tensorboard safetensors generated_from_trainer base_model:FacebookAI/xlm-roberta-base license:mit region:us
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: xlm-roberta-base-lora-text-classification
  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-lora-text-classification

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5131
- Precision: 0.7307
- Recall: 1.0
- F1 and accuracy: {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133}

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy                                            |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log        | 1.0   | 401  | 0.5787          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5822        | 2.0   | 802  | 0.5722          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.586         | 3.0   | 1203 | 0.5636          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5504        | 4.0   | 1604 | 0.5513          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5661        | 5.0   | 2005 | 0.5451          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5661        | 6.0   | 2406 | 0.5332          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5562        | 7.0   | 2807 | 0.5329          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5354        | 8.0   | 3208 | 0.5252          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5575        | 9.0   | 3609 | 0.5149          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |
| 0.5301        | 10.0  | 4010 | 0.5131          | 0.7307    | 1.0    | {'accuracy': 0.7306733167082294, 'f1': 0.8443804034582133} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
