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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: distilbert-base-uncased-finetuned-clinc |
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results: [] |
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datasets: |
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- clinc/clinc_oos |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-base-uncased-finetuned-clinc |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on clinc/clinc_oos dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7872 |
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- Accuracy: 0.9206 |
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## Model description |
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More information needed |
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## How to use |
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You can use this model directly with a pipeline for text classification: |
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```python |
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>>> from transformers import pipeline |
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>>> import torch |
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>>> bert_ckpt = "seddiktrk/distilbert-base-uncased-finetuned-clinc" |
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>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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>>> pipe = pipeline("text-classification", model=bert_ckpt, device=device) |
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>>> query = """Hey, I'd like to rent a vehicle from Nov 1st to Nov 15th in Paris and I need a 15 passenger van""" |
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>>> print(pipe(query)) |
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[{'label': 'car_rental', 'score': 0.5490034222602844}] |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 48 |
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- eval_batch_size: 48 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 318 | 3.2931 | 0.7255 | |
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| 3.8009 | 2.0 | 636 | 1.8849 | 0.8526 | |
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| 3.8009 | 3.0 | 954 | 1.1702 | 0.8897 | |
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| 1.7128 | 4.0 | 1272 | 0.8717 | 0.9145 | |
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| 0.9206 | 5.0 | 1590 | 0.7872 | 0.9206 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
