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LukeGPT88/patient-doctor-text-classifier-eng-0501

sourceHugging Faceupdated 2y agoView on Hugging Face
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Model Card

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distilbert-base-uncased-finetuned-patient-doctor-text-classifier-eng

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.0516
  • —Accuracy: 0.9879

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
  • —trainbatchsize: 16
  • —evalbatchsize: 16
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 2

Training results

Training LossEpochStepValidation LossAccuracy
0.08971.015470.05730.9865
0.03012.030940.05160.9879

Framework versions

  • —Transformers 4.38.1
  • —Pytorch 2.1.2
  • —Datasets 2.1.0
  • —Tokenizers 0.15.2
  • —# How to Use
python
from transformers import pipeline
classifier = pipeline("text-classification", model="LukeGPT88/patient-doctor-text-classifier-eng")
classifier("I see you’ve set aside this special time to humiliate yourself in public.")
python
Output:
[{'label': 'NEUTRAL', 'score': 0.9890775680541992}]

Contact

Please reach out to luca.flammia@gmail.com if you have any questions or feedback.