transformers tensorboard safetensors distilbert text-classification generated_from_trainer dataset:clinc/clinc_oos base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- clinc/clinc_oos
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-clinc
  results: []
---

<!-- 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 clinc/clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7872
- Accuracy: 0.9206

## Model description

More information needed

## How to use

You can use this model directly with a pipeline for text classification:

```python
>>> from transformers import pipeline
>>> import torch
>>> bert_ckpt = "seddiktrk/distilbert-base-uncased-finetuned-clinc"
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> pipe = pipeline("text-classification", model=bert_ckpt, device=device)


>>> query = """Hey, I'd like to rent a vehicle from Nov 1st to Nov 15th in Paris and I need a 15 passenger van"""
>>> print(pipe(query))

[{'label': 'car_rental', 'score': 0.5490034222602844}]
```

## 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  | 3.2931          | 0.7255   |
| 3.8009        | 2.0   | 636  | 1.8849          | 0.8526   |
| 3.8009        | 3.0   | 954  | 1.1702          | 0.8897   |
| 1.7128        | 4.0   | 1272 | 0.8717          | 0.9145   |
| 0.9206        | 5.0   | 1590 | 0.7872          | 0.9206   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1