---
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-data2
  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-data2

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: 0.0942
- Precision: 0.9401
- Recall: 0.9421
- F1: 0.9411
- Accuracy: 0.9802

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1064        | 1.0   | 250  | 0.1276          | 0.8786    | 0.9093 | 0.8937 | 0.9678   |
| 0.079         | 2.0   | 500  | 0.0827          | 0.9369    | 0.9435 | 0.9402 | 0.9806   |
| 0.0329        | 3.0   | 750  | 0.0942          | 0.9401    | 0.9421 | 0.9411 | 0.9802   |


### Framework versions

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