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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-tiny-22k-384 |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: convnextv2-tiny-22k-384-0.0005-finetuned-spiderTraining20-500 |
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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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# convnextv2-tiny-22k-384-0.0005-finetuned-spiderTraining20-500 |
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This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1840 |
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- Accuracy: 0.9439 |
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- Precision: 0.9448 |
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- Recall: 0.9411 |
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- F1: 0.9424 |
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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: 0.0005 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.934 | 1.0 | 125 | 0.6076 | 0.8118 | 0.8237 | 0.8060 | 0.8064 | |
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| 0.5826 | 2.0 | 250 | 0.4684 | 0.8529 | 0.8685 | 0.8506 | 0.8436 | |
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| 0.4743 | 3.0 | 375 | 0.3240 | 0.9059 | 0.9083 | 0.9006 | 0.9023 | |
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| 0.3488 | 4.0 | 500 | 0.2431 | 0.9369 | 0.9345 | 0.9348 | 0.9335 | |
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| 0.2307 | 5.0 | 625 | 0.1840 | 0.9439 | 0.9448 | 0.9411 | 0.9424 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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