---
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: 0.1224
- Accuracy: 0.9751
- F1: 0.9751

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3634        | 0.49  | 39   | 0.6900          | 0.5052   | 0.3515 |
| 0.7309        | 0.99  | 78   | 0.2791          | 0.9202   | 0.9202 |
| 0.4815        | 1.49  | 117  | 0.0854          | 0.9738   | 0.9738 |
| 0.145         | 1.99  | 156  | 0.0903          | 0.9699   | 0.9699 |
| 0.145         | 2.49  | 195  | 0.0931          | 0.9738   | 0.9738 |
| 0.0937        | 2.99  | 234  | 0.0875          | 0.9751   | 0.9751 |
| 0.0752        | 3.49  | 273  | 0.1164          | 0.9738   | 0.9738 |
| 0.0538        | 3.99  | 312  | 0.1386          | 0.9673   | 0.9673 |
| 0.0379        | 4.49  | 351  | 0.0893          | 0.9791   | 0.9791 |
| 0.0379        | 4.99  | 390  | 0.1002          | 0.9777   | 0.9777 |
| 0.0397        | 5.49  | 429  | 0.1214          | 0.9764   | 0.9764 |
| 0.031         | 5.99  | 468  | 0.1224          | 0.9751   | 0.9751 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2