from transformers import AutoModelForTokenClassification, BertTokenizerFast

tokenizer = BertTokenizerFast.from_pretrained('iioSnail/bert-base-chinese-medical-ner')
model = AutoModelForTokenClassification.from_pretrained("iioSnail/bert-base-chinese-medical-ner")

sentences = ["瘦脸针、水光针和玻尿酸详解！", "半月板钙化的病因有哪些？"]
inputs = tokenizer(sentences, return_tensors="pt", padding=True, add_special_tokens=False)
outputs = model(**inputs)
outputs = outputs.logits.argmax(-1) * inputs['attention_mask']

print(outputs)

tensor([[1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 4, 4],
        [1, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 0, 0]])

[
  [
    {'start': 0, 'end': 3, 'word': '瘦脸针'},
    {'start': 4, 'end': 7, 'word': '水光针'},
    {'start': 8, 'end': 11, 'word': '玻尿酸'}、
  ],
  [
    {'start': 0, 'end': 5, 'word': '半月板钙化'}
  ]
]