---
license: mit
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: userutterance_classification_verplus
  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: 0.9619354838709677
---

<!-- 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. -->

# userutterance_classification_verplus

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2270
- Accuracy: 0.9619

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.0219        | 0.21  | 200  | 4.9813          | 0.0077   |
| 4.8915        | 0.42  | 400  | 4.5741          | 0.1155   |
| 4.2736        | 0.63  | 600  | 3.5359          | 0.4719   |
| 3.2701        | 0.84  | 800  | 2.4291          | 0.7429   |
| 2.3578        | 1.05  | 1000 | 1.5793          | 0.8413   |
| 1.5695        | 1.26  | 1200 | 1.0029          | 0.8994   |
| 1.0412        | 1.47  | 1400 | 0.6475          | 0.9187   |
| 0.7034        | 1.68  | 1600 | 0.4439          | 0.9303   |
| 0.501         | 1.89  | 1800 | 0.3400          | 0.9381   |
| 0.3187        | 2.1   | 2000 | 0.2793          | 0.9439   |
| 0.2185        | 2.31  | 2200 | 0.2538          | 0.9490   |
| 0.1669        | 2.52  | 2400 | 0.2210          | 0.9523   |
| 0.1081        | 2.73  | 2600 | 0.2225          | 0.9519   |
| 0.1004        | 2.94  | 2800 | 0.2136          | 0.9555   |
| 0.0665        | 3.14  | 3000 | 0.2078          | 0.9561   |
| 0.0509        | 3.35  | 3200 | 0.2155          | 0.9568   |
| 0.05          | 3.56  | 3400 | 0.2107          | 0.9581   |
| 0.0527        | 3.77  | 3600 | 0.2171          | 0.9568   |
| 0.0447        | 3.98  | 3800 | 0.2128          | 0.9590   |
| 0.0259        | 4.19  | 4000 | 0.2099          | 0.9587   |
| 0.0279        | 4.4   | 4200 | 0.2179          | 0.9577   |
| 0.0176        | 4.61  | 4400 | 0.2191          | 0.9574   |
| 0.0288        | 4.82  | 4600 | 0.2216          | 0.9590   |
| 0.0328        | 5.03  | 4800 | 0.2237          | 0.9606   |
| 0.0154        | 5.24  | 5000 | 0.2241          | 0.9616   |
| 0.0157        | 5.45  | 5200 | 0.2265          | 0.9603   |
| 0.023         | 5.66  | 5400 | 0.2276          | 0.9613   |
| 0.0178        | 5.87  | 5600 | 0.2270          | 0.9619   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3