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

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: 0.6343
- Precision: 0.6371
- Recall: 0.9958
- F1 and accuracy: {'accuracy': 0.6353887399463807, 'f1': 0.7770491803278688}

## 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   | 372  | 0.6553          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6758        | 2.0   | 744  | 0.6534          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6765        | 3.0   | 1116 | 0.6518          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6765        | 4.0   | 1488 | 0.6519          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6722        | 5.0   | 1860 | 0.6470          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6654        | 6.0   | 2232 | 0.6425          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6626        | 7.0   | 2604 | 0.6419          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6626        | 8.0   | 2976 | 0.6404          | 0.6371    | 0.9958 | {'accuracy': 0.6353887399463807, 'f1': 0.7770491803278688} |
| 0.6547        | 9.0   | 3348 | 0.6356          | 0.6381    | 1.0    | {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545}  |
| 0.6544        | 10.0  | 3720 | 0.6343          | 0.6371    | 0.9958 | {'accuracy': 0.6353887399463807, 'f1': 0.7770491803278688} |


### Framework versions

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