|
---
|
|
library_name: transformers
|
|
license: mit
|
|
base_model: microsoft/deberta-v3-base
|
|
tags:
|
|
- generated_from_trainer
|
|
metrics:
|
|
- accuracy
|
|
- f1
|
|
model-index:
|
|
- name: deberta_biodiversite
|
|
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_biodiversite
|
|
|
|
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: 0.0237
|
|
- Accuracy: 0.9926
|
|
- F1: 0.9927
|
|
|
|
## 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: 16
|
|
- eval_batch_size: 16
|
|
- seed: 42
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
|
- lr_scheduler_type: linear
|
|
- num_epochs: 30
|
|
- mixed_precision_training: Native AMP
|
|
|
|
### Training results
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
|
|
| 1.2788 | 1.0 | 128 | 0.6534 | 0.7828 | 0.7762 |
|
|
| 0.5887 | 2.0 | 256 | 0.3681 | 0.8926 | 0.8884 |
|
|
| 0.4035 | 3.0 | 384 | 0.2719 | 0.9142 | 0.9123 |
|
|
| 0.2674 | 4.0 | 512 | 0.1562 | 0.9564 | 0.9563 |
|
|
| 0.2189 | 5.0 | 640 | 0.1438 | 0.9613 | 0.9610 |
|
|
| 0.152 | 6.0 | 768 | 0.1300 | 0.9701 | 0.9701 |
|
|
| 0.1403 | 7.0 | 896 | 0.0879 | 0.9794 | 0.9792 |
|
|
| 0.1072 | 8.0 | 1024 | 0.0673 | 0.9873 | 0.9872 |
|
|
| 0.1236 | 9.0 | 1152 | 0.0499 | 0.9887 | 0.9887 |
|
|
| 0.0803 | 10.0 | 1280 | 0.0490 | 0.9907 | 0.9907 |
|
|
| 0.0746 | 11.0 | 1408 | 0.0501 | 0.9912 | 0.9912 |
|
|
| 0.0568 | 12.0 | 1536 | 0.0314 | 0.9912 | 0.9912 |
|
|
| 0.0397 | 13.0 | 1664 | 0.0285 | 0.9917 | 0.9916 |
|
|
| 0.0445 | 14.0 | 1792 | 0.0204 | 0.9926 | 0.9927 |
|
|
| 0.0479 | 15.0 | 1920 | 0.0250 | 0.9922 | 0.9922 |
|
|
| 0.0363 | 16.0 | 2048 | 0.0244 | 0.9922 | 0.9922 |
|
|
| 0.0343 | 17.0 | 2176 | 0.0237 | 0.9926 | 0.9927 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.48.3
|
|
- Pytorch 2.5.1+cu121
|
|
- Datasets 3.2.0
|
|
- Tokenizers 0.21.0
|
|
|
