File size: 1,986 Bytes
82a3763 11dd815 82a3763 11dd815 82a3763 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
---
license: gpl-3.0
base_model: ckiplab/bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: clip-DIT-finetuned
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/l1gxhklx)
# clip-DIT-finetuned
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: 0.7439
## 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: 100.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.648 | 10.0 | 780 | 2.4340 |
| 0.797 | 20.0 | 1560 | 1.5686 |
| 0.3112 | 30.0 | 2340 | 1.2473 |
| 0.1758 | 40.0 | 3120 | 1.0606 |
| 0.1176 | 50.0 | 3900 | 0.9469 |
| 0.09 | 60.0 | 4680 | 0.8606 |
| 0.0733 | 70.0 | 5460 | 0.8265 |
| 0.0631 | 80.0 | 6240 | 0.7704 |
| 0.0576 | 90.0 | 7020 | 0.7507 |
| 0.0545 | 100.0 | 7800 | 0.7439 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|