metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.908256880733945
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
- name: Accuracy
type: accuracy
value: 0.8990825688073395
verified: true
- name: Precision
type: precision
value: 0.9009009009009009
verified: true
- name: Recall
type: recall
value: 0.9009009009009009
verified: true
- name: AUC
type: auc
value: 0.9643770522859308
verified: true
- name: F1
type: f1
value: 0.9009009009009009
verified: true
- name: loss
type: loss
value: 0.28972649574279785
verified: true
distilbert-base-uncased-finetuned-sst2
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: 0.4493
- Accuracy: 0.9083
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1804 | 1.0 | 2105 | 0.2843 | 0.9025 |
| 0.1216 | 2.0 | 4210 | 0.3242 | 0.9025 |
| 0.0871 | 3.0 | 6315 | 0.3320 | 0.9060 |
| 0.0607 | 4.0 | 8420 | 0.3913 | 0.9025 |
| 0.0429 | 5.0 | 10525 | 0.4493 | 0.9083 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0.dev20220409+cu115
- Datasets 2.0.0
- Tokenizers 0.12.0