|
--- |
|
license: gpl-3.0 |
|
base_model: ckiplab/bert-base-chinese |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: clip-DIT-finetuned_one_text_to_train |
|
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. --> |
|
|
|
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/shark_meow_team/huggingface/runs/mqbvpy21) |
|
# clip-DIT-finetuned_one_text_to_train |
|
|
|
This model is a fine-tuned version of [ckiplab/bert-base-chinese](https://huggingface.co/ckiplab/bert-base-chinese) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.3237 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 64 |
|
- eval_batch_size: 100 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 200.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 2.479 | 20.0 | 780 | 2.5238 | |
|
| 0.5415 | 40.0 | 1560 | 1.9513 | |
|
| 0.1937 | 60.0 | 2340 | 1.6752 | |
|
| 0.1072 | 80.0 | 3120 | 1.5576 | |
|
| 0.0722 | 100.0 | 3900 | 1.4878 | |
|
| 0.0542 | 120.0 | 4680 | 1.4187 | |
|
| 0.0433 | 140.0 | 5460 | 1.3938 | |
|
| 0.0376 | 160.0 | 6240 | 1.3544 | |
|
| 0.0333 | 180.0 | 7020 | 1.3325 | |
|
| 0.0311 | 200.0 | 7800 | 1.3237 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.3 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
