| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - glue |
| metrics: |
| - accuracy |
| model-index: |
| - name: autoevaluate-binary-classification |
| results: |
| - task: |
| type: text-classification |
| name: Text Classification |
| dataset: |
| name: glue |
| type: glue |
| args: sst2 |
| metrics: |
| - type: accuracy |
| value: 0.8967889908256881 |
| name: Accuracy |
| - type: accuracy |
| value: 0.8967889908256881 |
| name: Accuracy |
| verified: true |
| - type: precision |
| value: 0.8898678414096917 |
| name: Precision |
| verified: true |
| - type: recall |
| value: 0.9099099099099099 |
| name: Recall |
| verified: true |
| - type: auc |
| value: 0.967247621453229 |
| name: AUC |
| verified: true |
| - type: f1 |
| value: 0.8997772828507795 |
| name: F1 |
| verified: true |
| - type: loss |
| value: 0.30091655254364014 |
| name: loss |
| verified: true |
| - type: matthews_correlation |
| value: 0.793630584795814 |
| name: matthews_correlation |
| verified: true |
| - type: accuracy |
| value: 0.8967889908256881 |
| name: Accuracy |
| verified: true |
| verifyToken: '1234' |
| - type: precision |
| value: 0.8898678414096917 |
| name: Precision |
| verified: true |
| verifyToken: '1234' |
| - type: recall |
| value: 0.9099099099099099 |
| name: Recall |
| verified: true |
| verifyToken: '1234' |
| - type: auc |
| value: 0.967247621453229 |
| name: AUC |
| verified: true |
| verifyToken: '1234' |
| - type: f1 |
| value: 0.8997772828507795 |
| name: F1 |
| verified: true |
| verifyToken: '1234' |
| - type: loss |
| value: 0.30091655254364014 |
| name: loss |
| verified: true |
| verifyToken: '1234' |
| - type: matthews_correlation |
| value: 0.793630584795814 |
| name: matthews_correlation |
| verified: true |
| verifyToken: '1234' |
| --- |
| |
| <!-- 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. --> |
|
|
| # binary-classification |
|
|
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3009 |
| - Accuracy: 0.8968 |
|
|
| ## 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: 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: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.175 | 1.0 | 4210 | 0.3009 | 0.8968 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.19.2 |
| - Pytorch 1.11.0+cu113 |
| - Datasets 2.2.2 |
| - Tokenizers 0.12.1 |
|
|
