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---
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
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