pytorch distilbert generated_from_trainer dataset:clinc_oos license:apache-2.0 model-index region:us
---
datasets:
- clinc_oos
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-clinc_newtest_v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      args: plus
    metrics:
    - type: accuracy
      value: 0.954516129032258
      name: Accuracy
---

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# distilbert-base-uncased-finetuned-clinc_newtest_v2

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: 0.0766
- Accuracy: 0.9545

## 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: 0.00017198452872146017
- train_batch_size: 32
- eval_batch_size: 48
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4502        | 1.0   | 477  | 0.1245          | 0.9271   |
| 0.0968        | 2.0   | 954  | 0.0967          | 0.9390   |
| 0.0766        | 3.0   | 1431 | 0.0918          | 0.9455   |
| 0.068         | 4.0   | 1908 | 0.0835          | 0.9510   |
| 0.0647        | 5.0   | 2385 | 0.0822          | 0.9510   |
| 0.0618        | 6.0   | 2862 | 0.0800          | 0.9510   |
| 0.0604        | 7.0   | 3339 | 0.0792          | 0.9523   |
| 0.0586        | 8.0   | 3816 | 0.0784          | 0.9552   |
| 0.0575        | 9.0   | 4293 | 0.0772          | 0.9529   |
| 0.056         | 10.0  | 4770 | 0.0766          | 0.9545   |


### Framework versions

- Transformers 4.16.2
- Pytorch 2.4.1+cu121
- Datasets 2.0.0
- Tokenizers 0.19.1
