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---
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: aoi_clip_high_resolution_crossAttenttionFusion_fusin_gpt_new_sampler
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/cru2aevs)
# aoi_clip_high_resolution_crossAttenttionFusion_fusin_gpt_new_sampler
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: 5.3131
- Accuracy: 0.0559
## 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: 25
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:--------:|:-----:|:---------------:|:--------:|
| 2.152 | 19.9759 | 6220 | 3.0212 | 0.0537 |
| 1.9424 | 39.9518 | 12440 | 3.4495 | 0.0563 |
| 1.7646 | 59.9277 | 18660 | 3.9469 | 0.0561 |
| 1.6901 | 79.9037 | 24880 | 4.2354 | 0.0550 |
| 1.6562 | 99.8796 | 31100 | 4.6732 | 0.0548 |
| 1.6398 | 119.8555 | 37320 | 4.8612 | 0.0550 |
| 1.6289 | 139.8314 | 43540 | 4.8784 | 0.0550 |
| 1.6192 | 159.8073 | 49760 | 5.2516 | 0.0554 |
| 1.6163 | 179.7832 | 55980 | 5.2837 | 0.0558 |
| 1.6165 | 199.7591 | 62200 | 5.3131 | 0.0558 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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