---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-fineturned-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: 0.9383870967741935
---

<!-- 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-fineturned-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: 0.0292
- Accuracy: 0.9384

## 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: 0.0004
- train_batch_size: 1280
- eval_batch_size: 1280
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0092        | 1.0   | 12   | 0.6032          | 0.4881   |
| 0.5561        | 2.0   | 24   | 0.2063          | 0.7877   |
| 0.2481        | 3.0   | 36   | 0.0843          | 0.8977   |
| 0.1194        | 4.0   | 48   | 0.0525          | 0.9223   |
| 0.0563        | 5.0   | 60   | 0.0398          | 0.9326   |
| 0.0474        | 6.0   | 72   | 0.0351          | 0.9365   |
| 0.0423        | 7.0   | 84   | 0.0318          | 0.9358   |
| 0.0397        | 8.0   | 96   | 0.0306          | 0.9377   |
| 0.0378        | 9.0   | 108  | 0.0297          | 0.9381   |
| 0.0359        | 10.0  | 120  | 0.0292          | 0.9384   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3