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

# LiLT-SER-EN

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: 2.3960
- Precision: 0.7248
- Recall: 0.7458
- F1: 0.7351
- Accuracy: 0.7438

## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0832        | 6.67   | 500   | 1.0009          | 0.6741    | 0.6923 | 0.6831 | 0.7292   |
| 0.052         | 13.33  | 1000  | 1.4186          | 0.7225    | 0.7320 | 0.7272 | 0.7441   |
| 0.0027        | 20.0   | 1500  | 1.5508          | 0.7218    | 0.7376 | 0.7297 | 0.7464   |
| 0.0034        | 26.67  | 2000  | 1.7198          | 0.7051    | 0.7382 | 0.7213 | 0.7422   |
| 0.002         | 33.33  | 2500  | 1.8116          | 0.7106    | 0.7392 | 0.7246 | 0.7424   |
| 0.0002        | 40.0   | 3000  | 1.8843          | 0.6769    | 0.7514 | 0.7122 | 0.7435   |
| 0.0009        | 46.67  | 3500  | 1.9528          | 0.7401    | 0.7514 | 0.7457 | 0.7518   |
| 0.0224        | 53.33  | 4000  | 2.0602          | 0.7178    | 0.7529 | 0.7350 | 0.7476   |
| 0.0002        | 60.0   | 4500  | 2.2901          | 0.7283    | 0.7509 | 0.7394 | 0.7287   |
| 0.0001        | 66.67  | 5000  | 2.1746          | 0.7198    | 0.7433 | 0.7313 | 0.7371   |
| 0.0001        | 73.33  | 5500  | 1.9452          | 0.7214    | 0.7387 | 0.7299 | 0.7641   |
| 0.0           | 80.0   | 6000  | 2.0976          | 0.7350    | 0.7560 | 0.7454 | 0.7442   |
| 0.0021        | 86.67  | 6500  | 2.3034          | 0.7200    | 0.7387 | 0.7292 | 0.7365   |
| 0.0           | 93.33  | 7000  | 2.2409          | 0.7348    | 0.7636 | 0.7489 | 0.7499   |
| 0.0           | 100.0  | 7500  | 2.2742          | 0.7362    | 0.7193 | 0.7276 | 0.7472   |
| 0.0           | 106.67 | 8000  | 2.4953          | 0.7312    | 0.7509 | 0.7409 | 0.7363   |
| 0.0           | 113.33 | 8500  | 2.4936          | 0.7340    | 0.7438 | 0.7389 | 0.7396   |
| 0.0           | 120.0  | 9000  | 2.3976          | 0.7239    | 0.7453 | 0.7344 | 0.7440   |
| 0.0001        | 126.67 | 9500  | 2.3723          | 0.7282    | 0.7478 | 0.7379 | 0.7441   |
| 0.0           | 133.33 | 10000 | 2.3960          | 0.7248    | 0.7458 | 0.7351 | 0.7438   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1