---
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