---
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
---

<!-- 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. -->

# roberta-sst2-distilled

This model is a fine-tuned version of [roberta-base](https://huggingface.co/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