---
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-384-0.0005-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-384-0.0005-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: 0.1840
- Accuracy: 0.9439
- Precision: 0.9448
- Recall: 0.9411
- F1: 0.9424

## 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: 0.0005
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.934         | 1.0   | 125  | 0.6076          | 0.8118   | 0.8237    | 0.8060 | 0.8064 |
| 0.5826        | 2.0   | 250  | 0.4684          | 0.8529   | 0.8685    | 0.8506 | 0.8436 |
| 0.4743        | 3.0   | 375  | 0.3240          | 0.9059   | 0.9083    | 0.9006 | 0.9023 |
| 0.3488        | 4.0   | 500  | 0.2431          | 0.9369   | 0.9345    | 0.9348 | 0.9335 |
| 0.2307        | 5.0   | 625  | 0.1840          | 0.9439   | 0.9448    | 0.9411 | 0.9424 |


### Framework versions

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