---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-trading
  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. -->

# xlm-roberta-base-trading

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2400
- Accuracy: 0.9603

## 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: 2e-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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 227  | 0.1720          | 0.9488   |
| No log        | 2.0   | 454  | 0.1644          | 0.9427   |
| 0.5189        | 3.0   | 681  | 0.1209          | 0.9565   |
| 0.5189        | 4.0   | 908  | 0.1223          | 0.9557   |
| 0.0997        | 5.0   | 1135 | 0.1350          | 0.9535   |
| 0.0997        | 6.0   | 1362 | 0.1239          | 0.9557   |
| 0.0729        | 7.0   | 1589 | 0.1245          | 0.9581   |
| 0.0729        | 8.0   | 1816 | 0.1264          | 0.9568   |
| 0.0578        | 9.0   | 2043 | 0.1313          | 0.9590   |
| 0.0578        | 10.0  | 2270 | 0.1502          | 0.9540   |
| 0.0578        | 11.0  | 2497 | 0.1411          | 0.9573   |
| 0.0489        | 12.0  | 2724 | 0.1527          | 0.9581   |
| 0.0489        | 13.0  | 2951 | 0.1537          | 0.9562   |
| 0.0419        | 14.0  | 3178 | 0.1561          | 0.9581   |
| 0.0419        | 15.0  | 3405 | 0.1686          | 0.9592   |
| 0.0363        | 16.0  | 3632 | 0.1730          | 0.9559   |
| 0.0363        | 17.0  | 3859 | 0.1684          | 0.9603   |
| 0.0337        | 18.0  | 4086 | 0.1764          | 0.9581   |
| 0.0337        | 19.0  | 4313 | 0.1725          | 0.9592   |
| 0.0289        | 20.0  | 4540 | 0.1677          | 0.9595   |
| 0.0289        | 21.0  | 4767 | 0.1726          | 0.9570   |
| 0.0289        | 22.0  | 4994 | 0.1802          | 0.9614   |
| 0.0258        | 23.0  | 5221 | 0.1984          | 0.9587   |
| 0.0258        | 24.0  | 5448 | 0.1915          | 0.9584   |
| 0.0253        | 25.0  | 5675 | 0.2046          | 0.9587   |
| 0.0253        | 26.0  | 5902 | 0.2221          | 0.9592   |
| 0.0231        | 27.0  | 6129 | 0.2321          | 0.9584   |
| 0.0231        | 28.0  | 6356 | 0.2018          | 0.9562   |
| 0.0185        | 29.0  | 6583 | 0.2385          | 0.9592   |
| 0.0185        | 30.0  | 6810 | 0.2219          | 0.9598   |
| 0.0187        | 31.0  | 7037 | 0.2097          | 0.9609   |
| 0.0187        | 32.0  | 7264 | 0.2204          | 0.9606   |
| 0.0187        | 33.0  | 7491 | 0.2174          | 0.9598   |
| 0.0163        | 34.0  | 7718 | 0.2310          | 0.9601   |
| 0.0163        | 35.0  | 7945 | 0.2349          | 0.9603   |
| 0.0147        | 36.0  | 8172 | 0.2426          | 0.9595   |
| 0.0147        | 37.0  | 8399 | 0.2404          | 0.9592   |
| 0.0151        | 38.0  | 8626 | 0.2357          | 0.9609   |
| 0.0151        | 39.0  | 8853 | 0.2390          | 0.9601   |
| 0.0132        | 40.0  | 9080 | 0.2400          | 0.9603   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1