nli-cross-encoder-roberta
This model is a fine-tuned version of MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4859
- Accuracy: 0.9448
- F1 Macro: 0.9469
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: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.1806 | 1.0 | 211 | 0.3069 | 0.9088 | 0.9134 |
| 0.1021 | 2.0 | 422 | 0.1795 | 0.9530 | 0.9544 |
| 0.0343 | 3.0 | 633 | 0.4396 | 0.9365 | 0.9389 |
| 0.0182 | 4.0 | 844 | 0.4025 | 0.9475 | 0.9496 |
| 0.0047 | 5.0 | 1055 | 0.4674 | 0.9420 | 0.9441 |
| 0.0014 | 6.0 | 1266 | 0.4457 | 0.9448 | 0.9469 |
| 0.0049 | 7.0 | 1477 | 0.4835 | 0.9448 | 0.9469 |
| 0.0004 | 8.0 | 1688 | 0.4859 | 0.9448 | 0.9469 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.22.0
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