---
license: apache-2.0
base_model: facebook/convnextv2-tiny-22k-384
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnextv2-tiny-22k-384-finetuned-spiderTraining10-1000
  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. -->

# convnextv2-tiny-22k-384-finetuned-spiderTraining10-1000

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.
It achieves the following results on the evaluation set:
- Loss: 0.0815
- Accuracy: 0.9770
- Precision: 0.9760
- Recall: 0.9770
- F1: 0.9763

## 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: 16
- eval_batch_size: 16
- seed: 42
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4288        | 1.0   | 125  | 0.2043          | 0.9530   | 0.9533    | 0.9524 | 0.9523 |
| 0.2463        | 2.0   | 250  | 0.1362          | 0.9620   | 0.9619    | 0.9617 | 0.9612 |
| 0.2434        | 3.0   | 375  | 0.0860          | 0.9710   | 0.9699    | 0.9709 | 0.9703 |
| 0.1694        | 4.0   | 500  | 0.0805          | 0.9770   | 0.9763    | 0.9768 | 0.9765 |
| 0.1505        | 5.0   | 625  | 0.0815          | 0.9770   | 0.9760    | 0.9770 | 0.9763 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3