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---
license: mit
base_model: kavg/LiLT-SER-PT
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-PT-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: 0.7639225181598063
- name: Recall
type: recall
value: 0.7770935960591133
- name: F1
type: f1
value: 0.7704517704517705
- name: Accuracy
type: accuracy
value: 0.8626735867583111
---
<!-- 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-PT-SIN
This model is a fine-tuned version of [kavg/LiLT-SER-PT](https://huggingface.co/kavg/LiLT-SER-PT) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2074
- Precision: 0.7639
- Recall: 0.7771
- F1: 0.7705
- Accuracy: 0.8627
## 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 |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0124 | 21.74 | 500 | 0.8590 | 0.7403 | 0.8082 | 0.7381 | 0.7426 |
| 0.0017 | 43.48 | 1000 | 0.8301 | 0.7272 | 1.2608 | 0.75 | 0.7057 |
| 0.0004 | 65.22 | 1500 | 0.8694 | 0.7323 | 0.8843 | 0.7098 | 0.7562 |
| 0.0 | 86.96 | 2000 | 0.8617 | 0.7532 | 1.0638 | 0.7419 | 0.7648 |
| 0.0001 | 108.7 | 2500 | 0.8580 | 0.7674 | 1.1504 | 0.7689 | 0.7660 |
| 0.0006 | 130.43 | 3000 | 0.8677 | 0.7479 | 0.9865 | 0.7230 | 0.7746 |
| 0.0 | 152.17 | 3500 | 0.8617 | 0.7558 | 1.1492 | 0.7494 | 0.7623 |
| 0.0001 | 173.91 | 4000 | 0.8385 | 0.7590 | 1.3124 | 0.7485 | 0.7697 |
| 0.0055 | 195.65 | 4500 | 1.1331 | 0.7295 | 0.7869 | 0.7571 | 0.8479 |
| 0.0 | 217.39 | 5000 | 1.2061 | 0.7392 | 0.7611 | 0.7500 | 0.8500 |
| 0.0001 | 239.13 | 5500 | 1.2572 | 0.7253 | 0.7672 | 0.7457 | 0.8482 |
| 0.0 | 260.87 | 6000 | 1.3558 | 0.7494 | 0.7734 | 0.7612 | 0.8569 |
| 0.0 | 282.61 | 6500 | 1.4382 | 0.7598 | 0.7672 | 0.7635 | 0.8589 |
| 0.0 | 304.35 | 7000 | 1.4720 | 0.7537 | 0.7574 | 0.7555 | 0.8533 |
| 0.0 | 326.09 | 7500 | 1.3835 | 0.7524 | 0.7783 | 0.7651 | 0.8579 |
| 0.0 | 347.83 | 8000 | 1.2693 | 0.7534 | 0.7599 | 0.7566 | 0.8599 |
| 0.0 | 369.57 | 8500 | 1.2005 | 0.7417 | 0.7709 | 0.7560 | 0.8600 |
| 0.0 | 391.3 | 9000 | 1.2175 | 0.7560 | 0.7820 | 0.7688 | 0.8601 |
| 0.0 | 413.04 | 9500 | 1.2339 | 0.7556 | 0.7845 | 0.7698 | 0.8601 |
| 0.0 | 434.78 | 10000 | 1.2074 | 0.7639 | 0.7771 | 0.7705 | 0.8627 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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