---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: autoevaluate-binary-classification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8967889908256881
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: sst2
      split: validation
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8967889908256881
      verified: true
    - name: Precision
      type: precision
      value: 0.8898678414096917
      verified: true
    - name: Recall
      type: recall
      value: 0.9099099099099099
      verified: true
    - name: AUC
      type: auc
      value: 0.9672423591816114
      verified: true
    - name: F1
      type: f1
      value: 0.8997772828507795
      verified: true
    - name: loss
      type: loss
      value: 0.3009158968925476
      verified: true
---

<!-- 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