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

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