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---
license: apache-2.0
base_model: zkdeng/10-convnextv2-base-22k-384-finetuned-spiderTraining1000-1000
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 10-finetuned-ausSpiders2000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 10-finetuned-ausSpiders2000
This model is a fine-tuned version of [zkdeng/10-convnextv2-base-22k-384-finetuned-spiderTraining1000-1000](https://huggingface.co./zkdeng/10-convnextv2-base-22k-384-finetuned-spiderTraining1000-1000) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0184
- Accuracy: 0.9929
- Precision: 0.9955
- Recall: 0.9910
- F1: 0.9932
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2684 | 1.0 | 141 | 0.1271 | 0.9503 | 0.9350 | 0.9199 | 0.9198 |
| 0.1698 | 2.0 | 282 | 0.1668 | 0.9485 | 0.9229 | 0.9195 | 0.9123 |
| 0.1538 | 3.0 | 423 | 0.0906 | 0.9645 | 0.9764 | 0.9365 | 0.9523 |
| 0.153 | 4.0 | 564 | 0.0860 | 0.9707 | 0.9685 | 0.9451 | 0.9525 |
| 0.0699 | 5.0 | 705 | 0.0528 | 0.9813 | 0.9830 | 0.9728 | 0.9776 |
| 0.1107 | 6.0 | 846 | 0.0460 | 0.9831 | 0.9832 | 0.9879 | 0.9855 |
| 0.0647 | 7.0 | 987 | 0.0319 | 0.9849 | 0.9905 | 0.9765 | 0.9829 |
| 0.0461 | 8.0 | 1128 | 0.0350 | 0.9840 | 0.9866 | 0.9710 | 0.9776 |
| 0.0371 | 9.0 | 1269 | 0.0198 | 0.9920 | 0.9952 | 0.9903 | 0.9927 |
| 0.0496 | 10.0 | 1410 | 0.0184 | 0.9929 | 0.9955 | 0.9910 | 0.9932 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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