---
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-IT
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: xfun
      type: xfun
      config: xfun.it
      split: validation
      args: xfun.it
    metrics:
    - name: Precision
      type: precision
      value: 0.726186733731531
    - name: Recall
      type: recall
      value: 0.7927247769389156
    - name: F1
      type: f1
      value: 0.7579983593109106
    - name: Accuracy
      type: accuracy
      value: 0.768676917924818
---

<!-- 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-IT

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: 2.5355
- Precision: 0.7262
- Recall: 0.7927
- F1: 0.7580
- Accuracy: 0.7687

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0696        | 7.46   | 500   | 1.0876          | 0.6322    | 0.6517 | 0.6418 | 0.7584   |
| 0.0576        | 14.93  | 1000  | 1.3989          | 0.6712    | 0.7601 | 0.7129 | 0.7601   |
| 0.0096        | 22.39  | 1500  | 1.8059          | 0.6774    | 0.7639 | 0.7181 | 0.7662   |
| 0.0092        | 29.85  | 2000  | 2.0416          | 0.7266    | 0.7334 | 0.7300 | 0.7652   |
| 0.0003        | 37.31  | 2500  | 2.0467          | 0.7166    | 0.7539 | 0.7348 | 0.7628   |
| 0.0013        | 44.78  | 3000  | 2.0159          | 0.7027    | 0.7821 | 0.7403 | 0.7638   |
| 0.0013        | 52.24  | 3500  | 2.2751          | 0.6961    | 0.7728 | 0.7325 | 0.7575   |
| 0.0002        | 59.7   | 4000  | 2.2084          | 0.7236    | 0.7563 | 0.7396 | 0.7723   |
| 0.0002        | 67.16  | 4500  | 2.1843          | 0.7048    | 0.7701 | 0.7360 | 0.7581   |
| 0.0001        | 74.63  | 5000  | 2.2483          | 0.7366    | 0.7745 | 0.7551 | 0.7770   |
| 0.0001        | 82.09  | 5500  | 2.2685          | 0.7171    | 0.7752 | 0.7451 | 0.7677   |
| 0.0005        | 89.55  | 6000  | 2.2877          | 0.7180    | 0.7821 | 0.7487 | 0.7692   |
| 0.0001        | 97.01  | 6500  | 2.2574          | 0.7308    | 0.7725 | 0.7511 | 0.7721   |
| 0.0           | 104.48 | 7000  | 2.4696          | 0.7255    | 0.7862 | 0.7546 | 0.7660   |
| 0.0           | 111.94 | 7500  | 2.3996          | 0.7140    | 0.7917 | 0.7509 | 0.7725   |
| 0.0           | 119.4  | 8000  | 2.4592          | 0.7261    | 0.7852 | 0.7545 | 0.7665   |
| 0.0           | 126.87 | 8500  | 2.4129          | 0.7336    | 0.7900 | 0.7607 | 0.7718   |
| 0.0           | 134.33 | 9000  | 2.5367          | 0.7316    | 0.7896 | 0.7595 | 0.7666   |
| 0.0           | 141.79 | 9500  | 2.5327          | 0.7278    | 0.7900 | 0.7576 | 0.7663   |
| 0.0           | 149.25 | 10000 | 2.5355          | 0.7262    | 0.7927 | 0.7580 | 0.7687   |


### Framework versions

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