metadata
library_name: transformers
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xml-roberta-large-v3
results: []
xml-roberta-large-v3
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0792
- Precision: 0.9448
- Recall: 0.9726
- F1: 0.9585
- Accuracy: 0.9806
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- 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: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3722 | 1.0 | 204 | 0.2039 | 0.8490 | 0.8680 | 0.8584 | 0.9440 |
| 0.1353 | 2.0 | 408 | 0.0930 | 0.9324 | 0.9626 | 0.9473 | 0.9759 |
| 0.0969 | 3.0 | 612 | 0.0869 | 0.9407 | 0.9684 | 0.9543 | 0.9776 |
| 0.0785 | 4.0 | 816 | 0.0818 | 0.9411 | 0.9729 | 0.9568 | 0.9793 |
| 0.0498 | 5.0 | 1020 | 0.0792 | 0.9448 | 0.9726 | 0.9585 | 0.9806 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3