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xlm-roberta-base-finetuned-combined-DS
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0232
- Accuracy: 0.6362
- Precision: 0.6193
- Recall: 0.6204
- F1: 0.6160
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: 4.1187640010910775e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.0408 | 1.0 | 711 | 1.0206 | 0.5723 | 0.5597 | 0.5122 | 0.4897 |
| 0.9224 | 2.0 | 1422 | 0.9092 | 0.5695 | 0.5745 | 0.5610 | 0.5572 |
| 0.8395 | 3.0 | 2133 | 0.8878 | 0.6088 | 0.6083 | 0.6071 | 0.5981 |
| 0.7418 | 3.99 | 2844 | 0.8828 | 0.6088 | 0.6009 | 0.6068 | 0.5936 |
| 0.6484 | 4.99 | 3555 | 0.9636 | 0.6355 | 0.6235 | 0.6252 | 0.6184 |
| 0.5644 | 5.99 | 4266 | 1.0232 | 0.6362 | 0.6193 | 0.6204 | 0.6160 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
