---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xnli_en_lora_alpha_64_drop_0.1_rank_32_seed_456
  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. -->

# xnli_en_lora_alpha_64_drop_0.1_rank_32_seed_456

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: 0.4781
- Accuracy: 0.8361

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 456
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.5401        | 1.0   | 12272  | 0.4940          | 0.8012   |
| 0.5153        | 2.0   | 24544  | 0.4847          | 0.8185   |
| 0.4912        | 3.0   | 36816  | 0.4560          | 0.8281   |
| 0.464         | 4.0   | 49088  | 0.4341          | 0.8305   |
| 0.4353        | 5.0   | 61360  | 0.4341          | 0.8293   |
| 0.4222        | 6.0   | 73632  | 0.4421          | 0.8353   |
| 0.4029        | 7.0   | 85904  | 0.4692          | 0.8181   |
| 0.383         | 8.0   | 98176  | 0.4453          | 0.8289   |
| 0.3747        | 9.0   | 110448 | 0.4696          | 0.8273   |
| 0.358         | 10.0  | 122720 | 0.4697          | 0.8217   |
| 0.3303        | 11.0  | 134992 | 0.4648          | 0.8317   |
| 0.3217        | 12.0  | 147264 | 0.4618          | 0.8386   |
| 0.31          | 13.0  | 159536 | 0.4796          | 0.8333   |
| 0.2831        | 14.0  | 171808 | 0.4702          | 0.8329   |
| 0.2847        | 15.0  | 184080 | 0.4781          | 0.8361   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1