How to use ClinicalMetaScience/NegativeResultDetector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ClinicalMetaScience/NegativeResultDetector")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ClinicalMetaScience/NegativeResultDetector") model = AutoModelForSequenceClassification.from_pretrained("ClinicalMetaScience/NegativeResultDetector", device_map="auto")
https://huggingface.co/ClinicalMetaScience/NegativeResultDetector/resolve/main/tokenizer.json
hf download hf://ClinicalMetaScience/NegativeResultDetector/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/ClinicalMetaScience/NegativeResultDetector/resolve/main/tokenizer.json
