pytorch tensorboard distilbert text-classification dataset:clinc_oos transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert_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: 0.9161290322580645
---

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# distilbert_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: 0.7799
- Accuracy: 0.9161

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 318  | 3.2788          | 0.7371   |
| 3.7785        | 2.0   | 636  | 1.8739          | 0.8358   |
| 3.7785        | 3.0   | 954  | 1.1618          | 0.8923   |
| 1.6926        | 4.0   | 1272 | 0.8647          | 0.9090   |
| 0.9104        | 5.0   | 1590 | 0.7799          | 0.9161   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.11.6
