Instructions to use josephgatto/paint_doctor_speaker_identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use josephgatto/paint_doctor_speaker_identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="josephgatto/paint_doctor_speaker_identification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("josephgatto/paint_doctor_speaker_identification") model = AutoModelForSequenceClassification.from_pretrained("josephgatto/paint_doctor_speaker_identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This model is a bert for sequence classification model fine-tuned on the MedDialogue dataset. Basically, the task is just to predict if a given sentence in the corpus was spoken by the patient or doctor.
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