---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
  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. -->

# test

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6527
- Precision: 0.7393
- Recall: 0.7759
- F1: 0.7571
- Accuracy: 0.7614

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.3333  | 100  | 0.9776          | 0.4894    | 0.6113 | 0.5436 | 0.6166   |
| No log        | 2.6667  | 200  | 0.8649          | 0.6249    | 0.6296 | 0.6273 | 0.7231   |
| No log        | 4.0     | 300  | 0.8745          | 0.6449    | 0.7392 | 0.6888 | 0.7326   |
| No log        | 5.3333  | 400  | 0.9419          | 0.6292    | 0.7168 | 0.6702 | 0.7367   |
| 0.6362        | 6.6667  | 500  | 0.9902          | 0.7090    | 0.7458 | 0.7269 | 0.7700   |
| 0.6362        | 8.0     | 600  | 1.0048          | 0.7050    | 0.7315 | 0.7180 | 0.7614   |
| 0.6362        | 9.3333  | 700  | 1.1327          | 0.6918    | 0.7305 | 0.7106 | 0.7568   |
| 0.6362        | 10.6667 | 800  | 1.3954          | 0.6952    | 0.7366 | 0.7153 | 0.7333   |
| 0.6362        | 12.0    | 900  | 1.2721          | 0.7002    | 0.7509 | 0.7247 | 0.7491   |
| 0.1105        | 13.3333 | 1000 | 1.3422          | 0.7166    | 0.7356 | 0.7260 | 0.7521   |
| 0.1105        | 14.6667 | 1100 | 1.3957          | 0.72      | 0.7427 | 0.7312 | 0.7605   |
| 0.1105        | 16.0    | 1200 | 1.4581          | 0.7250    | 0.7519 | 0.7382 | 0.7608   |
| 0.1105        | 17.3333 | 1300 | 1.4598          | 0.7459    | 0.7371 | 0.7415 | 0.7577   |
| 0.1105        | 18.6667 | 1400 | 1.5542          | 0.7182    | 0.7504 | 0.7339 | 0.7497   |
| 0.0271        | 20.0    | 1500 | 1.5411          | 0.7176    | 0.7779 | 0.7465 | 0.7549   |
| 0.0271        | 21.3333 | 1600 | 1.6468          | 0.7251    | 0.7539 | 0.7393 | 0.7452   |
| 0.0271        | 22.6667 | 1700 | 1.6821          | 0.7311    | 0.7550 | 0.7429 | 0.7487   |
| 0.0271        | 24.0    | 1800 | 1.6220          | 0.7364    | 0.7499 | 0.7431 | 0.7585   |
| 0.0271        | 25.3333 | 1900 | 1.6220          | 0.7403    | 0.7667 | 0.7533 | 0.7599   |
| 0.0055        | 26.6667 | 2000 | 1.6161          | 0.7370    | 0.7682 | 0.7523 | 0.7687   |
| 0.0055        | 28.0    | 2100 | 1.6683          | 0.7396    | 0.7713 | 0.7551 | 0.7599   |
| 0.0055        | 29.3333 | 2200 | 1.6527          | 0.7393    | 0.7759 | 0.7571 | 0.7614   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1