---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: refine-good-name-xlm-roberta
  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. -->

# refine-good-name-xlm-roberta

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: 0.2236
- F1: 0.8688

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2618        | 1.0   | 553  | 0.2357          | 0.8314 |
| 0.2025        | 2.0   | 1106 | 0.2209          | 0.8661 |
| 0.186         | 3.0   | 1659 | 0.2075          | 0.8588 |
| 0.162         | 4.0   | 2212 | 0.2234          | 0.8609 |
| 0.1428        | 5.0   | 2765 | 0.2233          | 0.8700 |
| 0.1328        | 6.0   | 3318 | 0.2236          | 0.8688 |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.0
- Tokenizers 0.13.3