 0.9264 | 0.9379 | 0.9379 | |
|
| No log | 4.0 | 292 | 0.2694 | 0.9310 | 0.9229 | 0.9324 | 0.9310 | 0.9154 | 0.9324 | 0.9310 | 0.9310 | |
|
| No log | 5.0 | 365 | 0.1924 | 0.9448 | 0.9370 | 0.9370 | 0.9448 | 0.9370 | 0.9370 | 0.9448 | 0.9448 | |
|
| No log | 6.0 | 438 | 0.2648 | 0.9379 | 0.9288 | 0.9264 | 0.9379 | 0.9313 | 0.9264 | 0.9379 | 0.9379 | |
|
| 0.1908 | 7.0 | 511 | 0.3431 | 0.9379 | 0.9288 | 0.9264 | 0.9379 | 0.9313 | 0.9264 | 0.9379 | 0.9379 | |
|
| 0.1908 | 8.0 | 584 | 0.3450 | 0.9379 | 0.9288 | 0.9264 | 0.9379 | 0.9313 | 0.9264 | 0.9379 | 0.9379 | |
|
| 0.1908 | 9.0 | 657 | 0.3538 | 0.9379 | 0.9279 | 0.9209 | 0.9379 | 0.9362 | 0.9209 | 0.9379 | 0.9379 | |
|
| 0.1908 | 10.0 | 730 | 0.3772 | 0.9379 | 0.9288 | 0.9264 | 0.9379 | 0.9313 | 0.9264 | 0.9379 | 0.9379 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.0 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
