sociocom
/

MedTXTNER

@@ -1,199 +1,69 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+  - japanese
+  - ner
+  - medical
 ---
+# Model Card for `Tomohiro/MedTXTNER`
+**本モデルは、日本語医療テキストの NER（固有表現抽出）タスク向けに `cl-tohoku/bert-base-japanese-v3` をファインチューニングしたモデルです。**
+## モデル詳細
+### 説明
+- ベースに `cl-tohoku/bert-base-japanese-v3`を使用
+- 奈良先端大で作成された日本語医療テキストのアノテーション付きデータ（症例報告、読影レポート、看護記録）でファインチューニングを実施
+| 項目                    | 詳細                                   |
+|-------------------------|----------------------------------------|
+| **Developed by**        | NAIST ソーシャルコンピューティング研究室       |
+| **Model type**          | Token classification       　　         |
+| **Language(s)**         | Japanese                               |
+| **Finetuned from**      | cl-tohoku/bert-base-japanese-v3        |
+### モデルソース
+- **Hub リポジトリ**: https://huggingface.co/Tomohiro/MedTXTNER
+## 利用方法
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+model_dir = "Tomohiro/MedTXTNER"
+model     = AutoModelForTokenClassification.from_pretrained(model_dir)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir, use_fast=True)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+def predict_text(text: str):
+    enc = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding="max_length",
+        max_length=512,
+        is_split_into_words=False
+    ).to(device)
+    with torch.no_grad():
+        outputs = model(**enc)
+        logits  = outputs.logits
+    pred_ids = torch.argmax(logits, dim=-1)[0].cpu().tolist()
+    tokens = tokenizer.convert_ids_to_tokens(enc["input_ids"][0])
+    id2label = model.config.id2label
+    result = []
+    for tok, pid in zip(tokens, pred_ids):
+        if tok in tokenizer.all_special_tokens:
+            continue
+        result.append((tok, id2label[pid]))
+    return result
+sample = "症例】５３歳女性。発熱と嘔気を認め、プレドニゾロンを中断しました。"
+for tok, lab in predict_text(sample):
+    print(f"{tok}\t{lab}")