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-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
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|
| 0.0897 | 1.0 | 1547 | 0.0573 | 0.9865 |
| 0.0301 | 2.0 | 3094 | 0.0516 | 0.9879 |
Framework versions
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.")
Output:
[{'label': 'NEUTRAL', 'score': 0.9890775680541992}]
Contact
Please reach out to [email protected] if you have any questions or feedback.