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metadata
language: en
tags:
  - distilbert-base-uncased
  - text-classification
  - patient
  - doctor
widget:
  - text: I've got flu
  - text: >-
      I prescribe you some drugs and you need to stay at home for a couple of
      days
  - text: Let's move to the theatre this evening!

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

Training Loss Epoch Step Validation Loss Accuracy
0.115 1.0 774 0.0486 0.9864
0.0301 2.0 1548 0.0501 0.9861

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 luca.flammia@gmail.com if you have any questions or feedback.