|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
model-index: |
|
|
- name: just-nce |
|
|
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. --> |
|
|
|
|
|
# just-nce |
|
|
|
|
|
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: 1.0338 |
|
|
- Able: {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6} |
|
|
- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} |
|
|
- Ext: {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10} |
|
|
- Mage: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} |
|
|
- Ub heading: {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16} |
|
|
- Overall Precision: 0.6571 |
|
|
- Overall Recall: 0.575 |
|
|
- Overall F1: 0.6133 |
|
|
- Overall Accuracy: 0.68 |
|
|
|
|
|
## 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: 100 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Able | Eading | Ext | Mage | Ub heading | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
|
|
| 0.4724 | 14.29 | 100 | 1.0338 | {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16} | 0.6571 | 0.575 | 0.6133 | 0.68 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.26.1 |
|
|
- Pytorch 2.0.0+cu118 |
|
|
- Datasets 2.11.0 |
|
|
- Tokenizers 0.13.2 |
|
|
|
