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---
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentance_split_by_aoi_gpt_concate
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/xb0vwaee)
# sentance_split_by_aoi_gpt_concate
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: 3.7438
- Accuracy: 0.0955
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.1881 | 5.9676 | 276 | 2.9810 | 0.0841 |
| 0.9134 | 11.9351 | 552 | 3.3984 | 0.0946 |
| 0.6902 | 17.9027 | 828 | 3.6736 | 0.0946 |
| 0.6084 | 23.8703 | 1104 | 3.7337 | 0.0942 |
| 0.5615 | 29.8378 | 1380 | 3.6272 | 0.0933 |
| 0.5358 | 35.8054 | 1656 | 3.6502 | 0.0943 |
| 0.5184 | 41.7730 | 1932 | 3.6430 | 0.0939 |
| 0.5087 | 47.7405 | 2208 | 3.6398 | 0.0946 |
| 0.4951 | 53.7081 | 2484 | 3.7279 | 0.0949 |
| 0.4939 | 59.6757 | 2760 | 3.7438 | 0.0952 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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