---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-Adapter-en-ar-mlm-0.15-large-29OCT
  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-roberta-base-finetuned-Adapter-en-ar-mlm-0.15-large-29OCT

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: 2.2667
- Model Preparation Time: 0.0044

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Model Preparation Time |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|
| 3.9064        | 0.2498 | 1000 | 3.2366          | 0.0044                 |
| 3.0641        | 0.4995 | 2000 | 2.7403          | 0.0044                 |
| 2.8162        | 0.7493 | 3000 | 2.5485          | 0.0044                 |
| 2.7054        | 0.9990 | 4000 | 2.4384          | 0.0044                 |
| 2.6108        | 1.2488 | 5000 | 2.3627          | 0.0044                 |
| 2.5357        | 1.4985 | 6000 | 2.3141          | 0.0044                 |
| 2.5089        | 1.7483 | 7000 | 2.2847          | 0.0044                 |
| 2.4931        | 1.9980 | 8000 | 2.2667          | 0.0044                 |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.1.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1