transformers pytorch distilbert text-classification generated_from_trainer dataset:clinc_oos base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- clinc_oos
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-distilled-clinc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      config: plus
      split: validation
      args: plus
    metrics:
    - type: accuracy
      value: 0.9496774193548387
      name: Accuracy
---

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# distilbert-base-uncased-distilled-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: 0.2163
- Accuracy: 0.9497

## 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: 32
- eval_batch_size: 32
- 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   | 477  | 1.0819          | 0.7584   |
| 1.7363        | 2.0   | 954  | 0.4714          | 0.8919   |
| 0.7368        | 3.0   | 1431 | 0.2924          | 0.9352   |
| 0.3548        | 4.0   | 1908 | 0.2508          | 0.9416   |
| 0.2432        | 5.0   | 2385 | 0.2338          | 0.9439   |
| 0.2052        | 6.0   | 2862 | 0.2257          | 0.9497   |
| 0.1898        | 7.0   | 3339 | 0.2208          | 0.9487   |
| 0.1821        | 8.0   | 3816 | 0.2184          | 0.9494   |
| 0.1772        | 9.0   | 4293 | 0.2175          | 0.9484   |
| 0.1739        | 10.0  | 4770 | 0.2163          | 0.9497   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
