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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: 20-finetuned-spiderTraining50-200 |
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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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# 20-finetuned-spiderTraining50-200 |
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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: 0.4409 |
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- Accuracy: 0.8909 |
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- Precision: 0.8899 |
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- Recall: 0.8922 |
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- F1: 0.8881 |
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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: 20 |
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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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| 1.4597 | 1.0 | 125 | 1.1828 | 0.6617 | 0.7065 | 0.6632 | 0.6495 | |
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| 1.2938 | 2.0 | 250 | 1.0667 | 0.6827 | 0.7244 | 0.6845 | 0.6701 | |
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| 1.1316 | 3.0 | 375 | 0.9465 | 0.7257 | 0.7828 | 0.7291 | 0.7280 | |
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| 0.7827 | 4.0 | 500 | 0.8576 | 0.7397 | 0.7701 | 0.7394 | 0.7372 | |
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| 0.7407 | 5.0 | 625 | 0.8084 | 0.7728 | 0.7876 | 0.7728 | 0.7636 | |
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| 0.6481 | 6.0 | 750 | 0.7537 | 0.7798 | 0.7999 | 0.7783 | 0.7765 | |
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| 0.5868 | 7.0 | 875 | 0.6406 | 0.8258 | 0.8341 | 0.8266 | 0.8224 | |
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| 0.4461 | 8.0 | 1000 | 0.7555 | 0.7768 | 0.7953 | 0.7736 | 0.7679 | |
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| 0.4984 | 9.0 | 1125 | 0.6601 | 0.8128 | 0.8260 | 0.8120 | 0.8059 | |
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| 0.3898 | 10.0 | 1250 | 0.7017 | 0.8108 | 0.8296 | 0.8079 | 0.8059 | |
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| 0.3262 | 11.0 | 1375 | 0.6199 | 0.8258 | 0.8341 | 0.8268 | 0.8212 | |
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| 0.3243 | 12.0 | 1500 | 0.6561 | 0.8188 | 0.8316 | 0.8256 | 0.8191 | |
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| 0.2914 | 13.0 | 1625 | 0.6037 | 0.8368 | 0.8504 | 0.8429 | 0.8351 | |
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| 0.2627 | 14.0 | 1750 | 0.5609 | 0.8529 | 0.8588 | 0.8557 | 0.8501 | |
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| 0.2457 | 15.0 | 1875 | 0.5266 | 0.8639 | 0.8674 | 0.8666 | 0.8613 | |
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| 0.2294 | 16.0 | 2000 | 0.5475 | 0.8589 | 0.8658 | 0.8608 | 0.8560 | |
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| 0.2088 | 17.0 | 2125 | 0.4929 | 0.8699 | 0.8726 | 0.8678 | 0.8672 | |
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| 0.2101 | 18.0 | 2250 | 0.4488 | 0.8799 | 0.8782 | 0.8775 | 0.8752 | |
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| 0.1767 | 19.0 | 2375 | 0.4543 | 0.8829 | 0.8824 | 0.8808 | 0.8782 | |
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| 0.1445 | 20.0 | 2500 | 0.4409 | 0.8909 | 0.8899 | 0.8922 | 0.8881 | |
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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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