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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-NLI-idk-mrc-nli-keep-with-xlm-roberta-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-NLI-idk-mrc-nli-keep-with-xlm-roberta-large
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1080
- Accuracy: 0.9830
- F1: 0.9830
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3752 | 0.49 | 39 | 0.6866 | 0.5183 | 0.3749 |
| 0.7598 | 0.99 | 78 | 0.2098 | 0.9332 | 0.9331 |
| 0.3228 | 1.49 | 117 | 0.1063 | 0.9634 | 0.9633 |
| 0.1461 | 1.99 | 156 | 0.0813 | 0.9725 | 0.9725 |
| 0.1461 | 2.49 | 195 | 0.0719 | 0.9777 | 0.9777 |
| 0.1154 | 2.99 | 234 | 0.0704 | 0.9777 | 0.9777 |
| 0.0881 | 3.49 | 273 | 0.0625 | 0.9830 | 0.9830 |
| 0.0551 | 3.99 | 312 | 0.0738 | 0.9817 | 0.9817 |
| 0.0474 | 4.49 | 351 | 0.0779 | 0.9843 | 0.9843 |
| 0.0474 | 4.99 | 390 | 0.0860 | 0.9791 | 0.9791 |
| 0.0425 | 5.49 | 429 | 0.0801 | 0.9856 | 0.9856 |
| 0.0316 | 5.99 | 468 | 0.0947 | 0.9817 | 0.9817 |
| 0.0185 | 6.49 | 507 | 0.0953 | 0.9856 | 0.9856 |
| 0.0185 | 6.99 | 546 | 0.0979 | 0.9817 | 0.9817 |
| 0.0264 | 7.49 | 585 | 0.0923 | 0.9830 | 0.9830 |
| 0.0156 | 7.99 | 624 | 0.1080 | 0.9830 | 0.9830 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
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