---
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