from transformers import BertForTokenClassification, AutoTokenizer

import torch
text = "サンプルテキスト"
model_name = "yseop/SMM4H2024_Task2a_ja"
with torch.inference_mode():
    model = BertForTokenClassification.from_pretrained(model_name).eval()
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    idx2tag = model.config.id2label
    vecs = tokenizer(text, 
                     padding=True, 
                     truncation=True, 
                     return_tensors="pt")
    ner_logits = model(input_ids=vecs["input_ids"], 
                       attention_mask=vecs["attention_mask"])
    idx = torch.argmax(ner_logits.logits, dim=2).detach().cpu().numpy().tolist()[0]
    token = [tokenizer.convert_ids_to_tokens(v) for v in vecs["input_ids"]][0][1:-1]
    pred_tag = [idx2tag[x] for x in idx][1:-1]

Results

NE	tp	fp	fn	precision	recall	f1
DISORDER	588	409	330	0.5898	0.6405	0.6141
DRUG	307	143	169	0.6822	0.645	0.6631
FUNCTION	69	160	170	0.3013	0.2887	0.2949
all	964	712	669	0.5752	0.5903	0.5827