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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-base-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: 10-convnextv2-base-22k-384-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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# 10-convnextv2-base-22k-384-finetuned-spiderTraining20-500 |
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This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1779 |
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- Accuracy: 0.9489 |
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- Precision: 0.9485 |
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- Recall: 0.9477 |
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- F1: 0.9476 |
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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: 25 |
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- eval_batch_size: 25 |
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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: 100 |
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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: 10 |
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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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| 0.7479 | 1.0 | 80 | 0.5460 | 0.8238 | 0.8427 | 0.8154 | 0.8203 | |
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| 0.6232 | 2.0 | 160 | 0.4423 | 0.8619 | 0.8735 | 0.8584 | 0.8573 | |
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| 0.5591 | 3.0 | 240 | 0.4042 | 0.8769 | 0.8862 | 0.8662 | 0.8702 | |
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| 0.4503 | 4.0 | 320 | 0.3648 | 0.8839 | 0.8937 | 0.8811 | 0.8807 | |
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| 0.3479 | 5.0 | 400 | 0.3523 | 0.8989 | 0.8996 | 0.8956 | 0.8945 | |
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| 0.3144 | 6.0 | 480 | 0.2513 | 0.9189 | 0.9175 | 0.9164 | 0.9142 | |
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| 0.2779 | 7.0 | 560 | 0.2274 | 0.9289 | 0.9304 | 0.9234 | 0.9252 | |
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| 0.1958 | 8.0 | 640 | 0.2443 | 0.9289 | 0.9267 | 0.9285 | 0.9268 | |
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| 0.1479 | 9.0 | 720 | 0.2054 | 0.9399 | 0.9378 | 0.9383 | 0.9371 | |
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| 0.1533 | 10.0 | 800 | 0.1779 | 0.9489 | 0.9485 | 0.9477 | 0.9476 | |
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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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