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README.md
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---
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library_name: transformers
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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: fusion_None_sep_SEP_describe_llama
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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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# fusion_None_sep_SEP_describe_llama
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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: 2.5783
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- Accuracy: 0.2858
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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: 60
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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: 480
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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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| 2.686 | 5.9653 | 774 | 2.3316 | 0.3232 |
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| 2.5095 | 11.9306 | 1548 | 2.3506 | 0.3168 |
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| 2.4304 | 17.8960 | 2322 | 2.4180 | 0.3103 |
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| 2.3871 | 23.8613 | 3096 | 2.4723 | 0.3052 |
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| 2.3556 | 29.8266 | 3870 | 2.5127 | 0.3 |
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| 2.3325 | 35.7919 | 4644 | 2.5233 | 0.2965 |
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| 2.3155 | 41.7572 | 5418 | 2.5572 | 0.2930 |
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| 2.3137 | 47.7225 | 6192 | 2.5639 | 0.2903 |
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| 2.2978 | 53.6879 | 6966 | 2.5749 | 0.2878 |
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| 2.2964 | 59.6532 | 7740 | 2.5783 | 0.2858 |
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### Framework versions
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- Transformers 4.45.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.20.0
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