---
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
      config: plus
      split: train
      args: plus
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9496774193548387
---

<!-- 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: 0.3020
- Accuracy: 0.9497

## 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: 24
- eval_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6598        | 1.0   | 636  | 1.5602          | 0.8123   |
| 1.0323        | 2.0   | 1272 | 0.6060          | 0.9074   |
| 0.4494        | 3.0   | 1908 | 0.3979          | 0.9387   |
| 0.294         | 4.0   | 2544 | 0.3424          | 0.9468   |
| 0.2393        | 5.0   | 3180 | 0.3252          | 0.9481   |
| 0.216         | 6.0   | 3816 | 0.3124          | 0.9490   |
| 0.204         | 7.0   | 4452 | 0.3100          | 0.9494   |
| 0.1969        | 8.0   | 5088 | 0.3039          | 0.9494   |
| 0.1939        | 9.0   | 5724 | 0.3031          | 0.9506   |
| 0.1918        | 10.0  | 6360 | 0.3020          | 0.9497   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2