metadata
license: mit
tags:
- generated_from_trainer
model-index:
- name: new_model
results: []
new_model
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0292
- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
- Header: {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17}
- Question: {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}
- Overall Precision: 0.0556
- Overall Recall: 0.0455
- Overall F1: 0.0500
- Overall Accuracy: 0.6578
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.1533 | 3.92 | 200 | 0.0173 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0 | 0.0 | 0.0 | 0.5989 |
| 0.0443 | 7.84 | 400 | 0.0137 | {'precision': 0.09090909090909091, 'recall': 0.1, 'f1': 0.09523809523809525, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0164 | 0.0227 | 0.0190 | 0.6203 |
| 0.0194 | 11.76 | 600 | 0.0162 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.2222222222222222, 'recall': 0.11764705882352941, 'f1': 0.15384615384615383, 'number': 17} | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | 0.1154 | 0.0682 | 0.0857 | 0.6417 |
| 0.0089 | 15.69 | 800 | 0.0186 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.06666666666666667, 'recall': 0.058823529411764705, 'f1': 0.0625, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0278 | 0.0227 | 0.0250 | 0.6684 |
| 0.0036 | 19.61 | 1000 | 0.0264 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17} | 0.0556 | 0.0455 | 0.0500 | 0.6578 |
| 0.0031 | 23.53 | 1200 | 0.0200 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.1111111111111111, 'recall': 0.11764705882352941, 'f1': 0.11428571428571428, 'number': 17} | {'precision': 0.05263157894736842, 'recall': 0.058823529411764705, 'f1': 0.05555555555555555, 'number': 17} | 0.0732 | 0.0682 | 0.0706 | 0.6791 |
| 0.0015 | 27.45 | 1400 | 0.0222 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.05, 'recall': 0.058823529411764705, 'f1': 0.05405405405405405, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0196 | 0.0227 | 0.0211 | 0.6631 |
| 0.0011 | 31.37 | 1600 | 0.0249 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.045454545454545456, 'recall': 0.058823529411764705, 'f1': 0.05128205128205128, 'number': 17} | 0.0408 | 0.0455 | 0.0430 | 0.6845 |
| 0.0006 | 35.29 | 1800 | 0.0243 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0213 | 0.0227 | 0.0220 | 0.6952 |
| 0.0004 | 39.22 | 2000 | 0.0290 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | 0.0488 | 0.0455 | 0.0471 | 0.6578 |
| 0.0002 | 43.14 | 2200 | 0.0288 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | 0.0488 | 0.0455 | 0.0471 | 0.6578 |
| 0.0002 | 47.06 | 2400 | 0.0292 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17} | 0.0556 | 0.0455 | 0.0500 | 0.6578 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.1.0.dev20230810
- Datasets 2.14.4
- Tokenizers 0.11.0