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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-base-uncased-finetuned-sql-classification-with_questionV2
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. -->
# bert-base-uncased-finetuned-sql-classification-with_questionV2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4672
- Accuracy: 0.9050
- F1: 0.9172
- Precision: 0.8744
- Recall: 0.9645
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5044 | 1.0 | 645 | 0.4149 | 0.8376 | 0.8617 | 0.8046 | 0.9275 |
| 0.3823 | 2.0 | 1290 | 0.3644 | 0.8535 | 0.8797 | 0.7965 | 0.9822 |
| 0.3289 | 3.0 | 1935 | 0.2915 | 0.8857 | 0.8998 | 0.8620 | 0.9410 |
| 0.2576 | 4.0 | 2580 | 0.3151 | 0.8860 | 0.9004 | 0.8602 | 0.9446 |
| 0.2224 | 5.0 | 3225 | 0.3157 | 0.9039 | 0.9155 | 0.8795 | 0.9545 |
| 0.1899 | 6.0 | 3870 | 0.3412 | 0.9016 | 0.9140 | 0.8731 | 0.9588 |
| 0.165 | 7.0 | 4515 | 0.3729 | 0.8973 | 0.9116 | 0.8591 | 0.9709 |
| 0.1265 | 8.0 | 5160 | 0.4119 | 0.9035 | 0.9162 | 0.8702 | 0.9673 |
| 0.1162 | 9.0 | 5805 | 0.4244 | 0.9066 | 0.9184 | 0.8766 | 0.9645 |
| 0.0995 | 10.0 | 6450 | 0.4672 | 0.9050 | 0.9172 | 0.8744 | 0.9645 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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