metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base-eng
results: []
xlm-roberta-base-eng
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1062
- Accuracy: 0.7383
- F1 Binary: 0.6078
- Precision: 0.4993
- Recall: 0.7765
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 41
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 416 | 0.1130 | 0.5809 | 0.5198 | 0.3709 | 0.8687 |
| 0.1321 | 2.0 | 832 | 0.1055 | 0.5584 | 0.5215 | 0.3636 | 0.9217 |
| 0.1074 | 3.0 | 1248 | 0.1039 | 0.7265 | 0.5710 | 0.4836 | 0.6970 |
| 0.0922 | 4.0 | 1664 | 0.1062 | 0.7383 | 0.6078 | 0.4993 | 0.7765 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0