---
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-384-1e-05-finetuned-spiderTraining50-200
  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-384-1e-05-finetuned-spiderTraining50-200

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: 2.2312
- Accuracy: 0.5025
- Precision: 0.5189
- Recall: 0.5032
- F1: 0.4858

## 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: 1e-05
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 3.6298        | 1.0   | 125  | 3.4996          | 0.1481   | 0.1491    | 0.1491 | 0.1288 |
| 2.9856        | 2.0   | 250  | 2.8859          | 0.3263   | 0.3797    | 0.3289 | 0.3014 |
| 2.5878        | 3.0   | 375  | 2.5019          | 0.4404   | 0.4744    | 0.4397 | 0.4201 |
| 2.3898        | 4.0   | 500  | 2.2965          | 0.4905   | 0.5187    | 0.4896 | 0.4703 |
| 2.3254        | 5.0   | 625  | 2.2312          | 0.5025   | 0.5189    | 0.5032 | 0.4858 |


### Framework versions

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