---
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-spiderTraining5-100
  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-spiderTraining5-100

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: 0.7208
- Accuracy: 0.88
- Precision: 0.8822
- Recall: 0.8936
- F1: 0.8805

## 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: 3e-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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 0.96  | 6    | 1.3602          | 0.52     | 0.5198    | 0.5390 | 0.5169 |
| 1.4377        | 1.92  | 12   | 1.0444          | 0.78     | 0.7903    | 0.7869 | 0.7804 |
| 1.4377        | 2.88  | 18   | 0.8560          | 0.86     | 0.8636    | 0.8736 | 0.8598 |
| 0.9338        | 4.0   | 25   | 0.7443          | 0.86     | 0.8636    | 0.8736 | 0.8598 |
| 0.7051        | 4.8   | 30   | 0.7208          | 0.88     | 0.8822    | 0.8936 | 0.8805 |


### Framework versions

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