---
license: apache-2.0
base_model: facebook/convnextv2-base-22k-384
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 5-convnextv2-base-22k-384-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. -->

# 5-convnextv2-base-22k-384-finetuned-spiderTraining50-200

This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2780
- Accuracy: 0.9199
- Precision: 0.9173
- Recall: 0.9203
- F1: 0.9167

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0428        | 1.0   | 499  | 0.8337          | 0.7598   | 0.7843    | 0.7650 | 0.7530 |
| 0.7132        | 2.0   | 999  | 0.4413          | 0.8709   | 0.8722    | 0.8701 | 0.8661 |
| 0.4802        | 3.0   | 1499 | 0.3552          | 0.8869   | 0.8918    | 0.8862 | 0.8834 |
| 0.4238        | 4.0   | 1999 | 0.2918          | 0.9099   | 0.9095    | 0.9101 | 0.9065 |
| 0.2661        | 4.99  | 2495 | 0.2780          | 0.9199   | 0.9173    | 0.9203 | 0.9167 |


### Framework versions

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