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---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: Data_extraction
results: []
---
<!-- 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. -->
# Data_extraction
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Ign: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933}
- Overall Precision: 1.0
- Overall Recall: 1.0
- Overall F1: 1.0
- Overall Accuracy: 1.0
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ign | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.032 | 18.1818 | 200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 36.3636 | 400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 54.5455 | 600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 72.7273 | 800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 90.9091 | 1000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 109.0909 | 1200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 127.2727 | 1400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 145.4545 | 1600 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 163.6364 | 1800 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 181.8182 | 2000 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 200.0 | 2200 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 218.1818 | 2400 | 0.0000 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 6933} | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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