---
license: apache-2.0
base_model: facebook/convnextv2-huge-22k-384
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 5-convnextv2-huge-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-huge-22k-384-finetuned-spiderTraining50-200

This model is a fine-tuned version of [facebook/convnextv2-huge-22k-384](https://huggingface.co./facebook/convnextv2-huge-22k-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1990
- Accuracy: 0.9439
- Precision: 0.9439
- Recall: 0.9439
- F1: 0.9425

## 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: 5
- eval_batch_size: 5
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 20
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1014        | 1.0   | 399  | 0.5745          | 0.8428   | 0.8568    | 0.8422 | 0.8357 |
| 0.5338        | 2.0   | 799  | 0.3466          | 0.8969   | 0.8995    | 0.8968 | 0.8934 |
| 0.4343        | 3.0   | 1199 | 0.2666          | 0.9239   | 0.9292    | 0.9256 | 0.9231 |
| 0.2723        | 4.0   | 1599 | 0.2246          | 0.9299   | 0.9332    | 0.9316 | 0.9299 |
| 0.1415        | 4.99  | 1995 | 0.1990          | 0.9439   | 0.9439    | 0.9439 | 0.9425 |


### Framework versions

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