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---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-ES
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.es
split: validation
args: xfun.es
metrics:
- name: Precision
type: precision
value: 0.6718889883616831
- name: Recall
type: recall
value: 0.6733961417676088
- name: F1
type: f1
value: 0.6726417208155948
- name: Accuracy
type: accuracy
value: 0.7462640815388152
---
<!-- 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-ES
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5588
- Precision: 0.6719
- Recall: 0.6734
- F1: 0.6726
- Accuracy: 0.7463
## 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 | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.2279 | 8.2 | 500 | 0.6790 | 0.5205 | 1.2508 | 0.4589 | 0.6012 |
| 0.032 | 16.39 | 1000 | 0.6936 | 0.5885 | 1.9637 | 0.6321 | 0.5505 |
| 0.0073 | 24.59 | 1500 | 0.7351 | 0.6175 | 1.6711 | 0.5795 | 0.6608 |
| 0.0479 | 32.79 | 2000 | 0.7405 | 0.6422 | 1.8259 | 0.6265 | 0.6586 |
| 0.0666 | 40.98 | 2500 | 0.7424 | 0.6349 | 1.8343 | 0.5937 | 0.6824 |
| 0.0006 | 49.18 | 3000 | 0.7475 | 0.6536 | 2.0575 | 0.6512 | 0.6559 |
| 0.0084 | 57.38 | 3500 | 0.7138 | 0.6415 | 2.4488 | 0.6758 | 0.6106 |
| 0.0002 | 65.57 | 4000 | 0.7571 | 0.6468 | 1.9641 | 0.6406 | 0.6532 |
| 0.0005 | 73.77 | 4500 | 2.2976 | 0.6699 | 0.6429 | 0.6561 | 0.7413 |
| 0.0003 | 81.97 | 5000 | 2.1562 | 0.6287 | 0.6653 | 0.6465 | 0.7468 |
| 0.0007 | 90.16 | 5500 | 2.2806 | 0.6435 | 0.6689 | 0.6560 | 0.7435 |
| 0.0002 | 98.36 | 6000 | 2.0508 | 0.6294 | 0.6734 | 0.6506 | 0.7538 |
| 0.0 | 106.56 | 6500 | 2.2626 | 0.6602 | 0.6765 | 0.6683 | 0.7498 |
| 0.0 | 114.75 | 7000 | 2.3467 | 0.6687 | 0.6492 | 0.6588 | 0.7409 |
| 0.0 | 122.95 | 7500 | 2.4430 | 0.6773 | 0.6734 | 0.6754 | 0.7447 |
| 0.0 | 131.15 | 8000 | 2.3653 | 0.6643 | 0.6765 | 0.6704 | 0.7476 |
| 0.0 | 139.34 | 8500 | 2.2903 | 0.6567 | 0.6824 | 0.6693 | 0.7498 |
| 0.0 | 147.54 | 9000 | 2.4458 | 0.6536 | 0.6824 | 0.6677 | 0.7440 |
| 0.0 | 155.74 | 9500 | 2.5953 | 0.6703 | 0.6685 | 0.6694 | 0.7423 |
| 0.0 | 163.93 | 10000 | 2.5588 | 0.6719 | 0.6734 | 0.6726 | 0.7463 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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