metadata
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
distilbert-sql-timeout-classifier-with-trained-tokenizer
This model is a fine-tuned version of 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