metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: distilbert-sql-timeout-classifier-with-features-4096-sql-normalized
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8906033190875284
distilbert-sql-timeout-classifier-with-features-4096-sql-normalized
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: 0.5598
- Accuracy: 0.8906
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5057 | 1.0 | 1938 | 0.4010 | 0.8793 |
| 0.3304 | 2.0 | 3876 | 0.4271 | 0.8945 |
| 0.2143 | 3.0 | 5814 | 0.4978 | 0.8872 |
| 0.2079 | 4.0 | 7752 | 0.6021 | 0.8776 |
| 0.1329 | 5.0 | 9690 | 0.5598 | 0.8906 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2