---
license: apache-2.0
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
      args: plus
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.944516129032258
---

<!-- 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: 0.2565
- Accuracy: 0.9445

## Model description

This is subsequent example of knowledge-distillation used [`transformers.Trainer.hyperparameter_search`](https://huggingface.co./docs/transformers/main_classes/trainer#transformers.Trainer.hyperparameter_search) with the default Optuna back to find optimal values for the following hyperparameters:
- `num_train_epochs`
- `alpha`
- `temperature`

## Intended uses & limitations

More information needed

## Training and evaluation data

The training and evaluation data come straight from the `train` and `validation` splits in the clinc_oos dataset, respectively; and tokenized using the `distilbert-base-uncased` tokenization.

## Training procedure

Hyperparameter-search was done via default backend Optuna, leading to the values below.

Please see page 228 in Chapter 8: Making Transformers Efficient in Production, Natural Language Processing with Transformers, May 2022.

### Training hyperparameters

The following hyperparameters were used during training:
- num_epochs: 10
- alpha: 0.5858821400787321
- temperature: 4.917005721212045
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 8675309
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear


### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 318  | 2.0029          | 0.6910   |
| 2.3585        | 2.0   | 636  | 1.0585          | 0.8626   |
| 2.3585        | 3.0   | 954  | 0.6001          | 0.9058   |
| 0.9378        | 4.0   | 1272 | 0.4072          | 0.9348   |
| 0.4053        | 5.0   | 1590 | 0.3274          | 0.9387   |
| 0.4053        | 6.0   | 1908 | 0.2951          | 0.9426   |
| 0.2433        | 7.0   | 2226 | 0.2734          | 0.9439   |
| 0.1871        | 8.0   | 2544 | 0.2625          | 0.9452   |
| 0.1871        | 9.0   | 2862 | 0.2566          | 0.9452   |
| 0.166         | 10.0  | 3180 | 0.2565          | 0.9445   |


### Framework versions

- Transformers 4.16.2
- Pytorch 2.1.2+cu121
- Datasets 1.16.1
- Tokenizers 0.15.1