---
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: 0.9503225806451613
---

<!-- 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: 0.2869
- 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.026785267717638298
- num_epochs: 24

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 318  | 2.1228          | 0.7194   |
| 2.5433        | 2.0   | 636  | 0.8036          | 0.8935   |
| 2.5433        | 3.0   | 954  | 0.4630          | 0.9355   |
| 0.7139        | 4.0   | 1272 | 0.3767          | 0.9429   |
| 0.3352        | 5.0   | 1590 | 0.3417          | 0.9461   |
| 0.3352        | 6.0   | 1908 | 0.3249          | 0.95     |
| 0.2555        | 7.0   | 2226 | 0.3141          | 0.9487   |
| 0.2237        | 8.0   | 2544 | 0.3089          | 0.9490   |
| 0.2237        | 9.0   | 2862 | 0.3039          | 0.9487   |
| 0.2098        | 10.0  | 3180 | 0.3040          | 0.9487   |
| 0.2098        | 11.0  | 3498 | 0.2971          | 0.9516   |
| 0.2004        | 12.0  | 3816 | 0.2945          | 0.95     |
| 0.1949        | 13.0  | 4134 | 0.2967          | 0.9468   |
| 0.1949        | 14.0  | 4452 | 0.2912          | 0.9497   |
| 0.1905        | 15.0  | 4770 | 0.2907          | 0.9513   |
| 0.1883        | 16.0  | 5088 | 0.2927          | 0.9487   |
| 0.1883        | 17.0  | 5406 | 0.2901          | 0.9503   |
| 0.1852        | 18.0  | 5724 | 0.2879          | 0.9497   |
| 0.184         | 19.0  | 6042 | 0.2895          | 0.95     |
| 0.184         | 20.0  | 6360 | 0.2876          | 0.9519   |
| 0.1828        | 21.0  | 6678 | 0.2871          | 0.9503   |
| 0.1828        | 22.0  | 6996 | 0.2867          | 0.9510   |
| 0.1816        | 23.0  | 7314 | 0.2868          | 0.9503   |
| 0.1813        | 24.0  | 7632 | 0.2869          | 0.9503   |


### Framework versions

- Transformers 4.16.2
- Pytorch 2.4.1+cu121
- Datasets 1.16.1
- Tokenizers 0.19.1