metadata
library_name: peft
tags:
- generated_from_trainer
base_model: sgkinc/xlm-roberta-text-classification
metrics:
- accuracy
model-index:
- name: xlm-roberta-text-cls-peft-prompt-tuning
results: []
xlm-roberta-text-cls-peft-prompt-tuning
This model is a fine-tuned version of sgkinc/xlm-roberta-text-classification on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7050
- Accuracy: 0.7294
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7278 | 0.9992 | 594 | 1.2574 | 0.5517 |
| 1.3236 | 2.0 | 1189 | 0.9894 | 0.6393 |
| 1.1827 | 2.9992 | 1783 | 0.8649 | 0.6897 |
| 1.1147 | 4.0 | 2378 | 0.8593 | 0.6897 |
| 1.0552 | 4.9992 | 2972 | 0.7745 | 0.6976 |
| 1.0143 | 6.0 | 3567 | 0.7181 | 0.7135 |
| 0.9872 | 6.9992 | 4161 | 0.7037 | 0.7507 |
| 1.002 | 8.0 | 4756 | 0.7111 | 0.7347 |
| 0.9816 | 8.9992 | 5350 | 0.6931 | 0.7241 |
| 0.9602 | 9.9916 | 5940 | 0.7050 | 0.7294 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1