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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: 5-convnextv2-base-22k-384-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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# 5-convnextv2-base-22k-384-finetuned-spiderTraining50-200 |
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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: 0.2780 |
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- Accuracy: 0.9199 |
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- Precision: 0.9173 |
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- Recall: 0.9203 |
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- F1: 0.9167 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 16 |
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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: 5 |
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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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| 1.0428 | 1.0 | 499 | 0.8337 | 0.7598 | 0.7843 | 0.7650 | 0.7530 | |
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| 0.7132 | 2.0 | 999 | 0.4413 | 0.8709 | 0.8722 | 0.8701 | 0.8661 | |
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| 0.4802 | 3.0 | 1499 | 0.3552 | 0.8869 | 0.8918 | 0.8862 | 0.8834 | |
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| 0.4238 | 4.0 | 1999 | 0.2918 | 0.9099 | 0.9095 | 0.9101 | 0.9065 | |
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| 0.2661 | 4.99 | 2495 | 0.2780 | 0.9199 | 0.9173 | 0.9203 | 0.9167 | |
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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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