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# xlm-roberta-base-20240102

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: 0.1715
- F1: 0.7601
- Roc Auc: 0.8408
- Accuracy: 0.7024

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 109  | 0.2679          | 0.32   | 0.5957  | 0.1938   |
| No log        | 2.0   | 218  | 0.2054          | 0.6377 | 0.7490  | 0.5111   |
| No log        | 3.0   | 327  | 0.1897          | 0.7162 | 0.7956  | 0.6026   |
| No log        | 4.0   | 436  | 0.1776          | 0.7531 | 0.8345  | 0.6891   |
| 0.2255        | 5.0   | 545  | 0.1715          | 0.7601 | 0.8408  | 0.7024   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1", "accuracy"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-20240102", "results": []}]}
transformers tensorboard safetensors xlm-roberta text-classification generated_from_trainer base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us