|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
base_model: bert-base-chinese |
|
model-index: |
|
- name: bert-base-chinese-finetuned-ner-split_food |
|
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. --> |
|
|
|
# bert-base-chinese-finetuned-ner-split_food |
|
|
|
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0077 |
|
- F1: 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: 30 |
|
- eval_batch_size: 30 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 3.6798 | 1.0 | 1 | 1.6743 | 0.0 | |
|
| 1.8172 | 2.0 | 2 | 0.6580 | 0.0 | |
|
| 0.746 | 3.0 | 3 | 0.4864 | 0.0 | |
|
| 0.4899 | 4.0 | 4 | 0.3927 | 0.0 | |
|
| 0.401 | 5.0 | 5 | 0.2753 | 0.0 | |
|
| 0.2963 | 6.0 | 6 | 0.2160 | 0.0 | |
|
| 0.2452 | 7.0 | 7 | 0.1848 | 0.5455 | |
|
| 0.2188 | 8.0 | 8 | 0.1471 | 0.7692 | |
|
| 0.1775 | 9.0 | 9 | 0.1131 | 0.7692 | |
|
| 0.1469 | 10.0 | 10 | 0.0864 | 0.8293 | |
|
| 0.1145 | 11.0 | 11 | 0.0621 | 0.9333 | |
|
| 0.0881 | 12.0 | 12 | 0.0432 | 1.0 | |
|
| 0.0702 | 13.0 | 13 | 0.0329 | 1.0 | |
|
| 0.0531 | 14.0 | 14 | 0.0268 | 1.0 | |
|
| 0.044 | 15.0 | 15 | 0.0184 | 1.0 | |
|
| 0.0321 | 16.0 | 16 | 0.0129 | 1.0 | |
|
| 0.0255 | 17.0 | 17 | 0.0101 | 1.0 | |
|
| 0.0236 | 18.0 | 18 | 0.0087 | 1.0 | |
|
| 0.0254 | 19.0 | 19 | 0.0080 | 1.0 | |
|
| 0.0185 | 20.0 | 20 | 0.0077 | 1.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.18.0 |
|
- Pytorch 1.12.0+cu102 |
|
- Datasets 1.18.4 |
|
- Tokenizers 0.12.1 |
|
|
