---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo15_3
  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. -->

# xlmr_immigration_combo15_3

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2712
- Accuracy: 0.9332
- 1-f1: 0.8980
- 1-recall: 0.8842
- 1-precision: 0.9124
- Balanced Acc: 0.9209

## 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: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1   | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1474        | 1.0   | 25   | 0.2366          | 0.9306   | 0.8977 | 0.9151   | 0.8810      | 0.9267       |
| 0.1345        | 2.0   | 50   | 0.2140          | 0.9383   | 0.9024 | 0.8571   | 0.9528      | 0.9180       |
| 0.1144        | 3.0   | 75   | 0.2228          | 0.9357   | 0.9004 | 0.8726   | 0.9300      | 0.9199       |
| 0.1508        | 4.0   | 100  | 0.2712          | 0.9332   | 0.8980 | 0.8842   | 0.9124      | 0.9209       |


### Framework versions

- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4