transformers pytorch distilbert text-classification generated_from_trainer dataset:clinc_oos base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- clinc_oos
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-clinc_oos
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      config: plus
      split: validation
      args: plus
    metrics:
    - type: accuracy
      value:
        accuracy: 0.9248387096774193
      name: Accuracy
    - type: f1
      value:
        f1: 0.924017622321749
      name: F1
---

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# distilbert-base-uncased-finetuned-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: 0.6012
- Accuracy: {'accuracy': 0.9248387096774193}
- F1: {'f1': 0.924017622321749}

## Model Training Details

| Parameter            | Value                             |
|----------------------|-----------------------------------|
| **Task**             | text-classification               |
| **Base Model Name**  | distilbert-base-uncased           |
| **Dataset Name**     | clinc_oos                         |
| **Dataset Config**   | plus                              |
| **Batch Size**       | 16                                |
| **Number of Epochs** | 3                                 |
| **Learning Rate**    | 0.00002                           |


## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
| 4.3563        | 1.0   | 954  | 2.0254          | {'accuracy': 0.8274193548387097} | {'f1': 0.8157244857086648} |
| 1.5387        | 2.0   | 1908 | 0.8120          | {'accuracy': 0.9129032258064517} | {'f1': 0.9118433401777696} |
| 0.6711        | 3.0   | 2862 | 0.6012          | {'accuracy': 0.9248387096774193} | {'f1': 0.924017622321749}  |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
