---
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