OpenMed_OpenMed-NER-PharmaDetect-ElectraMed-109M

pip install transformers torch

import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification

device = torch.device("npu:0" if torch.npu.is_available() else "cpu")
model_name = "OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-109M"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForTokenClassification.from_pretrained(model_name, trust_remote_code=True)
model = model.to(device).eval()

texts = ["The patient was diagnosed with cancer."]
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=128)
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)
print(f"输出 logits 形状: {outputs.logits.shape}")