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

• Transformers 4.38.1

• Pytorch 2.1.2

• Datasets 2.1.0

• Tokenizers 0.15.2

• How to Use

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

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