---
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: 0.9503225806451613
---

<!-- 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: 0.2656
- Accuracy: 0.9503

## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.1212        | 1.0   | 1271  | 1.2698          | 0.8558   |
| 0.6441        | 2.0   | 2542  | 0.3528          | 0.9326   |
| 0.149         | 3.0   | 3813  | 0.2512          | 0.9494   |
| 0.0647        | 4.0   | 5084  | 0.2510          | 0.95     |
| 0.0406        | 5.0   | 6355  | 0.2575          | 0.9510   |
| 0.0318        | 6.0   | 7626  | 0.2592          | 0.9494   |
| 0.026         | 7.0   | 8897  | 0.2629          | 0.9503   |
| 0.023         | 8.0   | 10168 | 0.2682          | 0.95     |
| 0.0207        | 9.0   | 11439 | 0.2656          | 0.9503   |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1