---
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-spiderTraining50-2000
  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-spiderTraining50-2000

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: 0.2220
- Accuracy: 0.9353
- Precision: 0.9355
- Recall: 0.9353
- F1: 0.9352

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7311        | 1.0   | 1250 | 0.4761          | 0.8637   | 0.8682    | 0.8637 | 0.8637 |
| 0.5924        | 2.0   | 2500 | 0.3177          | 0.9084   | 0.9108    | 0.9087 | 0.9087 |
| 0.4035        | 3.0   | 3750 | 0.2668          | 0.9200   | 0.9211    | 0.9202 | 0.9198 |
| 0.4343        | 4.0   | 5000 | 0.2395          | 0.9290   | 0.9302    | 0.9289 | 0.9290 |
| 0.381         | 5.0   | 6250 | 0.2220          | 0.9353   | 0.9355    | 0.9353 | 0.9352 |


### Framework versions

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