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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-spiderTraining1000-1000 |
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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-spiderTraining1000-1000 |
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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: 0.3586 |
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- Accuracy: 0.9180 |
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- Precision: 0.9196 |
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- Recall: 0.9160 |
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- F1: 0.9168 |
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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: 27 |
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- eval_batch_size: 27 |
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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: 108 |
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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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| 2.0397 | 1.0 | 4064 | 1.4192 | 0.6356 | 0.6956 | 0.6130 | 0.6167 | |
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| 1.3997 | 2.0 | 8129 | 0.9638 | 0.7454 | 0.7708 | 0.7320 | 0.7325 | |
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| 1.1393 | 3.0 | 12193 | 0.7564 | 0.7973 | 0.8102 | 0.7883 | 0.7884 | |
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| 0.9942 | 4.0 | 16258 | 0.6256 | 0.8331 | 0.8464 | 0.8276 | 0.8294 | |
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| 0.8572 | 5.0 | 20322 | 0.5610 | 0.8507 | 0.8632 | 0.8441 | 0.8467 | |
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| 0.6445 | 6.0 | 24387 | 0.4866 | 0.8730 | 0.8802 | 0.8688 | 0.8697 | |
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| 0.5444 | 7.0 | 28451 | 0.4496 | 0.8852 | 0.8909 | 0.8812 | 0.8829 | |
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| 0.4955 | 8.0 | 32516 | 0.4241 | 0.8986 | 0.9039 | 0.8952 | 0.8974 | |
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| 0.448 | 9.0 | 36580 | 0.3875 | 0.9104 | 0.9133 | 0.9078 | 0.9091 | |
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| 0.4109 | 10.0 | 40640 | 0.3586 | 0.9180 | 0.9196 | 0.9160 | 0.9168 | |
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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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