metadata
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlmr
results: []
xlmr
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6138
- Accuracy: 0.9163
- F1: 0.9153
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.3962 | 1.0 | 450 | 0.3872 | 0.9011 | 0.9005 |
| 0.0584 | 2.0 | 900 | 0.4941 | 0.9180 | 0.9171 |
| 0.0284 | 3.0 | 1350 | 0.6192 | 0.9138 | 0.9127 |
| 0.0144 | 4.0 | 1800 | 0.5967 | 0.9224 | 0.9214 |
| 0.0103 | 5.0 | 2250 | 0.6138 | 0.9163 | 0.9153 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3