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  1. README.md +8 -3
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@@ -10,12 +10,12 @@ model-index:
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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/kcgspgk1)
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  # aoi_clip
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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: 5.5744
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  ## Model description
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@@ -40,7 +40,7 @@ The following hyperparameters were used during training:
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  - seed: 42
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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
@@ -57,6 +57,11 @@ The following hyperparameters were used during training:
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  | 0.0255 | 80.0 | 118320 | 5.7286 |
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  | 0.0238 | 90.0 | 133110 | 5.6773 |
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  | 0.0225 | 100.0 | 147900 | 5.5744 |
 
 
 
 
 
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  ### Framework versions
 
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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/kx6ba7ok)
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  # aoi_clip
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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: 5.5439
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  ## Model description
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  - seed: 42
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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: 150.0
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  - mixed_precision_training: Native AMP
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  ### Training results
 
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  | 0.0255 | 80.0 | 118320 | 5.7286 |
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  | 0.0238 | 90.0 | 133110 | 5.6773 |
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  | 0.0225 | 100.0 | 147900 | 5.5744 |
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+ | 0.0243 | 110.0 | 162690 | 5.6549 |
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+ | 0.024 | 120.0 | 177480 | 5.5997 |
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+ | 0.0227 | 130.0 | 192270 | 5.5604 |
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+ | 0.0219 | 140.0 | 207060 | 5.5429 |
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+ | 0.0217 | 150.0 | 221850 | 5.5439 |
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  ### Framework versions