我已使用以下命令将从LUIS应用程序下载的json迁移为RASA格式：python -m rasa_nlu.train -c config_spacy.json
我的配置文件如下所示：
{
"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"]
}
如下所示，使用RASA格式的json生成了一个模型。但是，当我使用
http://localhost:5000/parse?q=book过一会儿
与我输入的文字及其所有相关实体相关的正确的高分意图。但是当我尝试另一个文本时：
http://localhost:5000/parse?q=I今天下午5点想去骑
返回的意图是正确的，但其Entities对象为空。正如您在json下方看到的那样，这种话语也具有与工作示例类似的映射到它的实体。
请帮助我知道这是否是每个使用RASA的人都遇到的问题，还是我在做任何错误？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": []
}
}
最佳答案
如果可以包括您在Rasa中使用的pipeline，这将很有帮助。您可以在configuration文件中找到它。假设您尚未更改config_spacy.json中的默认管道，那么您将使用ner_crf进行实体识别。
由于库的差异，Rasa可能需要的培训数据比LUIS还要多。从质量上讲，mitie管道通常需要较少的培训数据，但是要权衡的是需要花费更多的时间进行培训。
因此，问题的基本答案是：如果要使用ner_crf，则需要增加为实体识别提供的训练数据量。
话虽这么说：RideTime是您唯一的实体吗？如果是这样，您应该考虑将ner_duckling添加到管道中，该管道可以识别日期。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"
}
完整的培训对我来说非常有效。只需添加更多培训示例即可。因此，当您进行更多测试时，如果遇到无法正常工作的示例，请将其添加到训练数据中并重新训练。因此，教您的模型处理更多不同的请求。
https://gist.github.com/wrathagom/7f05fbda75c785977bd07cd89e62ddd7