Lifehouse/distilbert-sql-timeout-classifier-2024022722

1---2license: apache-2.03base_model: distilbert-base-uncased4tags:5- generated_from_trainer6datasets:7- generator8metrics:9- accuracy10model-index:11- name: distilbert-sql-timeout-classifier-202402272212  results:13  - task:14      name: Text Classification15      type: text-classification16    dataset:17      name: generator18      type: generator19      config: default20      split: train21      args: default22    metrics:23    - name: Accuracy24      type: accuracy25      value: 0.973827699018538726---27 28<!-- This model card has been generated automatically according to the information the Trainer had access to. You29should probably proofread and complete it, then remove this comment. -->30 31# distilbert-sql-timeout-classifier-202402272232 33This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the generator dataset.34It achieves the following results on the evaluation set:35- Loss: 0.114436- Accuracy: 0.973837 38## Model description39 40More information needed41 42## Intended uses & limitations43 44More information needed45 46## Training and evaluation data47 48More information needed49 50## Training procedure51 52### Training hyperparameters53 54The following hyperparameters were used during training:55- learning_rate: 2e-0556- train_batch_size: 1657- eval_batch_size: 1658- seed: 4259- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0860- lr_scheduler_type: linear61- num_epochs: 362 63### Training results64 65| Training Loss | Epoch | Step | Validation Loss | Accuracy |66|:-------------:|:-----:|:----:|:---------------:|:--------:|67| 0.063         | 1.0   | 3064 | 0.1183          | 0.9684   |68| 0.041         | 2.0   | 6128 | 0.1046          | 0.9782   |69| 0.0265        | 3.0   | 9192 | 0.1144          | 0.9738   |70 71 72### Framework versions73 74- Transformers 4.38.175- Pytorch 2.2.1+cu12176- Datasets 2.17.177- Tokenizers 0.15.278