---
license: apache-2.0
base_model: facebook/convnextv2-tiny-22k-384
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 30-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. -->

# 30-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: 0.4873
- Accuracy: 0.8859
- Precision: 0.8884
- Recall: 0.8867
- F1: 0.8844

## 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: 0.0005
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5725        | 1.0   | 125  | 1.2861          | 0.6547   | 0.7027    | 0.6550 | 0.6374 |
| 1.1094        | 2.0   | 250  | 0.8928          | 0.7387   | 0.7725    | 0.7361 | 0.7306 |
| 1.153         | 3.0   | 375  | 0.9601          | 0.7117   | 0.7607    | 0.7069 | 0.7092 |
| 0.9492        | 4.0   | 500  | 0.9426          | 0.7107   | 0.7637    | 0.7084 | 0.7107 |
| 0.8308        | 5.0   | 625  | 0.8229          | 0.7608   | 0.7874    | 0.7525 | 0.7510 |
| 0.6969        | 6.0   | 750  | 0.8728          | 0.7658   | 0.7928    | 0.7620 | 0.7570 |
| 0.6008        | 7.0   | 875  | 0.7126          | 0.7968   | 0.8142    | 0.7936 | 0.7935 |
| 0.5553        | 8.0   | 1000 | 0.7980          | 0.7788   | 0.7986    | 0.7810 | 0.7746 |
| 0.6149        | 9.0   | 1125 | 0.8481          | 0.7908   | 0.8150    | 0.7983 | 0.7910 |
| 0.4931        | 10.0  | 1250 | 0.7269          | 0.8068   | 0.8216    | 0.8081 | 0.8015 |
| 0.4624        | 11.0  | 1375 | 0.7513          | 0.7978   | 0.8147    | 0.7952 | 0.7912 |
| 0.4795        | 12.0  | 1500 | 0.7173          | 0.8218   | 0.8362    | 0.8147 | 0.8178 |
| 0.4348        | 13.0  | 1625 | 0.6962          | 0.8158   | 0.8427    | 0.8179 | 0.8181 |
| 0.4129        | 14.0  | 1750 | 0.6100          | 0.8408   | 0.8426    | 0.8371 | 0.8347 |
| 0.3412        | 15.0  | 1875 | 0.7606          | 0.8148   | 0.8226    | 0.8142 | 0.8107 |
| 0.3238        | 16.0  | 2000 | 0.7354          | 0.8118   | 0.8305    | 0.8103 | 0.8079 |
| 0.2922        | 17.0  | 2125 | 0.7480          | 0.8228   | 0.8378    | 0.8250 | 0.8217 |
| 0.2478        | 18.0  | 2250 | 0.6308          | 0.8509   | 0.8613    | 0.8475 | 0.8472 |
| 0.2624        | 19.0  | 2375 | 0.6509          | 0.8338   | 0.8393    | 0.8328 | 0.8284 |
| 0.2183        | 20.0  | 2500 | 0.6546          | 0.8478   | 0.8568    | 0.8463 | 0.8454 |
| 0.2503        | 21.0  | 2625 | 0.6081          | 0.8549   | 0.8580    | 0.8541 | 0.8519 |
| 0.2578        | 22.0  | 2750 | 0.6065          | 0.8519   | 0.8546    | 0.8495 | 0.8469 |
| 0.2516        | 23.0  | 2875 | 0.5926          | 0.8629   | 0.8620    | 0.8603 | 0.8579 |
| 0.1922        | 24.0  | 3000 | 0.5702          | 0.8599   | 0.8626    | 0.8583 | 0.8545 |
| 0.1646        | 25.0  | 3125 | 0.5360          | 0.8779   | 0.8803    | 0.8770 | 0.8738 |
| 0.1595        | 26.0  | 3250 | 0.5625          | 0.8779   | 0.8814    | 0.8778 | 0.8747 |
| 0.1397        | 27.0  | 3375 | 0.5167          | 0.8889   | 0.8910    | 0.8887 | 0.8870 |
| 0.1323        | 28.0  | 3500 | 0.5151          | 0.8819   | 0.8850    | 0.8821 | 0.8796 |
| 0.1355        | 29.0  | 3625 | 0.4900          | 0.8899   | 0.8918    | 0.8904 | 0.8883 |
| 0.1673        | 30.0  | 3750 | 0.4873          | 0.8859   | 0.8884    | 0.8867 | 0.8844 |


### Framework versions

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