---
library_name: transformers
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fusion_concate_sep_SEP_describe_gpt
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# fusion_concate_sep_SEP_describe_gpt

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: 3.6264
- Accuracy: 0.0807

## 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: 20
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 320
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 2.4506        | 5.9807  | 1164  | 2.9871          | 0.0736   |
| 2.3229        | 11.9615 | 2328  | 3.1384          | 0.0733   |
| 2.2364        | 17.9422 | 3492  | 3.1920          | 0.0795   |
| 2.1589        | 23.9229 | 4656  | 3.2667          | 0.0825   |
| 2.1034        | 29.9037 | 5820  | 3.2883          | 0.0835   |
| 2.0555        | 35.8844 | 6984  | 3.3512          | 0.0837   |
| 2.0231        | 41.8651 | 8148  | 3.4304          | 0.0836   |
| 2.0056        | 47.8459 | 9312  | 3.5540          | 0.0831   |
| 1.9964        | 53.8266 | 10476 | 3.5894          | 0.0820   |
| 1.9848        | 59.8073 | 11640 | 3.6264          | 0.0813   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.0