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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
name: autoevaluate-binary-classification
results:
- 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
foo: 1234
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
---
<!-- 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
|