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-afr
results: []
xlm-roberta-base-afr
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: 0.1171
- Accuracy: 0.7395
- F1 Binary: 0.4241
- Precision: 0.2883
- Recall: 0.8011
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: 18
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 184 | 0.1042 | 0.6048 | 0.3622 | 0.2245 | 0.9375 |
| No log | 2.0 | 368 | 0.1271 | 0.8497 | 0.4920 | 0.4131 | 0.6080 |
| 0.1252 | 3.0 | 552 | 0.1223 | 0.7381 | 0.4193 | 0.2854 | 0.7898 |
| 0.1252 | 4.0 | 736 | 0.1171 | 0.7395 | 0.4241 | 0.2883 | 0.8011 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0