metadata
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_XLMROBERTA_cs_wikann
results: []
fine_tuned_XLMROBERTA_cs_wikann
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: 0.1238
- Overall Precision: 0.8962
- Overall Recall: 0.9193
- Overall F1: 0.9076
- Overall Accuracy: 0.9684
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3625 | 0.4 | 500 | 0.1809 | 0.7915 | 0.8509 | 0.8201 | 0.9486 |
| 0.1807 | 0.8 | 1000 | 0.1373 | 0.8363 | 0.8785 | 0.8568 | 0.9583 |
| 0.1384 | 1.2 | 1500 | 0.1371 | 0.8758 | 0.9085 | 0.8918 | 0.9651 |
| 0.1079 | 1.6 | 2000 | 0.1467 | 0.8924 | 0.9168 | 0.9044 | 0.9659 |
| 0.0997 | 2.0 | 2500 | 0.1170 | 0.9018 | 0.9264 | 0.9139 | 0.9700 |
| 0.0644 | 2.4 | 3000 | 0.1344 | 0.9123 | 0.9285 | 0.9203 | 0.9706 |
| 0.0594 | 2.8 | 3500 | 0.1269 | 0.9138 | 0.9345 | 0.9240 | 0.9718 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0