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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-nano-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-nano-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-nano-22k-384-finetuned-spiderTraining20-500 |
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This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2966 |
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- Accuracy: 0.9109 |
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- Precision: 0.9058 |
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- Recall: 0.9065 |
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- F1: 0.9057 |
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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: 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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| 1.7156 | 1.0 | 80 | 1.4314 | 0.6316 | 0.6182 | 0.6246 | 0.6155 | |
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| 0.7565 | 2.0 | 160 | 0.6340 | 0.8168 | 0.8213 | 0.8074 | 0.8095 | |
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| 0.5802 | 3.0 | 240 | 0.4632 | 0.8589 | 0.8566 | 0.8545 | 0.8539 | |
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| 0.4767 | 4.0 | 320 | 0.4006 | 0.8759 | 0.8748 | 0.8710 | 0.8708 | |
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| 0.3648 | 5.0 | 400 | 0.3529 | 0.8999 | 0.8976 | 0.8965 | 0.8960 | |
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| 0.3623 | 6.0 | 480 | 0.3326 | 0.9059 | 0.9030 | 0.9031 | 0.9024 | |
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| 0.3238 | 7.0 | 560 | 0.3178 | 0.8939 | 0.8910 | 0.8889 | 0.8892 | |
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| 0.2975 | 8.0 | 640 | 0.3016 | 0.9079 | 0.9037 | 0.9029 | 0.9028 | |
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| 0.2852 | 9.0 | 720 | 0.3090 | 0.9029 | 0.8979 | 0.8991 | 0.8974 | |
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| 0.2893 | 10.0 | 800 | 0.2966 | 0.9109 | 0.9058 | 0.9065 | 0.9057 | |
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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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