我已经使用以下命令将从 LUIS 应用程序下载的 json 迁移到 RASA 格式:python -m rasa_nlu.train -c config_spacy.json
我的配置文件是这样的:
{
"path" : "./models",
"data" : "./data/examples/rasa/BookACab.json",
"pipeline" : ["nlp_spacy", "tokenizer_spacy", "intent_featurizer_spacy",
"ner_crf", "ner_synonyms", "intent_classifier_sklearn",
"ner_duckling"]
}
模型是用 RASA 格式的 json 生成的，如下所示。但是，当我使用
查询此模型时http://localhost:5000/parse?q=book a ride later
返回与我输入的文本及其所有相关实体相关的正确高分意图。但是当我尝试另一个文本时:
http://localhost:5000/parse?q=I want to go ride today 5pm
返回的意图是正确的，但它的 Entities 对象是空的。正如您在下面的 json 中看到的，与工作示例类似，该话语也将实体映射到它。
请帮助我了解这是否是每个使用 RASA 的人都会遇到的问题，还是我做错了什么？谢谢!
{
"rasa_nlu_data": {
"common_examples": [
{
"entities": [
{
"entity": "RideTime",
"value": "later",
"start": 0,
"end": 5
}
],
"intent": "None",
"text": "later"
},
{
"entities": [],
"intent": "ServiceRequestEnquiry",
"text": "wake up"
},
{
"entities": [],
"intent": "ConfirmationNo",
"text": "no not now"
},
{
"entities": [],
"intent": "ConfirmationNo",
"text": "not sure"
},
{
"entities": [],
"intent": "ConfirmationNo",
"text": "no bot"
},
{
"entities": [],
"intent": "ConfirmationNo",
"text": "no goride bot"
},
{
"entities": [
{
"entity": "RideTime",
"value": "later",
"start": 12,
"end": 17
}
],
"intent": "BookCab",
"text": "book a ride later"
},
{
"entities": [
{
"entity": "RideTime",
"value": "now",
"start": 21,
"end": 24
}
],
"intent": "BookCab",
"text": "i want go for a ride now"
},
{
"entities": [
{
"entity": "RideTime",
"value": "today",
"start": 12,
"end": 17
}
],
"intent": "BookCab",
"text": "book a ride today"
},
{
"entities": [
{
"entity": "RideTime",
"value": "today 5pm",
"start": 18,
"end": 27
}
],
"intent": "BookCab",
"text": "I want to go ride today 5pm"
},
{
"entities": [
{
"entity": "RideTime",
"value": "today",
"start": 12,
"end": 17
}
],
"intent": "BookCab",
"text": "book a ride today 5pm"
},
{
"entities": [
{
"entity": "RideTime",
"value": "later",
"start": 13,
"end": 18
}
],
"intent": "BookCab",
"text": "book shuttle later"
},
{
"entities": [
{
"entity": "RideTime",
"value": "now",
"start": 15,
"end": 18
}
],
"intent": "None",
"text": "i want to book now"
},
{
"entities": [
{
"entity": "RideTime",
"value": "booknow",
"start": 10,
"end": 17
}
],
"intent": "None",
"text": "i want to booknow"
},
{
"entities": [
{
"entity": "RideTime",
"value": "book later",
"start": 10,
"end": 20
}
],
"intent": "None",
"text": "i want to book later"
}
],
"regex_features": []
}
}
请您参考如下方法:
如果您能包含 pipeline 会很有帮助您正在与 Rasa 一起使用。您可以在您的 configuration 中找到它文件。假设您没有更改 config_spacy.json 中的默认管道，那么您正在使用 ner_crf用于实体识别。
很可能由于库的差异，Rasa 只需要比 LUIS 需要更多的训练数据。从质量上讲，mitie 管道通常需要较少的训练数据，但代价是需要更多的训练时间。
所以您的问题的基本答案是:如果您想使用 ner_crf，那么您需要增加为实体识别提供的训练数据量。
也就是说:RideTime 是您唯一的实体吗？如果是这样，您应该考虑添加 ner_duckling到您的管道，它可以识别日期。这比您尝试自己训练日期的效果更好。
因此使用上面的训练数据和管道:
["nlp_spacy", "tokenizer_spacy", "intent_featurizer_spacy", "ner_crf", "ner_synonyms", "intent_classifier_sklearn", "ner_duckling"]
结果如下:
{
"entities": [
{
"additional_info": {
"grain": "hour",
"others": [
{
"grain": "hour",
"value": "2017-07-26T17:00:00.000Z"
}
],
"value": "2017-07-26T17:00:00.000Z"
},
"end": 27,
"entity": "time",
"extractor": "ner_duckling",
"start": 18,
"text": "today 5pm",
"value": "2017-07-26T17:00:00.000Z"
}
],
"intent": {
"confidence": 0.5469262356494486,
"name": "BookCab"
},
"intent_ranking": [
{
"confidence": 0.5469262356494486,
"name": "BookCab"
},
{
"confidence": 0.2812606328712321,
"name": "None"
},
{
"confidence": 0.08727531874740564,
"name": "ConfirmationNo"
},
{
"confidence": 0.0845378127319134,
"name": "ServiceRequestEnquiry"
}
],
"text": "I want to go ride today 5pm"
}
这个完整的训练集对我来说效果很好。这只是添加更多训练示例的问题。因此，当您进行更多测试时，如果您遇到一个无法按预期工作的示例，请将其添加到训练数据中并重新训练。从而教会您的模型处理更多不同的请求。
https://gist.github.com/wrathagom/7f05fbda75c785977bd07cd89e62ddd7
