---
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