---
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: 0.9506451612903226
---

<!-- 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: 0.2466
- Accuracy: 0.9506

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.9383        | 1.0   | 954  | 1.4511          | 0.8397   |
| 0.8485        | 2.0   | 1908 | 0.4733          | 0.9255   |
| 0.2822        | 3.0   | 2862 | 0.3070          | 0.9429   |
| 0.1515        | 4.0   | 3816 | 0.2664          | 0.9490   |
| 0.106         | 5.0   | 4770 | 0.2641          | 0.95     |
| 0.0874        | 6.0   | 5724 | 0.2536          | 0.9510   |
| 0.0764        | 7.0   | 6678 | 0.2475          | 0.9506   |
| 0.0718        | 8.0   | 7632 | 0.2450          | 0.9513   |
| 0.068         | 9.0   | 8586 | 0.2473          | 0.9497   |
| 0.0664        | 10.0  | 9540 | 0.2466          | 0.9506   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.12.1