---
library_name: transformers
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-r-langdetect-model
  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-r-langdetect-model

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2107
- Accuracy: 0.9617

## 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: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1059        | 1.0   | 1275  | 0.1641          | 0.9526   |
| 0.0838        | 2.0   | 2550  | 0.1660          | 0.9548   |
| 0.068         | 3.0   | 3825  | 0.1741          | 0.9552   |
| 0.0561        | 4.0   | 5100  | 0.1828          | 0.9556   |
| 0.0474        | 5.0   | 6375  | 0.1918          | 0.9549   |
| 0.0428        | 6.0   | 7650  | 0.1994          | 0.9568   |
| 0.0346        | 7.0   | 8925  | 0.2109          | 0.9568   |
| 0.0351        | 8.0   | 10200 | 0.2138          | 0.9588   |
| 0.0318        | 9.0   | 11475 | 0.2218          | 0.9588   |
| 0.0282        | 10.0  | 12750 | 0.2219          | 0.9593   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1