---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: fine-tuning-xlmr-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-tuning-xlmr-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7558
- Accuracy: 0.7692
- Precision: 0.7692
- Recall: 0.7692
- F1 Score: 0.7693

## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 101
- 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 | Precision | Recall | F1 Score |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 1.3385        | 1.0   | 10330  | 1.8072          | 0.5708   | 0.5708    | 0.5708 | 0.5622   |
| 1.7231        | 2.0   | 20660  | 1.8354          | 0.6445   | 0.6445    | 0.6445 | 0.6454   |
| 1.4049        | 3.0   | 30990  | 1.8380          | 0.6969   | 0.6969    | 0.6969 | 0.6990   |
| 1.4543        | 4.0   | 41320  | 1.5726          | 0.7415   | 0.7415    | 0.7415 | 0.7417   |
| 1.4139        | 5.0   | 51650  | 1.6838          | 0.7424   | 0.7424    | 0.7424 | 0.7439   |
| 1.2368        | 6.0   | 61980  | 1.6794          | 0.7424   | 0.7424    | 0.7424 | 0.7448   |
| 1.0418        | 7.0   | 72310  | 1.6720          | 0.7542   | 0.7542    | 0.7542 | 0.7556   |
| 1.246         | 8.0   | 82640  | 1.6746          | 0.7638   | 0.7638    | 0.7638 | 0.7642   |
| 0.9896        | 9.0   | 92970  | 1.7497          | 0.7674   | 0.7674    | 0.7674 | 0.7666   |
| 0.9855        | 10.0  | 103300 | 1.7558          | 0.7692   | 0.7692    | 0.7692 | 0.7693   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0