>>> 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