Crosslingual Generalization through Multitask Finetuning
Paper
•
2211.01786
•
Published
•
2
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
Fine-tuned BLOOMZ medical
The model is based on BLOOMZ-3B a large language model trained by the BigScience team.
The full paper is available here.
The model is a created using peft, transformers, and bitsandbytes.
The goal of the model
Using a transcript of a patient visit with his/her general health care provider (GP) recommend where that patient should be referred to and extract the main keywords from the transcript.
For example, if a patient has allergy-related concerns, then GP needs to refer that patient to the allergy/immunology department or an immunology doctor available.
Extracted keywords are there to help the doctor in making the final decision of what to recommend to the patient.
dataset for fine-tuning: - tchebonenko/MedicalTranscriptions
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support