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 [email protected] if you have any questions or feedback.