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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_time_gpt_concate_2
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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/cdver57p)
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# sentance_split_by_time_gpt_concate_2
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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: 3.8914
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- Accuracy: 0.0789
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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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| 2.0864 | 5.9928 | 1866 | 2.9935 | 0.0803 |
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| 1.9035 | 11.9855 | 3732 | 3.1629 | 0.0863 |
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| 1.779 | 17.9783 | 5598 | 3.2064 | 0.0870 |
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| 1.7158 | 23.9711 | 7464 | 3.4417 | 0.0854 |
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| 1.6832 | 29.9639 | 9330 | 3.4988 | 0.0845 |
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| 1.6554 | 35.9566 | 11196 | 3.5538 | 0.0833 |
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| 1.6498 | 41.9494 | 13062 | 3.6819 | 0.0819 |
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| 1.6335 | 47.9422 | 14928 | 3.7696 | 0.0809 |
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| 1.6339 | 53.9350 | 16794 | 3.8098 | 0.0799 |
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| 1.6264 | 59.9277 | 18660 | 3.8914 | 0.0789 |
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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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