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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_add
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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/harqk9wd)
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# sentance_split_by_aoi_gpt_add
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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: 3.5837
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- Accuracy: 0.1041
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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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| 1.168 | 5.9676 | 276 | 2.9707 | 0.1067 |
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| 0.8242 | 11.9351 | 552 | 3.4872 | 0.0994 |
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| 0.639 | 17.9027 | 828 | 3.5071 | 0.0965 |
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| 0.5825 | 23.8703 | 1104 | 3.5619 | 0.0994 |
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| 0.5449 | 29.8378 | 1380 | 3.5003 | 0.0994 |
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| 0.5257 | 35.8054 | 1656 | 3.4785 | 0.1019 |
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| 0.512 | 41.7730 | 1932 | 3.4630 | 0.1020 |
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| 0.5039 | 47.7405 | 2208 | 3.5130 | 0.1036 |
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| 0.492 | 53.7081 | 2484 | 3.5763 | 0.1038 |
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| 0.4922 | 59.6757 | 2760 | 3.5837 | 0.1041 |
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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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