(topic, term=None, ret=100)
| 84 | return st[:-1] |
| 85 | |
| 86 | def initial(topic, term=None, ret=100): |
| 87 | if term is None: |
| 88 | term = draw_term(topic) |
| 89 | docs = semantic_query(term.replace(' ', '+')[:50], ret) |
| 90 | sims = dense(topic, docs) |
| 91 | return docs,sims |
| 92 | |
| 93 | def split_question(criteria): |
| 94 | prp = """Please decompose the complex query into a series of simple questions. You can refer to these examples: [QUERY] Randomized controlled and controlled clinical trials which evaluated individual education for adults with type 2 diabetes. The intervention was individual face-to-face patient education while control individuals received usual care, routine treatment or group education. Only studies that assessed outcome measures at least six months from baseline were included. [QUESTIONS] ###1 Is a randomised controlled trial (RCT) or a controlled clinical trial conducted in the article? ###2 Does the article compare individual face-to-face patient education versus usual care, routine treatment or group education? ###3 Are the patients in the article adults with type 2 diabetes? ###4 Does the article outcome measure at least six months from baseline? [QUERY] Studies were included if they conduct experiments of comparing graph neural networks with other deep learning models. Performance on molecular datasets is reported. [QUESTIONS] ###1 Does the article conduct experiments of graph neural networks? ###2 Does the article compare different deep learning models? ###3 Does the article report performance on molecular datasets? |
no test coverage detected