---
license: mit
base_model: kavg/LiLT-SER-FR
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-FR-SIN
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xfun
      type: xfun
      config: xfun.sin
      split: validation
      args: xfun.sin
    metrics:
    - name: Precision
      type: precision
      value: 0.7617924528301887
    - name: Recall
      type: recall
      value: 0.7955665024630542
    - name: F1
      type: f1
      value: 0.7783132530120481
    - name: Accuracy
      type: accuracy
      value: 0.8647776686772338
---

<!-- 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-FR-SIN

This model is a fine-tuned version of [kavg/LiLT-SER-FR](https://huggingface.co/kavg/LiLT-SER-FR) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2426
- Precision: 0.7618
- Recall: 0.7956
- F1: 0.7783
- Accuracy: 0.8648

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0057        | 21.74  | 500   | 0.8019          | 0.6884    | 0.7020 | 0.6951 | 0.8582   |
| 0.008         | 43.48  | 1000  | 1.0139          | 0.6963    | 0.7623 | 0.7278 | 0.8648   |
| 0.0006        | 65.22  | 1500  | 0.9878          | 0.7090    | 0.7562 | 0.7318 | 0.8592   |
| 0.0038        | 86.96  | 2000  | 1.2269          | 0.7104    | 0.7401 | 0.7250 | 0.8373   |
| 0.001         | 108.7  | 2500  | 0.9751          | 0.7276    | 0.7697 | 0.7481 | 0.8707   |
| 0.0004        | 130.43 | 3000  | 1.0918          | 0.7479    | 0.7672 | 0.7574 | 0.8538   |
| 0.0003        | 152.17 | 3500  | 1.0782          | 0.7102    | 0.7635 | 0.7359 | 0.8604   |
| 0.0           | 173.91 | 4000  | 1.0515          | 0.7402    | 0.7894 | 0.7640 | 0.8704   |
| 0.0001        | 195.65 | 4500  | 1.2154          | 0.7373    | 0.7709 | 0.7538 | 0.8419   |
| 0.0           | 217.39 | 5000  | 1.1026          | 0.7411    | 0.7722 | 0.7563 | 0.8642   |
| 0.0001        | 239.13 | 5500  | 1.0594          | 0.7262    | 0.7512 | 0.7385 | 0.8576   |
| 0.0           | 260.87 | 6000  | 1.1103          | 0.7377    | 0.7759 | 0.7563 | 0.8609   |
| 0.0           | 282.61 | 6500  | 1.1591          | 0.7267    | 0.7599 | 0.7429 | 0.8610   |
| 0.0           | 304.35 | 7000  | 1.2382          | 0.7574    | 0.7537 | 0.7556 | 0.8562   |
| 0.0           | 326.09 | 7500  | 1.2027          | 0.7485    | 0.7882 | 0.7678 | 0.8578   |
| 0.0001        | 347.83 | 8000  | 1.1492          | 0.7433    | 0.7808 | 0.7616 | 0.8659   |
| 0.0002        | 369.57 | 8500  | 1.1924          | 0.7570    | 0.7980 | 0.7770 | 0.8655   |
| 0.0           | 391.3  | 9000  | 1.2426          | 0.7618    | 0.7956 | 0.7783 | 0.8648   |
| 0.0           | 413.04 | 9500  | 1.3078          | 0.7620    | 0.7808 | 0.7713 | 0.8597   |
| 0.0           | 434.78 | 10000 | 1.3219          | 0.7639    | 0.7771 | 0.7705 | 0.8579   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1