metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
datasets:
- clinc/clinc_oos
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