---
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-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. -->

# convnextv2-tiny-22k-384-finetuned-spiderTraining20-500

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: 0.2472
- Accuracy: 0.9279
- Precision: 0.9271
- Recall: 0.9247
- F1: 0.9247

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9602        | 1.0   | 125  | 0.7123          | 0.8148   | 0.8207    | 0.8080 | 0.8050 |
| 0.5216        | 2.0   | 250  | 0.3843          | 0.8909   | 0.8921    | 0.8893 | 0.8881 |
| 0.4456        | 3.0   | 375  | 0.2864          | 0.9179   | 0.9168    | 0.9099 | 0.9116 |
| 0.3673        | 4.0   | 500  | 0.2652          | 0.9229   | 0.9229    | 0.9200 | 0.9201 |
| 0.3069        | 5.0   | 625  | 0.2472          | 0.9279   | 0.9271    | 0.9247 | 0.9247 |


### Framework versions

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