---
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-spiderTraining1000-1000
  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-spiderTraining1000-1000

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: 0.5648
- Accuracy: 0.8509
- Precision: 0.8513
- Recall: 0.8405
- F1: 0.8436

## 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
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.8531        | 1.0   | 6858  | 1.3392          | 0.6713   | 0.6983    | 0.6342 | 0.6413 |
| 1.2682        | 2.0   | 13717 | 0.8656          | 0.7746   | 0.7827    | 0.7572 | 0.7591 |
| 1.0224        | 3.0   | 20576 | 0.6918          | 0.8165   | 0.8210    | 0.8029 | 0.8059 |
| 0.9031        | 4.0   | 27435 | 0.6062          | 0.8400   | 0.8426    | 0.8281 | 0.8318 |
| 0.8482        | 5.0   | 34290 | 0.5648          | 0.8509   | 0.8513    | 0.8405 | 0.8436 |


### Framework versions

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