---
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentance_split_by_aoi_gpt_add
  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/harqk9wd)
# sentance_split_by_aoi_gpt_add

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: 3.5837
- Accuracy: 0.1044

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.168         | 5.9676  | 276  | 2.9707          | 0.1067   |
| 0.8242        | 11.9351 | 552  | 3.4872          | 0.0994   |
| 0.639         | 17.9027 | 828  | 3.5071          | 0.0965   |
| 0.5825        | 23.8703 | 1104 | 3.5619          | 0.0994   |
| 0.5449        | 29.8378 | 1380 | 3.5003          | 0.0994   |
| 0.5257        | 35.8054 | 1656 | 3.4785          | 0.1019   |
| 0.512         | 41.7730 | 1932 | 3.4630          | 0.1020   |
| 0.5039        | 47.7405 | 2208 | 3.5130          | 0.1036   |
| 0.492         | 53.7081 | 2484 | 3.5763          | 0.1038   |
| 0.4922        | 59.6757 | 2760 | 3.5837          | 0.1041   |


### Framework versions

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