---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-clinc_oos-distilled-clinc_oos
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      config: plus
      split: validation
      args: plus
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9158064516129032
---

<!-- 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. -->

# bert-base-uncased-finetuned-clinc_oos-distilled-clinc_oos

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: 0.7724
- Accuracy: 0.9158

## Model Training Details

| Parameter            | Value                                            |
|----------------------|--------------------------------------------------|
| **Task**             | text-classification                              |
| **Teacher Model**    | bert-base-uncased-finetuned-clinc_oos            |
| **Student Model**    | distilbert-base-uncased                          |
| **Dataset Name**     | clinc_oos                                        |
| **Dataset Config**   | plus                                             |
| **Evaluation Dataset**| validation                                       |
| **Batch Size**       | 48                                               |
| **Number of Epochs** | 5                                                |
| **Learning Rate**    | 0.00002                                          |
| **Alpha***            | 1                                                |
*alpha: (Total_loss = alpha * Loss_CE + (1-alpha) * Loss_KD)

## 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
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 318  | 3.2762          | 0.7284   |
| 3.7824        | 2.0   | 636  | 1.8624          | 0.8358   |
| 3.7824        | 3.0   | 954  | 1.1512          | 0.8984   |
| 1.6858        | 4.0   | 1272 | 0.8540          | 0.9132   |
| 0.8983        | 5.0   | 1590 | 0.7724          | 0.9158   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3