patient-doctor-text-classifier-eng-old

distilbert-base-uncased-finetuned-text-classification

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.

Fine-tuned DistilBERT-base-uncased for Patient-Doctor Classification

Model Description

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.

The model is a fine-tuned version of DistilBERT.

It was fine-tuned on the prepared dataset (https://huggingface.co/datasets/LukeGPT88/text-classification-dataset).

It achieves the following results on the evaluation set:

• Loss: 0.0501
• Accuracy: 0.9861

Model description

More information needed

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.37.0
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.1

How to Use

from transformers import pipeline
classifier = pipeline("text-classification", model="LukeGPT88/patient-doctor-text-classifier")
classifier("I see you’ve set aside this special time to humiliate yourself in public.")

Output:
[{'label': 'NEUTRAL', 'score': 0.9890775680541992}]

Contact

Please reach out to [email protected] if you have any questions or feedback.

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