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---
license: apache-2.0
base_model: facebook/convnextv2-nano-22k-384
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 10-convnextv2-nano-22k-384-finetuned-spiderTraining20-500
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-convnextv2-nano-22k-384-finetuned-spiderTraining20-500
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.
It achieves the following results on the evaluation set:
- Loss: 0.2969
- Accuracy: 0.9099
- Precision: 0.9049
- Recall: 0.9057
- F1: 0.9048
## 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: 5e-05
- train_batch_size: 25
- eval_batch_size: 25
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 100
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7156 | 1.0 | 80 | 1.4314 | 0.6316 | 0.6182 | 0.6246 | 0.6155 |
| 0.7565 | 2.0 | 160 | 0.6340 | 0.8168 | 0.8213 | 0.8074 | 0.8095 |
| 0.5802 | 3.0 | 240 | 0.4633 | 0.8589 | 0.8566 | 0.8545 | 0.8539 |
| 0.4765 | 4.0 | 320 | 0.4006 | 0.8759 | 0.8746 | 0.8709 | 0.8708 |
| 0.3644 | 5.0 | 400 | 0.3530 | 0.9019 | 0.8995 | 0.8984 | 0.8979 |
| 0.3622 | 6.0 | 480 | 0.3326 | 0.9049 | 0.9019 | 0.9020 | 0.9013 |
| 0.3232 | 7.0 | 560 | 0.3180 | 0.8939 | 0.8910 | 0.8889 | 0.8892 |
| 0.2968 | 8.0 | 640 | 0.3018 | 0.9089 | 0.9050 | 0.9039 | 0.9039 |
| 0.285 | 9.0 | 720 | 0.3097 | 0.9029 | 0.8979 | 0.8991 | 0.8974 |
| 0.2889 | 10.0 | 800 | 0.2969 | 0.9099 | 0.9049 | 0.9057 | 0.9048 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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