metadata
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large-mnli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-mnli_nli
results: []
roberta-large-mnli_nli
This model is a fine-tuned version of FacebookAI/roberta-large-mnli on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.9476
- Accuracy: 0.6009
- Precision Macro: 0.6028
- Recall Macro: 0.6009
- F1 Macro: 0.6014
- F1 Weighted: 0.6012
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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use 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: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|---|---|
| 1.0485 | 1.0 | 143 | 0.9848 | 0.5162 | 0.5491 | 0.5191 | 0.4775 | 0.4758 |
| 0.9114 | 2.0 | 286 | 0.9839 | 0.5264 | 0.5642 | 0.5266 | 0.5150 | 0.5148 |
| 0.8746 | 3.0 | 429 | 0.9618 | 0.5517 | 0.5743 | 0.5522 | 0.5453 | 0.5451 |
| 0.7909 | 4.0 | 572 | 0.9498 | 0.5805 | 0.5859 | 0.5813 | 0.5766 | 0.5762 |
| 0.7105 | 5.0 | 715 | 0.9324 | 0.5956 | 0.6000 | 0.5960 | 0.5939 | 0.5936 |
| 0.6205 | 6.0 | 858 | 0.9797 | 0.5933 | 0.5958 | 0.5934 | 0.5927 | 0.5925 |
| 0.5113 | 7.0 | 1001 | 1.1925 | 0.5889 | 0.5918 | 0.5896 | 0.5857 | 0.5853 |
| 0.4181 | 8.0 | 1144 | 1.2665 | 0.5916 | 0.5922 | 0.5918 | 0.5918 | 0.5916 |
| 0.3218 | 9.0 | 1287 | 1.4587 | 0.5849 | 0.5866 | 0.5848 | 0.5849 | 0.5849 |
| 0.2543 | 10.0 | 1430 | 1.5554 | 0.5902 | 0.5910 | 0.5908 | 0.5892 | 0.5889 |
| 0.1851 | 11.0 | 1573 | 1.8125 | 0.5787 | 0.5829 | 0.5782 | 0.5786 | 0.5787 |
| 0.1316 | 12.0 | 1716 | 2.0182 | 0.5827 | 0.5837 | 0.5826 | 0.5826 | 0.5825 |
| 0.0884 | 13.0 | 1859 | 2.1233 | 0.5809 | 0.5823 | 0.5810 | 0.5812 | 0.5811 |
| 0.0708 | 14.0 | 2002 | 2.2924 | 0.5938 | 0.5936 | 0.5943 | 0.5935 | 0.5931 |
| 0.0527 | 15.0 | 2145 | 2.4595 | 0.5916 | 0.5923 | 0.5919 | 0.5918 | 0.5916 |
| 0.0334 | 16.0 | 2288 | 2.6315 | 0.5991 | 0.6009 | 0.5991 | 0.5996 | 0.5995 |
| 0.0186 | 17.0 | 2431 | 2.8367 | 0.5947 | 0.5979 | 0.5946 | 0.5953 | 0.5952 |
| 0.0179 | 18.0 | 2574 | 2.9197 | 0.6004 | 0.6032 | 0.6004 | 0.6010 | 0.6009 |
| 0.0113 | 19.0 | 2717 | 2.9423 | 0.5982 | 0.6003 | 0.5982 | 0.5987 | 0.5986 |
| 0.0134 | 20.0 | 2860 | 2.9476 | 0.6009 | 0.6028 | 0.6009 | 0.6014 | 0.6012 |
Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4