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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-large-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-large-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-large-22k-384-finetuned-spiderTraining20-500 |
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This model is a fine-tuned version of [facebook/convnextv2-large-22k-384](https://huggingface.co./facebook/convnextv2-large-22k-384) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0881 |
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- Accuracy: 0.9740 |
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- Precision: 0.9749 |
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- Recall: 0.9729 |
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- F1: 0.9733 |
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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: 5e-05 |
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- train_batch_size: 15 |
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- eval_batch_size: 15 |
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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: 60 |
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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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| 0.5835 | 1.0 | 133 | 0.4317 | 0.8659 | 0.8765 | 0.8664 | 0.8594 | |
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| 0.3813 | 2.0 | 266 | 0.2008 | 0.9499 | 0.9533 | 0.9464 | 0.9488 | |
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| 0.3476 | 2.99 | 399 | 0.1535 | 0.9580 | 0.9591 | 0.9563 | 0.9570 | |
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| 0.1858 | 4.0 | 533 | 0.1591 | 0.9540 | 0.9542 | 0.9535 | 0.9532 | |
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| 0.1962 | 5.0 | 666 | 0.1356 | 0.9570 | 0.9565 | 0.9566 | 0.9556 | |
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| 0.1674 | 6.0 | 799 | 0.1290 | 0.9610 | 0.9612 | 0.9597 | 0.9599 | |
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| 0.1673 | 6.99 | 932 | 0.1138 | 0.9660 | 0.9669 | 0.9643 | 0.9651 | |
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| 0.1793 | 8.0 | 1066 | 0.0919 | 0.9720 | 0.9714 | 0.9707 | 0.9706 | |
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| 0.1369 | 9.0 | 1199 | 0.0936 | 0.9690 | 0.9690 | 0.9676 | 0.9677 | |
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| 0.1256 | 9.98 | 1330 | 0.0881 | 0.9740 | 0.9749 | 0.9729 | 0.9733 | |
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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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