---
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
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# 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