397 lines
9.4 KiB
JSON
397 lines
9.4 KiB
JSON
{
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"meta": {
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"build_date": "2026-01-01T00:00:00Z",
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"wiki_title": "测试知识库",
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"total_nodes": 8,
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"total_edges": 13,
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"initial_view": [
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"Attention",
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"Transformer",
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"GPT",
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"arch",
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"Decoder",
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"Encoder",
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"finetune",
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"paper"
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],
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"degraded": false,
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"insights_degraded": false
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},
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"nodes": [
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{
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"id": "Attention",
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"label": "Attention",
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"type": "entity",
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"community": "Attention",
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"content": "# Attention\n\n注意力机制是 Transformer 的核心。参见 [[Transformer]]。"
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},
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{
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"id": "Decoder",
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"label": "Decoder",
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"type": "entity",
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"community": "arch",
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"content": "# Decoder\n\n解码器组件。与 [[Transformer]] <!-- confidence: INFERRED --> 的解码端对应。"
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},
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{
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"id": "Encoder",
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"label": "Encoder",
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"type": "entity",
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"community": "arch",
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"content": "# Encoder\n\n编码器组件。属于 [[Transformer]] 架构的一部分。"
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},
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{
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"id": "GPT",
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"label": "GPT",
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"type": "entity",
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"community": "finetune",
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"content": "# GPT\n\nGPT 系列基于 [[Transformer]] <!-- confidence: AMBIGUOUS --> 的解码器架构。"
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},
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{
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"id": "Transformer",
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"label": "Transformer",
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"type": "entity",
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"community": "arch",
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"content": "# Transformer\n\nTransformer 是一种基于自注意力机制的序列到序列模型架构。\n\n## 核心组件\n\n- [[Attention]] — 自注意力机制\n- [[Encoder]] — 编码器\n- [[Decoder]] — 解码器"
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},
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{
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"id": "arch",
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"label": "深度学习架构",
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"type": "topic",
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"community": "arch",
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"content": "# 深度学习架构\n\n本主题涵盖深度学习的核心架构组件。\n\n- [[Transformer]]\n- [[Attention]]\n- [[Encoder]]\n- [[Decoder]]"
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},
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{
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"id": "finetune",
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"label": "微调技术",
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"type": "topic",
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"community": "finetune",
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"content": "# 微调技术\n\n本主题涵盖模型微调相关技术。\n\n- [[GPT]]"
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},
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{
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"id": "paper",
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"label": "Attention Is All You Need",
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"type": "source",
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"community": "Attention",
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"content": "# Attention Is All You Need\n\n原始论文提出了自注意力机制。\n\n- [[Attention]] <!-- confidence: EXTRACTED -->"
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}
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],
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"edges": [
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{
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"id": "e1",
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"from": "Attention",
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"to": "Transformer",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.733,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0.2,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e2",
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"from": "Decoder",
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"to": "Transformer",
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"type": "INFERRED",
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"confidence": "INFERRED",
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"relation_type": "依赖",
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"weight": 0.733,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0.2,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e3",
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"from": "Encoder",
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"to": "Transformer",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.733,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0.2,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e4",
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"from": "GPT",
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"to": "Transformer",
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"type": "AMBIGUOUS",
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"confidence": "AMBIGUOUS",
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"relation_type": "依赖",
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"weight": 0.333,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0,
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"source_overlap": 0,
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"type_affinity": 1
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}
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},
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{
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"id": "e5",
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"from": "Transformer",
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"to": "Attention",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.733,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0.2,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e6",
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"from": "Transformer",
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"to": "Decoder",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.733,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0.2,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e7",
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"from": "Transformer",
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"to": "Encoder",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.733,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0.2,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e8",
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"from": "arch",
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"to": "Attention",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.667,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e9",
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"from": "arch",
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"to": "Decoder",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.667,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e10",
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"from": "arch",
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"to": "Encoder",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.667,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e11",
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"from": "arch",
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"to": "Transformer",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.667,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e12",
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"from": "finetune",
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"to": "GPT",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.667,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0,
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"source_overlap": 1,
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"type_affinity": 1
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}
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},
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{
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"id": "e13",
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"from": "paper",
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"to": "Attention",
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"type": "EXTRACTED",
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"confidence": "EXTRACTED",
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"relation_type": "依赖",
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"weight": 0.533,
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"source_signal_available": true,
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"signals": {
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"co_citation": 0,
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"source_overlap": 1,
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"type_affinity": 0.6
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}
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}
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],
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"insights": {
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"surprising_connections": [],
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"isolated_nodes": [
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{
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"id": "finetune",
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"label": "微调技术",
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"degree": 1,
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"community": "finetune"
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},
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{
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"id": "paper",
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"label": "Attention Is All You Need",
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"degree": 1,
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"community": "Attention"
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}
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],
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"bridge_nodes": [
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{
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"id": "Transformer",
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"label": "Transformer",
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"community": "arch",
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"connected_communities": [
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"Attention",
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"finetune"
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],
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"community_count": 2
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}
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],
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"sparse_communities": [],
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"meta": {
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"degraded": false,
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"node_count": 8,
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"edge_count": 13,
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"max_insight_nodes": 250,
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"max_insight_edges": 1000
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}
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},
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"learning": {
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"version": 1,
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"entry": {
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"recommended_start_node_id": "Transformer",
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"recommended_start_reason": "community_hub",
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"default_mode": "global"
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},
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"views": {
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"path": {
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"enabled": true,
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"start_node_id": "Transformer",
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"node_ids": [
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"Transformer",
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"Decoder",
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"Encoder",
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"arch"
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],
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"degraded": false
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},
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"community": {
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"enabled": true,
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"community_id": "arch",
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"label": "深度学习架构",
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"node_ids": [
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"Decoder",
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"Encoder",
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"Transformer",
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"arch"
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],
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"is_weak": false,
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"degraded": false
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},
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"global": {
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"enabled": true,
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"node_ids": [
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"Transformer",
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"arch",
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"Attention",
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"Decoder",
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"Encoder",
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"GPT",
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"finetune",
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"paper"
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],
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"degraded": false
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}
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},
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"communities": [
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{
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"id": "arch",
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"label": "深度学习架构",
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"node_count": 4,
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"source_count": 0,
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"internal_edge_weight": 4.933,
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"is_primary": true,
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"is_weak": false,
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"recommended_start_node_id": "Transformer"
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},
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{
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"id": "finetune",
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"label": "微调技术",
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"node_count": 2,
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"source_count": 0,
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"internal_edge_weight": 0.667,
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"is_primary": false,
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"is_weak": true,
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"recommended_start_node_id": "GPT"
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},
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{
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"id": "Attention",
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"label": "Attention",
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"node_count": 2,
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"internal_edge_weight": 0.533,
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"is_primary": false,
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"is_weak": true,
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"recommended_start_node_id": "Attention"
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}
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],
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"degraded": {
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"path_to_community": false,
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"community_to_global": false
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}
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}
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}
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