WpyQwq
643e22ecb9
- 引入 MemoryRouterXL 与 v5/v6 流式多线程训练/编码管线 - 修复 prepare_memory_router_dataset 候选池重建缺陷（mega 家族 3568x 加速，输出逐字节相同） - 修复 v5 被破坏的拒答与多跳标签（train 未知样本 319 -> 16319，multi_hop 平均正例 1.00 -> 2.00） - 同存储预算下 V2-128 v6 逐轴 22/22 通过：Top-1 41.12% -> 94.62%，未知拒答 0.00% -> 100.00% - 记录三条被实测推翻的显然优化（logits_to_keep=1 反而慢 55%、XL 容量未带来收益） - 记忆手术跨架构可移植性 14/14，读写关闭时与原生模型逐位相同
124 lines
2.8 KiB
JSON
124 lines
2.8 KiB
JSON
{
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"attributes": 24,
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"chance_pct": 4.17,
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"train_queries": 1200,
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"eval_queries": 300,
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"distinct_train_queries": null,
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"train_accuracy_pct": 83.42,
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"eval_accuracy_pct": 83.33,
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"eval_accuracy_answerable_pct": 100.0,
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"note": "eval queries are different paraphrases of the same attributes, so this is generalisation to unseen wording",
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"per_attribute": {
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"主管姓名": {
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"eval_queries": 12,
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"correct_pct": 100.0
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},
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"入职年份": {
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"eval_queries": 11,
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"correct_pct": 100.0
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},
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"出生城市": {
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"eval_queries": 9,
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"correct_pct": 100.0
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},
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"办公城市": {
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"eval_queries": 9,
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"correct_pct": 100.0
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},
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"午餐偏好": {
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"eval_queries": 12,
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"correct_pct": 100.0
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},
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"咖啡口味": {
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"eval_queries": 19,
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"correct_pct": 100.0
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},
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"团队名称": {
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"eval_queries": 10,
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"correct_pct": 100.0
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},
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"客户名称": {
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"eval_queries": 12,
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"correct_pct": 100.0
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},
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"宿舍楼号": {
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"eval_queries": 14,
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"correct_pct": 100.0
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},
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"工位楼层": {
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"eval_queries": 6,
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"correct_pct": 100.0
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},
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"常住城市": {
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"eval_queries": 13,
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"correct_pct": 100.0
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},
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"常用编辑器": {
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"eval_queries": 6,
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"correct_pct": 100.0
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},
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"手机尾号": {
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"eval_queries": 11,
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"correct_pct": 100.0
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},
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"档案标识": {
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"eval_queries": 16,
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"correct_pct": 100.0
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},
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"紧急联系人姓氏": {
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"eval_queries": 7,
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"correct_pct": 100.0
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},
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"设备型号": {
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"eval_queries": 7,
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"correct_pct": 100.0
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},
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"课程名称": {
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"eval_queries": 12,
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"correct_pct": 100.0
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},
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"起床时间": {
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"eval_queries": 12,
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"correct_pct": 100.0
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},
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"运动习惯": {
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"eval_queries": 6,
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"correct_pct": 100.0
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},
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"通勤方式": {
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"eval_queries": 8,
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"correct_pct": 100.0
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},
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"邮箱域名": {
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"eval_queries": 7,
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"correct_pct": 100.0
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},
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"阅读工具": {
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"eval_queries": 7,
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"correct_pct": 100.0
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},
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"项目代号": {
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"eval_queries": 12,
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"correct_pct": 100.0
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},
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"默认语言": {
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"eval_queries": 12,
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"correct_pct": 100.0
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}
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},
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"coverage_rule": {
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"rule": "predict the asked-about attribute; refuse when the candidate set lacks it",
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"answerable_inside_pct": 100.0,
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"unknown_inside_pct": 0.0,
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"answerable_cases": 250,
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"unknown_cases": 50,
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"unknown_refusal_pct": 100.0,
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"known_false_refusal_pct": 0.0,
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"counts": {
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"tp": 50,
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"fn": 0,
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"fp": 0,
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"tn": 250
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}
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}
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} |