---
license: apache-2.0
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
      args: plus
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9148387096774193
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      config: small
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8627272727272727
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.861664336839455
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.8627272727272727
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.8787483927993249
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9187704194260485
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.8627272727272727
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.8627272727272727
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8842101413648463
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.8627272727272727
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.8585620882832584
      verified: true
    - name: loss
      type: loss
      value: 0.9942931532859802
      verified: true
---

<!-- 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 [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7760
- Accuracy: 0.9148

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2994        | 1.0   | 318  | 3.3016          | 0.7442   |
| 2.6387        | 2.0   | 636  | 1.8892          | 0.8339   |
| 1.5535        | 3.0   | 954  | 1.1602          | 0.8948   |
| 1.0139        | 4.0   | 1272 | 0.8619          | 0.9084   |
| 0.7936        | 5.0   | 1590 | 0.7760          | 0.9148   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.6