---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      config: plus
      split: validation
      args: plus
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9490322580645161
---

<!-- 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-clinc

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: 0.1852
- Accuracy: 0.9490

## 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: 0.0004
- train_batch_size: 1280
- eval_batch_size: 1280
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9692        | 1.0   | 12   | 1.3486          | 0.6574   |
| 1.1867        | 2.0   | 24   | 0.5409          | 0.8884   |
| 0.5614        | 3.0   | 36   | 0.2845          | 0.9387   |
| 0.295         | 4.0   | 48   | 0.2234          | 0.9471   |
| 0.1729        | 5.0   | 60   | 0.2021          | 0.9487   |
| 0.1574        | 6.0   | 72   | 0.1942          | 0.9513   |
| 0.1477        | 7.0   | 84   | 0.1895          | 0.9510   |
| 0.1446        | 8.0   | 96   | 0.1870          | 0.9497   |
| 0.1405        | 9.0   | 108  | 0.1856          | 0.9494   |
| 0.1382        | 10.0  | 120  | 0.1852          | 0.9490   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3