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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<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/ujufd8ei)
# sentance_split_by_aoi_gpt_None

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: 2.6624
- Accuracy: 0.3279

## 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: 60.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1843        | 5.9676  | 276  | 2.2263          | 0.3670   |
| 0.9309        | 11.9351 | 552  | 2.3463          | 0.3428   |
| 0.7822        | 17.9027 | 828  | 2.4978          | 0.3344   |
| 0.7077        | 23.8703 | 1104 | 2.5808          | 0.3321   |
| 0.6579        | 29.8378 | 1380 | 2.5881          | 0.3297   |
| 0.6308        | 35.8054 | 1656 | 2.6445          | 0.3283   |
| 0.6117        | 41.7730 | 1932 | 2.6626          | 0.3281   |
| 0.6045        | 47.7405 | 2208 | 2.6374          | 0.3284   |
| 0.5889        | 53.7081 | 2484 | 2.6702          | 0.3280   |
| 0.5908        | 59.6757 | 2760 | 2.6624          | 0.3280   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1