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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: aoi_clip_high_resolution_concate_fusin_gpt_random_sampler
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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/4jms502r)
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# aoi_clip_high_resolution_concate_fusin_gpt_random_sampler
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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: 2.9963
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- Accuracy: 0.0502
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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: 40
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- eval_batch_size: 20
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- seed: 42
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- gradient_accumulation_steps: 5
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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: 100.0
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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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| 0.0008 | 9.9872 | 3110 | 3.0008 | 0.0500 |
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| 0.0007 | 19.9743 | 6220 | 2.9971 | 0.0501 |
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| 0.0007 | 29.9615 | 9330 | 2.9971 | 0.0501 |
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| 0.0007 | 39.9486 | 12440 | 2.9988 | 0.0500 |
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| 0.0007 | 49.9358 | 15550 | 2.9968 | 0.0499 |
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| 0.0007 | 59.9229 | 18660 | 2.9966 | 0.0502 |
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| 0.0007 | 69.9101 | 21770 | 2.9961 | 0.0503 |
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| 0.0007 | 79.8972 | 24880 | 2.9967 | 0.0503 |
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| 0.0007 | 89.8844 | 27990 | 2.9966 | 0.0503 |
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| 0.0007 | 99.8715 | 31100 | 2.9963 | 0.0502 |
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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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