|
--- |
|
base_model: OFA-Sys/chinese-clip-vit-base-patch16 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: aoi_clip_clean |
|
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/n13f9o9o) |
|
# aoi_clip_clean |
|
|
|
This model is a fine-tuned version of [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 5.6248 |
|
|
|
## 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: 40 |
|
- eval_batch_size: 44 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 120.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:------:|:---------------:| |
|
| 0.2626 | 12.0 | 17748 | 6.2362 | |
|
| 0.057 | 24.0 | 35496 | 6.3541 | |
|
| 0.0428 | 36.0 | 53244 | 6.0895 | |
|
| 0.0359 | 48.0 | 70992 | 6.1071 | |
|
| 0.0312 | 60.0 | 88740 | 5.9994 | |
|
| 0.0287 | 72.0 | 106488 | 5.8543 | |
|
| 0.0262 | 84.0 | 124236 | 5.7595 | |
|
| 0.0246 | 96.0 | 141984 | 5.7167 | |
|
| 0.0227 | 108.0 | 159732 | 5.6840 | |
|
| 0.0215 | 120.0 | 177480 | 5.6248 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.3 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
