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README.md
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---
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base_model: OFA-Sys/chinese-clip-vit-base-patch16
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: sentance_split_by_aoi_gpt_None
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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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)
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# sentance_split_by_aoi_gpt_None
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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.
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It achieves the following results on the evaluation set:
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- Loss: 2.6624
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- Accuracy: 0.3280
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 25
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- eval_batch_size: 20
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 200
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 60.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|
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| 1.1843 | 5.9676 | 276 | 2.2263 | 0.3670 |
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| 0.9309 | 11.9351 | 552 | 2.3463 | 0.3428 |
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| 0.7822 | 17.9027 | 828 | 2.4978 | 0.3344 |
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| 0.7077 | 23.8703 | 1104 | 2.5808 | 0.3321 |
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| 0.6579 | 29.8378 | 1380 | 2.5881 | 0.3297 |
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| 0.6308 | 35.8054 | 1656 | 2.6445 | 0.3283 |
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| 0.6117 | 41.7730 | 1932 | 2.6626 | 0.3281 |
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| 0.6045 | 47.7405 | 2208 | 2.6374 | 0.3284 |
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| 0.5889 | 53.7081 | 2484 | 2.6702 | 0.3280 |
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| 0.5908 | 59.6757 | 2760 | 2.6624 | 0.3280 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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