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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-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
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