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language: "en" |
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tags: |
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- distilbert-base-uncased |
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- text-classification |
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- patient |
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- doctor |
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widget: |
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- text: "I've got flu" |
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- text: "I prescribe you some drugs and you need to stay at home for a couple of days" |
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- text: "Let's move to the theatre this evening!" |
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--- |
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# distilbert-base-uncased-finetuned-text-classification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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# Fine-tuned DistilBERT-base-uncased for Patient-Doctor Classification |
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# Model Description |
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DistilBERT is a transformer model that performs text classification. I fine-tuned the model on with the purpose of classifying patient, doctor or neutral content, specifically when text is related to the supposed context. The model predicts 3 classes, which are Patient, Doctor or Neutral. |
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The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert). |
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It was fine-tuned on the prepared dataset (https://huggingface.co/datasets/LukeGPT88/text-classification-dataset). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0501 |
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- Accuracy: 0.9861 |
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## Model description |
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More information needed |
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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: 32 |
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- eval_batch_size: 32 |
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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: 2 |
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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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| 0.115 | 1.0 | 774 | 0.0486 | 0.9864 | |
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| 0.0301 | 2.0 | 1548 | 0.0501 | 0.9861 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
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# How to Use |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="LukeGPT88/patient-doctor-text-classifier") |
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classifier("I see you’ve set aside this special time to humiliate yourself in public.") |
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``` |
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```python |
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Output: |
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[{'label': 'NEUTRAL', 'score': 0.9890775680541992}] |
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``` |
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# Contact |
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Please reach out to [[email protected]]([email protected]) if you have any questions or feedback. |
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