{"id":33290,"date":"2025-07-23T16:27:18","date_gmt":"2025-07-23T08:27:18","guid":{"rendered":"https:\/\/www.kdjingpai.com\/?p=33290"},"modified":"2025-07-23T16:31:31","modified_gmt":"2025-07-23T08:31:31","slug":"openmed","status":"publish","type":"post","link":"https:\/\/www.kdjingpai.com\/en\/openmed\/","title":{"rendered":"OpenMed\uff1a\u514d\u8d39\u63d0\u4f9b\u533b\u7597\u9886\u57dfAI\u6a21\u578b\u7684\u5f00\u6e90\u5e73\u53f0"},"content":{"rendered":"<p>OpenMed \u662f\u4e00\u4e2a\u81f4\u529b\u4e8e\u533b\u7597\u548c\u751f\u547d\u79d1\u5b66\u9886\u57df\u7684\u5f00\u6e90AI\u6a21\u578b\u5e73\u53f0\uff0c\u6258\u7ba1\u4e8e Hugging Face\u3002\u5b83\u63d0\u4f9b\u8d85\u8fc7380\u4e2a\u514d\u8d39\u7684\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff08NER\uff09\u6a21\u578b\uff0c\u4e13\u6ce8\u4e8e\u4ece\u4e34\u5e8a\u6587\u672c\u548c\u7814\u7a76\u6587\u732e\u4e2d\u63d0\u53d6\u5173\u952e\u4fe1\u606f\uff0c\u5982\u836f\u7269\u3001\u75be\u75c5\u3001\u57fa\u56e0\u548c\u89e3\u5256\u7ed3\u6784\u7b49\u3002\u8fd9\u4e9b\u6a21\u578b\u5168\u90e8\u57fa\u4e8e Apache 2.0 \u8bb8\u53ef\uff0c\u4efb\u4f55\u4eba\u90fd\u53ef\u81ea\u7531\u4f7f\u7528\u3002OpenMed \u7684\u76ee\u6807\u662f\u6253\u7834\u533b\u7597AI\u7684\u9ad8\u6210\u672c\u58c1\u5792\uff0c\u8ba9\u7814\u7a76\u4eba\u5458\u3001\u533b\u751f\u548c\u5f00\u53d1\u8005\u80fd\u591f\u8f7b\u677e\u83b7\u53d6\u9ad8\u8d28\u91cf\u5de5\u5177\uff0c\u52a0\u901f\u533b\u7597\u7814\u7a76\u548c\u6539\u5584\u60a3\u8005\u670d\u52a1\u3002\u5e73\u53f0\u6a21\u578b\u6027\u80fd\u4f18\u5f02\uff0c\u5728\u591a\u4e2a\u6570\u636e\u96c6\u4e0a\u751a\u81f3\u8d85\u8d8a\u4e86\u6602\u8d35\u7684\u5546\u4e1a\u6a21\u578b\uff0c\u6700\u9ad8\u63d0\u5347\u8fbe36%\u3002OpenMed \u5f3a\u8c03\u5f00\u653e\u6027\u548c\u793e\u533a\u534f\u4f5c\uff0c\u6b22\u8fce\u5168\u7403\u7528\u6237\u8d21\u732e\u548c\u4f7f\u7528\u3002<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-33291\" src=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/07\/2c704e9c24719e4.jpg\" alt=\"\" width=\"900\" height=\"600\" srcset=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/07\/2c704e9c24719e4.jpg 900w, https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/07\/2c704e9c24719e4-18x12.jpg 18w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/p>\n<p>&nbsp;<\/p>\n<h2>\u529f\u80fd\u5217\u8868<\/h2>\n<ul>\n<li>\u63d0\u4f9b380\u591a\u4e2a\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff08NER\uff09\u6a21\u578b\uff0c\u8986\u76d6\u836f\u7269\u3001\u75be\u75c5\u3001\u57fa\u56e0\u3001\u89e3\u5256\u7ed3\u6784\u3001\u80bf\u7624\u7b49\u533b\u7597\u9886\u57df\u3002<\/li>\n<li>\u652f\u6301\u4ece\u4e34\u5e8a\u8bb0\u5f55\u3001\u7814\u7a76\u8bba\u6587\u4e2d\u63d0\u53d6\u7279\u5b9a\u5b9e\u4f53\uff0c\u5982\u5316\u5b66\u7269\u8d28\u3001\u57fa\u56e0\u53d8\u5f02\u3001\u75c5\u7406\u4fe1\u606f\u7b49\u3002<\/li>\n<li>\u6a21\u578b\u5927\u5c0f\u591a\u6837\uff0c\u4ece65M\u5230568M\u53c2\u6570\uff0c\u9002\u914d\u4e0d\u540c\u786c\u4ef6\u73af\u5883\uff08\u59828GB\u81f340GB GPU\uff09\u3002<\/li>\n<li>\u4e0e Hugging Face Transformers \u751f\u6001\u7cfb\u7edf\u65e0\u7f1d\u96c6\u6210\uff0c\u65b9\u4fbf\u52a0\u8f7d\u548c\u90e8\u7f72\u3002<\/li>\n<li>\u63d0\u4f9b\u6a21\u578b\u53d1\u73b0\u5e94\u7528\uff0c\u5141\u8bb8\u7528\u6237\u6309\u9886\u57df\uff08\u5982\u836f\u7406\u5b66\u3001\u80bf\u7624\u5b66\uff09\u6216\u5b9e\u4f53\u7c7b\u578b\u7b5b\u9009\u6a21\u578b\u3002<\/li>\n<li>\u6240\u6709\u6a21\u578b\u5f00\u6e90\uff0c\u57fa\u4e8e Apache 2.0 \u8bb8\u53ef\uff0c\u514d\u8d39\u7528\u4e8e\u7814\u7a76\u548c\u751f\u4ea7\u73af\u5883\u3002<\/li>\n<li>\u652f\u6301\u6279\u91cf\u5904\u7406\u533b\u7597\u6587\u672c\u6570\u636e\uff0c\u4f18\u5316\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\u6548\u7387\u3002<\/li>\n<\/ul>\n<h2>\u4f7f\u7528\u5e2e\u52a9<\/h2>\n<h3>\u5b89\u88c5\u4e0e\u73af\u5883\u51c6\u5907<\/h3>\n<p>OpenMed \u7684\u6a21\u578b\u6258\u7ba1\u5728 Hugging Face \u5e73\u53f0\uff0c\u4f7f\u7528\u524d\u9700\u5b89\u88c5\u5fc5\u8981\u7684\u8f6f\u4ef6\u73af\u5883\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u6b65\u9aa4\uff1a<\/p>\n<ol>\n<li><strong>\u5b89\u88c5 Python \u73af\u5883<\/strong>\uff1a\u786e\u4fdd\u7cfb\u7edf\u5df2\u5b89\u88c5 Python 3.7 \u6216\u66f4\u9ad8\u7248\u672c\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u68c0\u67e5\uff1a\n<pre><code>python --version\r\n<\/code><\/pre>\n<p>\u5982\u679c\u672a\u5b89\u88c5\uff0c\u53ef\u4ece\u00a0Python \u5b98\u7f51\u00a0\u4e0b\u8f7d\u3002<\/li>\n<li><strong>\u5b89\u88c5 Hugging Face Transformers<\/strong>\uff1aOpenMed \u6a21\u578b\u57fa\u4e8e Transformers \u6846\u67b6\u8fd0\u884c\uff0c\u9700\u5b89\u88c5\u8be5\u5e93\u3002\u6253\u5f00\u7ec8\u7aef\uff0c\u8f93\u5165\uff1a\n<pre><code>pip install transformers datasets pandas\r\n<\/code><\/pre>\n<p>\u8fd9\u5c06\u5b89\u88c5 Transformers\u3001Datasets \u548c Pandas\uff0c\u7528\u4e8e\u6a21\u578b\u52a0\u8f7d\u548c\u6570\u636e\u5904\u7406\u3002<\/li>\n<li><strong>\u9a8c\u8bc1 GPU \u652f\u6301\uff08\u53ef\u9009\uff09<\/strong>\uff1a\u5982\u679c\u4f7f\u7528 GPU \u52a0\u901f\uff0c\u9700\u5b89\u88c5 PyTorch \u6216 TensorFlow\uff0c\u5e76\u786e\u4fdd GPU \u9a71\u52a8\u548c CUDA \u5df2\u914d\u7f6e\u3002\u68c0\u67e5 GPU \u53ef\u7528\u6027\uff1a\n<pre><code>python -c \"import torch; print(torch.cuda.is_available())\"\r\n<\/code><\/pre>\n<p>\u8f93\u51fa\u00a0<code>True<\/code>\u00a0\u8868\u793a GPU \u53ef\u7528\u3002<\/li>\n<\/ol>\n<h3>\u57fa\u672c\u4f7f\u7528\u6d41\u7a0b<\/h3>\n<p>OpenMed \u7684\u6838\u5fc3\u529f\u80fd\u662f\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff08NER\uff09\uff0c\u7528\u4e8e\u4ece\u533b\u7597\u6587\u672c\u4e2d\u63d0\u53d6\u7ed3\u6784\u5316\u4fe1\u606f\u3002\u4ee5\u4e0b\u4ee5\u00a0<code>OpenMed\/OpenMed-NER-PharmaDetect-SuperClinical-434M<\/code>\u00a0\u6a21\u578b\u4e3a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u52a0\u8f7d\u548c\u4f7f\u7528\u6a21\u578b\uff1a<\/p>\n<ol>\n<li><strong>\u52a0\u8f7d\u6a21\u578b<\/strong>\uff1a<br \/>\n\u4f7f\u7528 Hugging Face \u7684\u00a0<code>pipeline<\/code>\u00a0\u63a5\u53e3\u52a0\u8f7d\u6a21\u578b\u3002\u4ee5\u4e0b\u4ee3\u7801\u52a0\u8f7d\u4e00\u4e2a\u836f\u7269\u8bc6\u522b\u6a21\u578b\uff1a<\/p>\n<pre><code>from transformers import pipeline\r\nmodel_name = \"OpenMed\/OpenMed-NER-PharmaDetect-SuperClinical-434M\"\r\nner_pipeline = pipeline(\"token-classification\", model=model_name, aggregation_strategy=\"simple\")\r\n<\/code><\/pre>\n<ul>\n<li><code>model_name<\/code>\uff1a\u6307\u5b9a\u6a21\u578b\u540d\u79f0\uff0c\u53ef\u5728\u00a0OpenMed \u6a21\u578b\u9875\u9762\u00a0\u67e5\u627e\u5176\u4ed6\u6a21\u578b\u3002<\/li>\n<li><code>aggregation_strategy=\"simple\"<\/code>\uff1a\u5c06\u5206\u8bcd\u7ed3\u679c\u805a\u5408\u6210\u5b8c\u6574\u5b9e\u4f53\uff0c\u8be6\u89c1 Hugging Face \u6587\u6863\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u5904\u7406\u5355\u6761\u6587\u672c<\/strong>\uff1a<br \/>\n\u8f93\u5165\u533b\u7597\u6587\u672c\uff0c\u6a21\u578b\u5c06\u8bc6\u522b\u5176\u4e2d\u7684\u5b9e\u4f53\u3002\u4f8b\u5982\uff1a<\/p>\n<pre><code>text = \"\u60a3\u8005\u670d\u752810\u6beb\u514b\u963f\u53f8\u5339\u6797\u6cbb\u7597\u9ad8\u8840\u538b\u3002\"\r\nentities = ner_pipeline(text)\r\nfor entity in entities:\r\nprint(f\"\u5b9e\u4f53: {entity['word']} ({entity['entity_group']}), \u7f6e\u4fe1\u5ea6: {entity['score']:.4f}\")\r\n<\/code><\/pre>\n<p>\u8f93\u51fa\u793a\u4f8b\uff1a<\/p>\n<pre><code>\u5b9e\u4f53: \u963f\u53f8\u5339\u6797 (CHEMICAL), \u7f6e\u4fe1\u5ea6: 0.9987\r\n<\/code><\/pre>\n<p>\u8fd9\u8868\u793a\u6a21\u578b\u6210\u529f\u8bc6\u522b\u201c\u963f\u53f8\u5339\u6797\u201d\u4e3a\u5316\u5b66\u7269\u8d28\u5b9e\u4f53\u3002<\/li>\n<li><strong>\u6279\u91cf\u5904\u7406\u6587\u672c<\/strong>\uff1a<br \/>\n\u5bf9\u4e8e\u5927\u91cf\u6587\u672c\uff0cOpenMed \u652f\u6301\u6279\u91cf\u5904\u7406\u4ee5\u63d0\u9ad8\u6548\u7387\u3002\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u5982\u4f55\u5904\u7406\u591a\u6761\u6587\u672c\uff1a<\/p>\n<pre><code>texts = [\r\n\"\u60a3\u8005\u670d\u752810\u6beb\u514b\u963f\u53f8\u5339\u6797\u6cbb\u7597\u9ad8\u8840\u538b\u3002\",\r\n\"\u591a\u67d4\u6bd4\u661f\u6cbb\u7597\u663e\u793a\u80bf\u7624\u663e\u8457\u6d88\u9000\u3002\",\r\n\"\u7814\u7a76\u53d1\u73b0\u7532\u6c28\u8776\u5464\u5bf9\u7c7b\u98ce\u6e7f\u6027\u5173\u8282\u708e\u6709\u6548\u3002\"\r\n]\r\nresults = ner_pipeline(texts, batch_size=8)\r\nfor i, entities in enumerate(results):\r\nprint(f\"\u6587\u672c {i+1} \u5b9e\u4f53\uff1a\")\r\nfor entity in entities:\r\nprint(f\" - {entity['word']} ({entity['entity_group']}): {entity['score']:.4f}\")\r\n<\/code><\/pre>\n<p>\u8f93\u51fa\u793a\u4f8b\uff1a<\/p>\n<pre><code>\u6587\u672c 1 \u5b9e\u4f53\uff1a\r\n- \u963f\u53f8\u5339\u6797 (CHEMICAL): 0.9987\r\n\u6587\u672c 2 \u5b9e\u4f53\uff1a\r\n- \u591a\u67d4\u6bd4\u661f (CHEMICAL): 0.9972\r\n\u6587\u672c 3 \u5b9e\u4f53\uff1a\r\n- \u7532\u6c28\u8776\u5464 (CHEMICAL): 0.9965\r\n<\/code><\/pre>\n<ul>\n<li><code>batch_size=8<\/code>\uff1a\u6839\u636e\u786c\u4ef6\u6027\u80fd\u8c03\u6574\u6279\u6b21\u5927\u5c0f\uff0cGPU \u5185\u5b58\u8f83\u5c0f\u65f6\u53ef\u51cf\u5c0f\u8be5\u503c\u3002<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u4f7f\u7528\u6570\u636e\u96c6\u6279\u91cf\u5904\u7406<\/strong>\uff1a<br \/>\nOpenMed \u652f\u6301\u5904\u7406 Hugging Face \u6570\u636e\u96c6\u3002\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u5982\u4f55\u52a0\u8f7d\u516c\u5f00\u533b\u7597\u6570\u636e\u96c6\u5e76\u5904\u7406\uff1a<\/p>\n<pre><code>from datasets import load_dataset\r\nfrom transformers.pipelines.pt_utils import KeyDataset\r\nimport pandas as pd\r\n# \u52a0\u8f7d\u533b\u7597\u6570\u636e\u96c6\r\nmedical_dataset = load_dataset(\"BI55\/MedText\", split=\"train[:100]\")\r\ndata = pd.DataFrame({\"text\": medical_dataset[\"Completion\"]})\r\ndataset = Dataset.from_pandas(data)\r\n# \u6279\u91cf\u5904\u7406\r\nbatch_size = 16\r\nresults = []\r\nfor out in ner_pipeline(KeyDataset(dataset, \"text\"), batch_size=batch_size):\r\nresults.extend(out)\r\nprint(f\"\u5df2\u5904\u7406 {len(results)} \u6761\u6587\u672c\")\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u4f7f\u7528\u6a21\u578b\u53d1\u73b0\u5e94\u7528<\/strong>\uff1a<br \/>\nOpenMed \u63d0\u4f9b\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u6a21\u578b\u53d1\u73b0\u5e94\u7528\uff0c\u7f51\u5740\u4e3a\u00a0OpenMed NER Model Discovery App\u3002\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u4f7f\u7528\uff1a<\/p>\n<ul>\n<li>\u6253\u5f00\u5e94\u7528\u9875\u9762\uff0c\u8f93\u5165\u9700\u8981\u8bc6\u522b\u7684\u5b9e\u4f53\u7c7b\u578b\uff08\u5982\u201c\u5316\u5b66\u7269\u8d28\u201d\u6216\u201c\u57fa\u56e0\u201d\uff09\u3002<\/li>\n<li>\u4f7f\u7528\u7b5b\u9009\u529f\u80fd\uff0c\u6309\u9886\u57df\uff08\u5982\u836f\u7406\u5b66\u3001\u80bf\u7624\u5b66\uff09\u6216\u6a21\u578b\u67b6\u6784\uff08BERT\u3001RoBERTa\uff09\u67e5\u627e\u9002\u5408\u7684\u6a21\u578b\u3002<\/li>\n<li>\u70b9\u51fb\u6a21\u578b\u94fe\u63a5\uff0c\u76f4\u63a5\u83b7\u53d6\u6a21\u578b\u540d\u79f0\u548c\u4ee3\u7801\u793a\u4f8b\uff0c\u590d\u5236\u5230\u672c\u5730\u8fd0\u884c\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h3>\u7279\u8272\u529f\u80fd\u64cd\u4f5c<\/h3>\n<ul>\n<li><strong>\u591a\u9886\u57df\u652f\u6301<\/strong>\uff1aOpenMed \u6a21\u578b\u8986\u76d6\u836f\u7406\u5b66\u3001\u80bf\u7624\u5b66\u3001\u57fa\u56e0\u7ec4\u5b66\u3001\u75c5\u7406\u5b66\u7b49\u591a\u4e2a\u9886\u57df\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u00a0<code>OpenMed\/OpenMed-NER-OncologyDetect-SuperClinical-434M<\/code>\u00a0\u8bc6\u522b\u764c\u75c7\u76f8\u5173\u5b9e\u4f53\uff1a\n<pre><code>text = \"KRAS\u57fa\u56e0\u7a81\u53d8\u9a71\u52a8\u80bf\u7624\u5f62\u6210\u3002\"\r\nentities = ner_pipeline(text)\r\nprint(entities)\r\n<\/code><\/pre>\n<p>\u8f93\u51fa\u793a\u4f8b\uff1a<\/p>\n<pre><code>[{'word': 'KRAS', 'entity_group': 'GENE', 'score': 0.9991}]\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u9ad8\u6548\u96c6\u6210<\/strong>\uff1a\u6a21\u578b\u4e0e Hugging Face \u751f\u6001\u517c\u5bb9\uff0c\u652f\u6301\u5feb\u901f\u90e8\u7f72\u5230\u751f\u4ea7\u73af\u5883\u3002\u7528\u6237\u53ef\u901a\u8fc7 Hugging Face Inference Endpoints \u90e8\u7f72\u6a21\u578b\uff0c\u65e0\u9700\u672c\u5730\u786c\u4ef6\u3002<\/li>\n<li><strong>\u793e\u533a\u8d21\u732e<\/strong>\uff1a\u7528\u6237\u53ef\u901a\u8fc7 Hugging Face \u7684 \u201cWatch\u201d \u529f\u80fd\u5173\u6ce8 OpenMed\uff0c\u63d0\u4ea4\u529f\u80fd\u8bf7\u6c42\u6216\u8d21\u732e\u65b0\u6a21\u578b\u3002<\/li>\n<\/ul>\n<h3>\u6ce8\u610f\u4e8b\u9879<\/h3>\n<ul>\n<li>\u786e\u4fdd\u7f51\u7edc\u8fde\u63a5\u7a33\u5b9a\uff0c\u4ee5\u4e0b\u8f7d\u6a21\u578b\u6743\u91cd\uff08\u90e8\u5206\u6a21\u578b\u8f83\u5927\uff0c\u5982568M \u53c2\u6570\u6a21\u578b\u9700\u7ea640GB\u5b58\u50a8\uff09\u3002<\/li>\n<li>GPU \u5185\u5b58\u4e0d\u8db3\u65f6\uff0c\u5efa\u8bae\u9009\u62e9\u8f83\u5c0f\u7684\u6a21\u578b\uff08\u598265M \u53c2\u6570\u7684\u00a0<code>OpenMed-NER-PathologyDetect-TinyMed-65M<\/code>\uff09\u3002<\/li>\n<li>\u5b9a\u671f\u68c0\u67e5 OpenMed \u9875\u9762\uff0c\u83b7\u53d6\u6700\u65b0\u6a21\u578b\u66f4\u65b0\u3002<\/li>\n<\/ul>\n<h2>\u5e94\u7528\u573a\u666f<\/h2>\n<ol>\n<li><strong>\u4e34\u5e8a\u8bb0\u5f55\u5206\u6790<\/strong><br \/>\n\u533b\u9662\u53ef\u4f7f\u7528 OpenMed \u6a21\u578b\u4ece\u60a3\u8005\u8bb0\u5f55\u4e2d\u63d0\u53d6\u836f\u7269\u3001\u75be\u75c5\u7b49\u4fe1\u606f\u3002\u4f8b\u5982\uff0c\u5feb\u901f\u8bc6\u522b\u201c\u60a3\u8005\u670d\u7528\u963f\u53f8\u5339\u6797\u201d\u4e2d\u7684\u836f\u7269\u540d\u79f0\uff0c\u8f85\u52a9\u533b\u751f\u6574\u7406\u7535\u5b50\u75c5\u5386\u3002<\/li>\n<li><strong>\u533b\u5b66\u7814\u7a76<\/strong><br \/>\n\u7814\u7a76\u4eba\u5458\u53ef\u5229\u7528\u6a21\u578b\u5206\u6790\u6587\u732e\uff0c\u63d0\u53d6\u57fa\u56e0\u3001\u86cb\u767d\u8d28\u7b49\u4fe1\u606f\uff0c\u6784\u5efa\u77e5\u8bc6\u56fe\u8c31\u3002\u4f8b\u5982\uff0c\u4ece\u8bba\u6587\u4e2d\u63d0\u53d6\u201cBRCA2\u57fa\u56e0\u201d\u4e0e\u764c\u75c7\u7684\u5173\u8054\u3002<\/li>\n<li><strong>\u836f\u7269\u7814\u53d1<\/strong><br \/>\n\u836f\u4f01\u53ef\u4f7f\u7528\u6a21\u578b\u8bc6\u522b\u5316\u5b66\u7269\u8d28\u548c\u836f\u7269\u4ea4\u4e92\u4fe1\u606f\uff0c\u52a0\u901f\u836f\u7269\u53d1\u73b0\u3002\u4f8b\u5982\uff0c\u5206\u6790\u201c\u591a\u67d4\u6bd4\u661f\u201d\u5728\u80bf\u7624\u6cbb\u7597\u4e2d\u7684\u4f5c\u7528\u3002<\/li>\n<li><strong>\u60a3\u8005\u9690\u79c1\u4fdd\u62a4<\/strong><br \/>\n\u901a\u8fc7 NER \u6a21\u578b\u5b9e\u73b0\u53bb\u6807\u8bc6\u5316\uff0c\u81ea\u52a8\u79fb\u9664\u60a3\u8005\u8bb0\u5f55\u4e2d\u7684\u4e2a\u4eba\u4fe1\u606f\uff08\u5982\u59d3\u540d\u3001\u5730\u5740\uff09\uff0c\u7b26\u5408 HIPAA \u7b49\u9690\u79c1\u6cd5\u89c4\u8981\u6c42\u3002<\/li>\n<\/ol>\n<h2>QA<\/h2>\n<ol>\n<li><strong>OpenMed \u6a21\u578b\u662f\u5426\u514d\u8d39\uff1f<\/strong><br \/>\n\u662f\u7684\uff0c\u6240\u6709\u6a21\u578b\u57fa\u4e8e Apache 2.0 \u8bb8\u53ef\uff0c\u5b8c\u5168\u514d\u8d39\uff0c\u9002\u7528\u4e8e\u7814\u7a76\u548c\u5546\u4e1a\u7528\u9014\u3002<\/li>\n<li><strong>\u5982\u4f55\u9009\u62e9\u9002\u5408\u7684\u6a21\u578b\uff1f<\/strong><br \/>\n\u4f7f\u7528 OpenMed NER Model Discovery App\uff0c\u6309\u9886\u57df\u6216\u5b9e\u4f53\u7c7b\u578b\u7b5b\u9009\u6a21\u578b\u3002\u4e5f\u53ef\u6839\u636e\u786c\u4ef6\u6761\u4ef6\u9009\u62e9\u53c2\u6570\u89c4\u6a21\uff08\u598265M \u6216434M\uff09\u3002<\/li>\n<li><strong>\u9700\u8981\u54ea\u4e9b\u786c\u4ef6\u8fd0\u884c\u6a21\u578b\uff1f<\/strong><br \/>\n\u6a21\u578b\u652f\u63018GB\u81f340GB GPU\uff0cCPU \u4e5f\u53ef\u8fd0\u884c\u8f83\u5c0f\u6a21\u578b\uff0c\u4f46\u901f\u5ea6\u8f83\u6162\u3002\u5efa\u8bae\u81f3\u5c1116GB\u5185\u5b58\u3002<\/li>\n<li><strong>\u5982\u4f55\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff1f<\/strong><br \/>\n\u4f7f\u7528\u6279\u91cf\u5904\u7406\u4ee3\u7801\uff0c\u8c03\u6574\u00a0<code>batch_size<\/code>\u00a0\u53c2\u6570\u4ee5\u9002\u914d\u786c\u4ef6\u3002\u53c2\u8003\u201c\u4f7f\u7528\u5e2e\u52a9\u201d\u4e2d\u7684\u6279\u91cf\u5904\u7406\u793a\u4f8b\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>OpenMed \u662f\u4e00\u4e2a\u81f4\u529b\u4e8e\u533b\u7597\u548c\u751f\u547d\u79d1\u5b66\u9886\u57df\u7684\u5f00\u6e90AI\u6a21\u578b\u5e73\u53f0\uff0c\u6258\u7ba1\u4e8e Hugging Face\u3002\u5b83\u63d0\u4f9b\u8d85\u8fc7380\u4e2a\u514d\u8d39\u7684\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff08NER\uff09\u6a21\u578b\uff0c\u4e13\u6ce8\u4e8e\u4ece\u4e34\u5e8a\u6587\u672c\u548c\u7814\u7a76\u6587\u732e\u4e2d\u63d0\u53d6\u5173\u952e\u4fe1\u606f\uff0c\u5982\u836f\u7269\u3001\u75be\u75c5\u3001\u57fa\u56e0\u548c\u89e3\u5256\u7ed3\u6784\u7b49\u3002\u8fd9\u4e9b\u6a21\u578b\u5168\u90e8\u57fa&#8230;<\/p>\n","protected":false},"author":1,"featured_media":31260,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20,403,392],"tags":[230],"class_list":["post-33290","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tool","category-dedicated-model","category-models","tag-aikaiyuanxiangmu"],"_links":{"self":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts\/33290","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/comments?post=33290"}],"version-history":[{"count":0,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/posts\/33290\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/media\/31260"}],"wp:attachment":[{"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/media?parent=33290"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/categories?post=33290"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kdjingpai.com\/en\/wp-json\/wp\/v2\/tags?post=33290"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}