metadata
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: 0.3009374737739563
verified: true
- name: matthews_correlation
type: matthews_correlation
value: 0.793630584795814
verified: true
binary-classification
This model is a fine-tuned version of 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