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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-finetuned-spiderTraining5-100 |
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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-finetuned-spiderTraining5-100 |
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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: 0.7208 |
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- Accuracy: 0.88 |
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- Precision: 0.8822 |
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- Recall: 0.8936 |
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- F1: 0.8805 |
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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: 3e-05 |
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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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| No log | 0.96 | 6 | 1.3602 | 0.52 | 0.5198 | 0.5390 | 0.5169 | |
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| 1.4377 | 1.92 | 12 | 1.0444 | 0.78 | 0.7903 | 0.7869 | 0.7804 | |
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| 1.4377 | 2.88 | 18 | 0.8560 | 0.86 | 0.8636 | 0.8736 | 0.8598 | |
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| 0.9338 | 4.0 | 25 | 0.7443 | 0.86 | 0.8636 | 0.8736 | 0.8598 | |
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| 0.7051 | 4.8 | 30 | 0.7208 | 0.88 | 0.8822 | 0.8936 | 0.8805 | |
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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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