distilbert-base-uncased-finetuned-clinc

This model is a fine-tuned version of 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:

>>> 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

Framework versions

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