---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- recall
- precision
- accuracy
model-index:
- name: distilbert-sql-timeout-classifier-with-trained-tokenizer
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - name: Recall
      type: recall
      value: 0.7370441458733206
    - name: Precision
      type: precision
      value: 0.15262321144674085
    - name: Accuracy
      type: accuracy
      value: 0.8761327655857626
---

<!-- 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. -->

# distilbert-sql-timeout-classifier-with-trained-tokenizer

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Recall: 0.7370
- Precision: 0.1526
- Affect Rate: 0.1164
- Accuracy: 0.8761

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | Affect Rate | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:-----------:|:--------:|
| 0.5018        | 1.0   | 1946 | 0.3744          | 0.6929 | 0.1758    | 0.0924      | 0.8988   |
| 0.3196        | 2.0   | 3892 | 0.4938          | 0.7390 | 0.1294    | 0.1414      | 0.8512   |
| 0.2219        | 3.0   | 5838 | 0.4898          | 0.7370 | 0.1526    | 0.1164      | 0.8761   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2