---
license: apache-2.0
base_model: patnelt60/distilbert-base-uncased-finetuned-clinc
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
  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: 0.9267741935483871
---

<!-- 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-finetuned-clinc

This model is a fine-tuned version of [patnelt60/distilbert-base-uncased-finetuned-clinc](https://huggingface.co/patnelt60/distilbert-base-uncased-finetuned-clinc) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1904
- Accuracy: 0.9268

## 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: 384
- eval_batch_size: 384
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 40   | 0.4572          | 0.8619   |
| No log        | 2.0   | 80   | 0.3775          | 0.8881   |
| No log        | 3.0   | 120  | 0.3184          | 0.9013   |
| No log        | 4.0   | 160  | 0.2753          | 0.9110   |
| No log        | 5.0   | 200  | 0.2441          | 0.9187   |
| No log        | 6.0   | 240  | 0.2224          | 0.9232   |
| No log        | 7.0   | 280  | 0.2073          | 0.9248   |
| 0.3426        | 8.0   | 320  | 0.1982          | 0.9268   |
| 0.3426        | 9.0   | 360  | 0.1923          | 0.9265   |
| 0.3426        | 10.0  | 400  | 0.1904          | 0.9268   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.13.3