---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      args: plus
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9351612903225807
---

<!-- 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. -->

# distilbert-base-uncased-distilled

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0695
- Accuracy: 0.9352

## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.68          | 1.0   | 318  | 0.3985          | 0.6926   |
| 0.3163        | 2.0   | 636  | 0.1939          | 0.8732   |
| 0.1847        | 3.0   | 954  | 0.1241          | 0.9158   |
| 0.1338        | 4.0   | 1272 | 0.0976          | 0.9271   |
| 0.1097        | 5.0   | 1590 | 0.0852          | 0.93     |
| 0.0969        | 6.0   | 1908 | 0.0781          | 0.9342   |
| 0.0891        | 7.0   | 2226 | 0.0739          | 0.9339   |
| 0.0845        | 8.0   | 2544 | 0.0714          | 0.9352   |
| 0.0814        | 9.0   | 2862 | 0.0700          | 0.9361   |
| 0.0801        | 10.0  | 3180 | 0.0695          | 0.9352   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.10.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1