metadata
license: mit
base_model: joeddav/xlm-roberta-large-xnli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-xnli-v4.0
results: []
xlm-roberta-large-xnli-v4.0
This model is a fine-tuned version of joeddav/xlm-roberta-large-xnli on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4963
- F1 Macro: 0.8192
- F1 Micro: 0.8204
- Accuracy Balanced: 0.8190
- Accuracy: 0.8204
- Precision Macro: 0.8193
- Recall Macro: 0.8190
- Precision Micro: 0.8204
- Recall Micro: 0.8204
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: 9e-06
- train_batch_size: 8
- eval_batch_size: 64
- seed: 40
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3593 | 1.69 | 200 | 0.4297 | 0.8211 | 0.8218 | 0.8224 | 0.8218 | 0.8206 | 0.8224 | 0.8218 | 0.8218 |
eval result
| Datasets | asadfgglie/nli-zh-tw-all/test | asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test | eval_dataset | test_dataset |
|---|---|---|---|---|
| eval_loss | 0.494 | 0.773 | 0.483 | 0.496 |
| eval_f1_macro | 0.821 | 0.627 | 0.825 | 0.819 |
| eval_f1_micro | 0.822 | 0.644 | 0.826 | 0.82 |
| eval_accuracy_balanced | 0.821 | 0.638 | 0.826 | 0.819 |
| eval_accuracy | 0.822 | 0.644 | 0.826 | 0.82 |
| eval_precision_macro | 0.821 | 0.663 | 0.825 | 0.819 |
| eval_recall_macro | 0.821 | 0.638 | 0.826 | 0.819 |
| eval_precision_micro | 0.822 | 0.644 | 0.826 | 0.82 |
| eval_recall_micro | 0.822 | 0.644 | 0.826 | 0.82 |
| eval_runtime | 50.82 | 0.635 | 10.346 | 39.781 |
| eval_samples_per_second | 167.257 | 1490.523 | 164.308 | 170.938 |
| eval_steps_per_second | 2.617 | 23.634 | 2.61 | 2.69 |
| Size of dataset | 8500 | 946 | 1700 | 6800 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3