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---
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
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