---
library_name: transformers
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: cool_full_clip
  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. -->

# cool_full_clip

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: 2.9110
- Accuracy: 0.1265

## 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: 50
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 200
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step   | Accuracy | Validation Loss |
|:-------------:|:-------:|:------:|:--------:|:---------------:|
| 2.1154        | 9.9998  | 21890  | 0.1279   | 2.7240          |
| 2.0659        | 19.9957 | 43780  | 0.1278   | 2.7772          |
| 1.9905        | 29.9970 | 65670  | 2.7804   | 0.1312          |
| 1.9583        | 39.9925 | 87560  | 2.8292   | 0.1301          |
| 1.9332        | 49.9879 | 109450 | 2.8340   | 0.1296          |
| 1.9132        | 59.9833 | 131340 | 2.8476   | 0.1292          |
| 1.8997        | 69.9788 | 153230 | 2.8616   | 0.1287          |
| 1.8921        | 79.9742 | 175120 | 2.8884   | 0.1281          |
| 1.8875        | 89.9696 | 197010 | 2.9169   | 0.1273          |
| 1.8833        | 99.9651 | 218900 | 2.9121   | 0.1268          |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0