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License: Apache License 2.0
Examples about using MGeo finetune models
License: Apache License 2.0
您好,在给出的示例中是输入一条query然后返回3个待选地址的相似度,但是实际应用中待选地址是一个很大的地址库,比如有十几万待选地址,这样的话推理起来往往造成内存溢出,想问这种情况需要怎么做,期待您的回复,谢谢
这是训练日志的可视化
train_log.csv
eval_log.csv
您好,我在运行模型比较地址相似度代码的时候出现了cuda问题,能否帮我检查一下这个问题应该怎样解决?
代码:
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
task = Tasks.sentence_similarity
model = 'damo/mgeo_geographic_entity_alignment_chinese_base'
inputs = ('紫萱路363号人力社保局', '紫萱路363号市人社局')
pipeline_ins = pipeline(task=task, model=model, model_revision='v1.2.0')
print(pipeline_ins(input=inputs)) (报错位置)
运行环境:
docker
pyhton = 3.7
(其余配置与文章中所提到的配置完全相同)
模型可以顺利加载,但是在运行的时候出现了问题。
报错信息:
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
你好,非常感谢你们的优秀工作,填补了此领域公开benchmark以及预训练权重的稀缺。
但我有一点疑惑:在尝试做POI retrieval的任务时,我没有在仓库中看到用于recall任务的MGeo预训练权重(注明年后发布的recall任务数据集似乎是已经发布了)。其他五个任务的模型都提供了下载。我想请问是否是我的疏漏?
如果你们的确没有提供recall任务相关,请问你们是否有计划发布这一预训练权重呢?如果不能,我怎样做才能复现论文中的对应结果?
再次感谢!
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