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---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
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. -->
# test
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6527
- Precision: 0.7393
- Recall: 0.7759
- F1: 0.7571
- Accuracy: 0.7614
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.3333 | 100 | 0.9776 | 0.4894 | 0.6113 | 0.5436 | 0.6166 |
| No log | 2.6667 | 200 | 0.8649 | 0.6249 | 0.6296 | 0.6273 | 0.7231 |
| No log | 4.0 | 300 | 0.8745 | 0.6449 | 0.7392 | 0.6888 | 0.7326 |
| No log | 5.3333 | 400 | 0.9419 | 0.6292 | 0.7168 | 0.6702 | 0.7367 |
| 0.6362 | 6.6667 | 500 | 0.9902 | 0.7090 | 0.7458 | 0.7269 | 0.7700 |
| 0.6362 | 8.0 | 600 | 1.0048 | 0.7050 | 0.7315 | 0.7180 | 0.7614 |
| 0.6362 | 9.3333 | 700 | 1.1327 | 0.6918 | 0.7305 | 0.7106 | 0.7568 |
| 0.6362 | 10.6667 | 800 | 1.3954 | 0.6952 | 0.7366 | 0.7153 | 0.7333 |
| 0.6362 | 12.0 | 900 | 1.2721 | 0.7002 | 0.7509 | 0.7247 | 0.7491 |
| 0.1105 | 13.3333 | 1000 | 1.3422 | 0.7166 | 0.7356 | 0.7260 | 0.7521 |
| 0.1105 | 14.6667 | 1100 | 1.3957 | 0.72 | 0.7427 | 0.7312 | 0.7605 |
| 0.1105 | 16.0 | 1200 | 1.4581 | 0.7250 | 0.7519 | 0.7382 | 0.7608 |
| 0.1105 | 17.3333 | 1300 | 1.4598 | 0.7459 | 0.7371 | 0.7415 | 0.7577 |
| 0.1105 | 18.6667 | 1400 | 1.5542 | 0.7182 | 0.7504 | 0.7339 | 0.7497 |
| 0.0271 | 20.0 | 1500 | 1.5411 | 0.7176 | 0.7779 | 0.7465 | 0.7549 |
| 0.0271 | 21.3333 | 1600 | 1.6468 | 0.7251 | 0.7539 | 0.7393 | 0.7452 |
| 0.0271 | 22.6667 | 1700 | 1.6821 | 0.7311 | 0.7550 | 0.7429 | 0.7487 |
| 0.0271 | 24.0 | 1800 | 1.6220 | 0.7364 | 0.7499 | 0.7431 | 0.7585 |
| 0.0271 | 25.3333 | 1900 | 1.6220 | 0.7403 | 0.7667 | 0.7533 | 0.7599 |
| 0.0055 | 26.6667 | 2000 | 1.6161 | 0.7370 | 0.7682 | 0.7523 | 0.7687 |
| 0.0055 | 28.0 | 2100 | 1.6683 | 0.7396 | 0.7713 | 0.7551 | 0.7599 |
| 0.0055 | 29.3333 | 2200 | 1.6527 | 0.7393 | 0.7759 | 0.7571 | 0.7614 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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