---
library_name: transformers
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output_LiLT_test_03
  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. -->

# output_LiLT_test_03

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: 0.1452
- Precision: 0.7549
- Recall: 0.8276
- F1: 0.7896
- Accuracy: 0.9577

## 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
- lr_scheduler_warmup_steps: 300
- num_epochs: 45

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.0808 | 100  | 0.5589          | 0.0259    | 0.0020 | 0.0037 | 0.8724   |
| No log        | 0.1617 | 200  | 0.3198          | 0.3704    | 0.3698 | 0.3701 | 0.9009   |
| No log        | 0.2425 | 300  | 0.2222          | 0.6005    | 0.6743 | 0.6353 | 0.9346   |
| No log        | 0.3234 | 400  | 0.1950          | 0.7231    | 0.6316 | 0.6743 | 0.9446   |
| 0.4262        | 0.4042 | 500  | 0.1684          | 0.7115    | 0.7215 | 0.7165 | 0.9491   |
| 0.4262        | 0.4850 | 600  | 0.1885          | 0.6328    | 0.7602 | 0.6907 | 0.9366   |
| 0.4262        | 0.5659 | 700  | 0.1776          | 0.6609    | 0.8097 | 0.7278 | 0.9437   |
| 0.4262        | 0.6467 | 800  | 0.2086          | 0.6420    | 0.8034 | 0.7137 | 0.9398   |
| 0.4262        | 0.7276 | 900  | 0.1702          | 0.8241    | 0.7469 | 0.7836 | 0.9611   |
| 0.1027        | 0.8084 | 1000 | 0.1496          | 0.8028    | 0.7558 | 0.7786 | 0.9601   |
| 0.1027        | 0.8892 | 1100 | 0.1620          | 0.7354    | 0.7754 | 0.7549 | 0.9513   |
| 0.1027        | 0.9701 | 1200 | 0.1404          | 0.8062    | 0.7997 | 0.8029 | 0.9627   |
| 0.1027        | 1.0509 | 1300 | 0.1626          | 0.7976    | 0.7564 | 0.7764 | 0.9597   |
| 0.1027        | 1.1318 | 1400 | 0.1741          | 0.7253    | 0.8089 | 0.7648 | 0.9485   |
| 0.0764        | 1.2126 | 1500 | 0.1278          | 0.8170    | 0.7798 | 0.7979 | 0.9645   |
| 0.0764        | 1.2935 | 1600 | 0.1452          | 0.7549    | 0.8276 | 0.7896 | 0.9577   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1