metadata
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: roberta-sst2-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.930045871559633
roberta-sst2-distilled
This model is a fine-tuned version of roberta-base on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2485
- Accuracy: 0.9300
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: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.257 | 1.0 | 527 | 0.2575 | 0.9117 |
| 0.2386 | 2.0 | 1054 | 0.2469 | 0.9369 |
| 0.2331 | 3.0 | 1581 | 0.2484 | 0.9358 |
| 0.2289 | 4.0 | 2108 | 0.2516 | 0.9278 |
| 0.2266 | 5.0 | 2635 | 0.2499 | 0.9335 |
| 0.2252 | 6.0 | 3162 | 0.2477 | 0.9312 |
| 0.2238 | 7.0 | 3689 | 0.2485 | 0.9300 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0