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library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base_42
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. -->
# xlm-roberta-base_42
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3930
- F1-score: 0.8657
- Accuracy: 0.8657
- Precision: 0.8658
- Recall: 0.8659
## 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-06
- 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| No log | 1.0 | 379 | 0.4004 | 0.8284 | 0.8287 | 0.8299 | 0.8282 |
| 0.5426 | 2.0 | 758 | 0.3531 | 0.8502 | 0.8503 | 0.8508 | 0.8500 |
| 0.4202 | 3.0 | 1137 | 0.3569 | 0.8564 | 0.8565 | 0.8566 | 0.8563 |
| 0.3646 | 4.0 | 1516 | 0.3520 | 0.8688 | 0.8688 | 0.8689 | 0.8687 |
| 0.3646 | 5.0 | 1895 | 0.4078 | 0.8564 | 0.8565 | 0.8577 | 0.8569 |
| 0.3229 | 6.0 | 2274 | 0.3930 | 0.8657 | 0.8657 | 0.8658 | 0.8659 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|