---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: guj-eng-code-switch-xlm-roberta-data3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# guj-eng-code-switch-xlm-roberta-data3

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0931
- Precision: 0.9379
- Recall: 0.9506
- F1: 0.9442
- Accuracy: 0.9788

## 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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2012        | 1.0   | 247  | 0.1486          | 0.8770    | 0.9166 | 0.8964 | 0.9519   |
| 0.1059        | 2.0   | 494  | 0.0823          | 0.9265    | 0.9438 | 0.9351 | 0.9800   |
| 0.0824        | 3.0   | 741  | 0.0931          | 0.9379    | 0.9506 | 0.9442 | 0.9788   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1