|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: EuroBERT/EuroBERT-210m |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: eurobert210m_Eau_v2 |
|
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. --> |
|
|
|
# eurobert210m_Eau_v2 |
|
|
|
This model is a fine-tuned version of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0680 |
|
- Accuracy: 0.9584 |
|
- F1: 0.9595 |
|
|
|
## 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: 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: 100 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| 1.4372 | 1.0 | 67 | 0.9689 | 0.6322 | 0.5664 | |
|
| 0.8205 | 2.0 | 134 | 0.6235 | 0.8213 | 0.8222 | |
|
| 0.4899 | 3.0 | 201 | 0.4782 | 0.8326 | 0.8367 | |
|
| 0.3598 | 4.0 | 268 | 0.2252 | 0.9196 | 0.9200 | |
|
| 0.2854 | 5.0 | 335 | 0.2137 | 0.9258 | 0.9265 | |
|
| 0.2054 | 6.0 | 402 | 0.1284 | 0.9452 | 0.9443 | |
|
| 0.1735 | 7.0 | 469 | 0.1984 | 0.9296 | 0.9303 | |
|
| 0.1763 | 8.0 | 536 | 0.1177 | 0.9409 | 0.9379 | |
|
| 0.1601 | 9.0 | 603 | 0.1133 | 0.9485 | 0.9462 | |
|
| 0.1206 | 10.0 | 670 | 0.1219 | 0.9461 | 0.9448 | |
|
| 0.1269 | 11.0 | 737 | 0.0756 | 0.9565 | 0.9575 | |
|
| 0.1238 | 12.0 | 804 | 0.1025 | 0.9522 | 0.9539 | |
|
| 0.0969 | 13.0 | 871 | 0.0823 | 0.9570 | 0.9580 | |
|
| 0.1046 | 14.0 | 938 | 0.0802 | 0.9527 | 0.9513 | |
|
| 0.1101 | 15.0 | 1005 | 0.0797 | 0.9546 | 0.9539 | |
|
| 0.0864 | 16.0 | 1072 | 0.0853 | 0.9565 | 0.9550 | |
|
| 0.1002 | 17.0 | 1139 | 0.0696 | 0.9579 | 0.9582 | |
|
| 0.0794 | 18.0 | 1206 | 0.0774 | 0.9579 | 0.9588 | |
|
| 0.0849 | 19.0 | 1273 | 0.0719 | 0.9546 | 0.9529 | |
|
| 0.0867 | 20.0 | 1340 | 0.0723 | 0.9589 | 0.9575 | |
|
| 0.0952 | 21.0 | 1407 | 0.0680 | 0.9584 | 0.9595 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.48.3 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.3.2 |
|
- Tokenizers 0.21.0 |
|
|
