---
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-384-0.0001-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-384-0.0001-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: 0.4409
- Accuracy: 0.8729
- Precision: 0.8706
- Recall: 0.8714
- F1: 0.8672

## 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: 0.0001
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6172        | 1.0   | 125  | 1.2793          | 0.6767   | 0.7024    | 0.6713 | 0.6581 |
| 0.9187        | 2.0   | 250  | 0.7649          | 0.7918   | 0.8129    | 0.7878 | 0.7869 |
| 0.6421        | 3.0   | 375  | 0.5605          | 0.8458   | 0.8577    | 0.8418 | 0.8397 |
| 0.5017        | 4.0   | 500  | 0.4645          | 0.8719   | 0.8717    | 0.8722 | 0.8672 |
| 0.4235        | 5.0   | 625  | 0.4409          | 0.8729   | 0.8706    | 0.8714 | 0.8672 |


### Framework versions

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