有关电子商务网站建设与维护的书籍,浙江大学微纳加工平台,还有什么类型的网站,哪个网站可以做销售记录仪本文主要介绍使用ResponseSelector实现校园招聘FAQ机器人#xff0c;回答面试流程和面试结果查询的FAQ问题。FAQ机器人功能分为业务无关的功能和业务相关的功能2类。 一.data/nlu.yml文件   与普通意图相比#xff0c;ResponseSelector训练数据中的意图采用group/intent格…  本文主要介绍使用ResponseSelector实现校园招聘FAQ机器人回答面试流程和面试结果查询的FAQ问题。FAQ机器人功能分为业务无关的功能和业务相关的功能2类。 一.data/nlu.yml文件   与普通意图相比ResponseSelector训练数据中的意图采用group/intent格式检索意图。比如普通意图intent: greet而后者intent: faq/notes。如下所示 version: 3.1 nlu:- intent: goodbyeexamples: |- 拜拜- 再见- 拜- 退出- 结束- intent: greetexamples: |- 你好- 您好- hello- hi- 喂- 在么- intent: faq/notesexamples: |- 应聘ACME校园招聘职位的注意事项?- intent: faq/work_locationexamples: |- 校园招聘录取的应届生主要工作地点在哪里?- intent: faq/max_job_requestexamples: |- 最多申请几个职位?- intent: faq/auditexamples: |- 各阶段审核说明- intent: faq/write_exam_participateexamples: |- 怎样参加笔试?- intent: faq/write_exam_locationexamples: |- 笔试考试地点如何安排?- intent: faq/write_exam_againexamples: |- 笔试只安排一次吗?我笔试当天没有参加是否还有再次笔试的机会?- intent: faq/write_exam_with-out-offerexamples: |- 如果我没有收到笔试通知但我很想进入ACME能否直接进入考场参加考试?- intent: faq/interview_arrangementexamples: |- 面试什么时候开始?会提前多少天通知面试安排?- intent: faq/interview_timesexamples: |- 一般会安排几次面试?- intent: faq/interview_fromexamples: |- 面试的形式是怎样的?是单独面试还是小组面试?- intent: faq/interview_clothingexamples: |- 对面试的服装有什么具体的要求?- intent: faq/interview_paperworkexamples: |- 面试时需要携带什么资料?- intent: faq/interview_resultexamples: |- 如何查询面试结果?二.data/responses.yml文件   主要是根据相关intent来进行相应的response。比如utter_faq/notes的response对应于意图faq/notes。如下所示 version: 3.1 responses:utter_faq/notes:- text: 1、登在校园招聘板块内的职位信息才适用于应届毕业生招聘请所有的应届毕业生去校园招聘的版块寻找您感兴趣的职位。2、列出的每个职位的要求是该职位的最低要求为了保证您应聘的成功率希望您严格按照职位的要求考虑您的选择。3、提交成功后在招聘结束前您将不能修改或再次提交简历因此请于仔细确认填写信息后提交简历。utter_faq/work_location:- text: 招聘信息中包含各职位的工作地点内容请参考各职位内容的详细介绍。utter_faq/max_job_request:- text: 对于校园招聘最多申请2个职位。utter_faq/audit:- text: 1、简历审核应聘者需要通过ACME网站填写并提交个人简历ACME的招聘专员将对收取的简历进行认真的审查和筛选。了解应聘者的情况并筛选出符合职位要求的简历同时确认简历记载内容是否属实。2、笔试审核ACME技术类测试主要针对应聘者的专业技能进行检查和评价。3、面试审核经过实施评价应聘者基本素质的第一阶段面试和评价专业知识的第二阶段面试对应聘者是否符合ACME人才理念以及应聘者的工作能力做出客观的综合评价从而决定是否录用该应聘者。utter_faq/write_exam_participate:- text: 通过简历审核的应聘者我们将采用短信、e-mail、ACME公告栏以及电话通知的方式告知您utter_faq/write_exam_location:- text: 笔试地点将根据您在简历中填写的学校所在城市进行统筹安排utter_faq/write_exam_again:- text: 校园招聘的大规模的笔试仅安排一次请收到笔试通知的同学认真对待笔试机会。utter_faq/write_exam_with-out-offer:- text: 由于我们是按照严格的招聘流程筛选出的笔试名单所以非常抱歉对于没有收到笔试通知的同学就不能参加本次校园招聘的笔试。utter_faq/interview_arrangement:- text: 不同的职位面试进度安排不同除特殊安排外笔试结束一周左右会安排面试。utter_faq/interview_times:- text: 一般情况下业务部门和人力资源部会同时或者分别安排一次面试。个别特殊职位需要2次及以上的面试。utter_faq/interview_from:- text: 面试一般以单独面试的形式进行但根据各公司的面试安排也会进行小组面试。utter_faq/interview_clothing:- text: 面试着装没有统一要求但建议您尽量穿着较为正式的职业装参加。utter_faq/interview_paperwork:- text: 面试时请您携带可以证明您身份的有效证件有特殊要求的职位请携带好能证明您专业水平的证书原件以及复印件。utter_faq/interview_result:- text: 我们会通过邮件或电话的形式通知您面试结果。三.data/stories.yml文件   story即场景编排如下所示 version: 3.1 stories:- story: greetsteps:- intent: greet- action: utter_greet- story: say goodbyesteps:- intent: goodbye- action: utter_goodbye四.data/rules.yml文件   定义了规则名respond to FAQs当检索意图是faq时执行utter_faq如下所示 version: 3.1 rules:- rule: respond to FAQssteps:- intent: faq- action: utter_faq五.domain.yml文件   该文件主要包含intents、responses和actions等信息如下所示 version: 3.1session_config:session_expiration_time: 60carry_over_slots_to_new_session: true intents:- goodbye- greet- faq responses:utter_greet:- text: 你好我是 Silly我是一个基于 Rasa 的 FAQ 机器人utter_goodbye:- text: 再见utter_default:- text: 系统不明白您说的话 actions:- utter_goodbye- utter_greet- utter_default- utter_faq六.config.yml文件   主要是pipeline和policies设置。前者基本思路是分词、特征化、意图识别和实体抽取后者定义各种策略。特别注意FAQ机器人需要将ResponseSelector组件加入NLU的流水线并且还需要启用RulePolicy和设置rule参考四.data/rules.yml文件。如下所示 recipe: default.v1 language: zhpipeline: - name: JiebaTokenizer - name: LanguageModelFeaturizermodel_name: bert # model_weights: bert-base-chinesemodel_weights: L:/20230713_HuggingFaceModel/20231004_BERT/bert-base-chinese - name: DIETClassifierepochs: 100tensorboard_log_directory: ./loglearning_rate: 0.001 - name: ResponseSelectorpolicies: - name: MemoizationPolicy - name: TEDPolicy - name: RulePolicy assistant_id: 20231109-225257-frayed-branch七.endpoints.yml文件   action_endpoint、tracker_store和event_broker通常使用默认配置如下所示 # This file contains the different endpoints your bot can use.# Server where the models are pulled from. # https://rasa.com/docs/rasa/user-guide/running-the-server/#fetching-models-from-a-server/#models: # url: http://my-server.com/models/default_corelatest # wait_time_between_pulls: 10 # [optional](default: 100)# Server which runs your custom actions. # https://rasa.com/docs/rasa/core/actions/#custom-actions/action_endpoint:url: http://localhost:5055/webhook# Tracker store which is used to store the conversations. # By default the conversations are stored in memory. # https://rasa.com/docs/rasa/api/tracker-stores/#tracker_store: # type: redis # url: host of the redis instance, e.g. localhost # port: port of your redis instance, usually 6379 # db: number of your database within redis, e.g. 0 # password: password used for authentication#tracker_store: # type: mongod # url: url to your mongo instance, e.g. mongodb://localhost:27017 # db: name of the db within your mongo instance, e.g. rasa # username: username used for authentication # password: password used for authentication# Event broker which all conversation events should be streamed to. # https://rasa.com/docs/rasa/api/event-brokers/#event_broker: # url: localhost # username: username # password: password # queue: queue八.模型训练和运行Rasa服务器 1.模型训练 rasa train2.运行Rasa服务器 rasa run --cors *3.开启http server服务 python -m http.server说明测试FAQ机器人可以通过Web页面还可通过命令行rasa shell --debug。 九.PyCharm调试Rasa代码 1.Rasa中的DAG   Rasa中DAG图节点可能是NLP组件也可能是Policy组件本质上都可以抽象为Graph Component。如下所示   Rasa会把训练过的Component缓存到磁盘中当某个Component发生变化的时候比如CountVectorizer只会把依赖CountVectorizer的组件DIETClassifier、TEDPolicy和Policy Ensemble再训练而其它的组件不变。如下所示 2.PyCharm调试Rasa代码   PyCharm调试Rasa源码也比较方便主要是设置脚本路径、参数和工作目录如下所示   然后就可以调试训练数据是如何被处理的DAG是如何被构建的Component是如何被加载和运行的最终模型文件是如何被存储的等。Rasa中的fingerprint_key可能是唯一标识的意思。 3.rasa train nlu --debug日志   通过控制台输出日志可辅助理解Rasa执行过程以及源码调试如下所示 L:\20231106_ConversationSystem\20220407_RasaEcosystem\RasaBooks\RasaInAction\rasa_chinese_book_code\Chapter04\venv\Scripts\python.exe D:/Program Files/JetBrains/PyCharm 2023.1.3/plugins/python/helpers/pydev/pydevd.py --multiprocess --qt-supportauto --client 127.0.0.1 --port 38019 --file L:\20231106_ConversationSystem\20220407_RasaEcosystem\RasaBooks\RasaInAction\rasa_chinese_book_code\Chapter04\venv\Lib\site-packages\rasa\__main__.py train nlu --debug Connected to pydev debugger (build 232.9559.58)2023-11-10 23:24:32 DEBUG h5py._conv - Creating converter from 7 to 5 2023-11-10 23:24:32 DEBUG h5py._conv - Creating converter from 5 to 72023-11-10 23:26:17 DEBUG rasa.shared.nlu.training_data.loading - Training data format of data\nlu.yml is rasa_yml. # nul.yml文件(rasa_yml数据格式) 2023-11-10 23:26:17 DEBUG rasa.shared.nlu.training_data.loading - Training data format of data\responses.yml is rasa_yml. # responses.yml文件(rasa_yml数据格式) 2023-11-10 23:26:17 DEBUG rasa.shared.nlu.training_data.loading - Training data format of data\rules.yml is unk. # rules.yml文件(unk数据格式) 2023-11-10 23:26:17 DEBUG rasa.shared.nlu.training_data.loading - Training data format of data\stories.yml is unk. # stories.yml文件(unk数据格式)2023-11-10 23:26:33 DEBUG rasa.telemetry - Skipping telemetry reporting: no license hash found. # 跳过telemetry报告找不到许可证哈希。 2023-11-10 23:27:24 DEBUG rasa.engine.training.graph_trainer - Starting training. # 开始训练2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node train_JiebaTokenizer0 loading FingerprintComponent.create and kwargs: {}. # train_JiebaTokenizer0 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node run_JiebaTokenizer0 loading FingerprintComponent.create and kwargs: {}. # run_JiebaTokenizer0 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node run_LanguageModelFeaturizer1 loading FingerprintComponent.create and kwargs: {}. # run_LanguageModelFeaturizer1 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node train_DIETClassifier2 loading FingerprintComponent.create and kwargs: {}. # train_DIETClassifier2 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node train_ResponseSelector3 loading FingerprintComponent.create and kwargs: {}. # train_ResponseSelector3 2023-11-10 23:27:24 DEBUG rasa.engine.training.graph_trainer - Running the train graph in fingerprint mode. # 在fingerprint模式下运行训练图。 2023-11-10 23:27:24 DEBUG rasa.engine.runner.dask - Running graph with inputs: {__importer__: NluDataImporter}, targets: None and ExecutionContext(model_idNone, should_add_diagnostic_dataFalse, is_finetuningFalse, node_nameNone). 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node schema_validator loading DefaultV1RecipeValidator.create and kwargs: {}. # schema_validator 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node schema_validator running DefaultV1RecipeValidator.validate. # schema_validator 2023-11-10 23:27:24 DEBUG rasa.shared.nlu.training_data.training_data - Validating training data... # 验证训练数据... 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node finetuning_validator loading FinetuningValidator.create and kwargs: {}. # finetuning_validator 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node finetuning_validator running FinetuningValidator.validate. # finetuning_validator 2023-11-10 23:27:24 DEBUG rasa.engine.storage.local_model_storage - Resource finetuning_validator was requested for writing. # finetuning_validator 2023-11-10 23:27:24 DEBUG rasa.engine.storage.local_model_storage - Resource finetuning_validator was persisted. # finetuning_validator 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node nlu_training_data_provider loading NLUTrainingDataProvider.create and kwargs: {}. # nlu_training_data_provider 2023-11-10 23:27:24 DEBUG rasa.engine.graph - Node nlu_training_data_provider running NLUTrainingDataProvider.provide. # nlu_training_data_provider 2023-11-10 23:27:24 DEBUG rasa.shared.nlu.training_data.loading - Training data format of data\nlu.yml is rasa_yml. # nul.yml文件(rasa_yml数据格式) 2023-11-10 23:27:25 DEBUG rasa.shared.nlu.training_data.loading - Training data format of data\responses.yml is rasa_yml. # responses.yml文件(rasa_yml数据格式) 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node train_JiebaTokenizer0 running FingerprintComponent.run. # train_JiebaTokenizer0 2023-11-10 23:27:25 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key 963f41cf1cdb9cadc8914a14e070fb8e for class JiebaTokenizer. # 计算类JiebaTokenizer的指纹密钥 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node run_JiebaTokenizer0 running FingerprintComponent.run. # run_JiebaTokenizer0 2023-11-10 23:27:25 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key ae36d2dae4cc78840b153d44fee8f81a for class JiebaTokenizer. # 计算类JiebaTokenizer的指纹密钥 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node run_LanguageModelFeaturizer1 running FingerprintComponent.run. # run_LanguageModelFeaturizer1 2023-11-10 23:27:25 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key f2bfce545dd2c1c12fb895b075954315 for class LanguageModelFeaturizer. # 计算类LanguageModelFeaturizer的指纹密钥 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node train_DIETClassifier2 running FingerprintComponent.run. # train_DIETClassifier2 2023-11-10 23:27:25 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key 1d3616cf6980e5f0f38aa9ceb51f1e7a for class DIETClassifier. # 计算类DIETClassifier的指纹密钥 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node train_ResponseSelector3 running FingerprintComponent.run. # train_ResponseSelector3 2023-11-10 23:27:25 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key b91434757a05a4178cdc7f7882cfd9aa for class ResponseSelector. # 计算类ResponseSelector的指纹密钥 2023-11-10 23:27:25 DEBUG rasa.engine.training.graph_trainer - Running the pruned train graph with real node execution. # 使用真实节点执行修剪的训练图。 2023-11-10 23:27:25 DEBUG rasa.engine.runner.dask - Running graph with inputs: {__importer__: NluDataImporter}, targets: None and ExecutionContext(model_idNone, should_add_diagnostic_dataFalse, is_finetuningFalse, node_nameNone). 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook LoggingHook.on_before_node running for node nlu_training_data_provider. # nlu_training_data_provider 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook TrainingHook.on_before_node running for node nlu_training_data_provider. # nlu_training_data_provider 2023-11-10 23:27:25 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key 1fbfa24243412736ce1002efbeba382f for class NLUTrainingDataProvider. # 计算类NLUTrainingDataProvider的指纹密钥 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node nlu_training_data_provider loading PrecomputedValueProvider.create and kwargs: {}. # nlu_training_data_provider 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node nlu_training_data_provider running PrecomputedValueProvider.get_value. # nlu_training_data_provider 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook LoggingHook.on_after_node running for node nlu_training_data_provider. # nlu_training_data_provider 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook TrainingHook.on_after_node running for node nlu_training_data_provider. # nlu_training_data_provider 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook LoggingHook.on_before_node running for node train_JiebaTokenizer0. # train_JiebaTokenizer0 2023-11-10 23:27:25 INFO rasa.engine.training.hooks - Starting to train component JiebaTokenizer. # 开始训练组件JiebaTokenizer。 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook TrainingHook.on_before_node running for node train_JiebaTokenizer0. # train_JiebaTokenizer0 2023-11-10 23:27:25 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key 963f41cf1cdb9cadc8914a14e070fb8e for class JiebaTokenizer. # 计算类JiebaTokenizer的指纹密钥 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node train_JiebaTokenizer0 loading JiebaTokenizer.create and kwargs: {}. # train_JiebaTokenizer0 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Node train_JiebaTokenizer0 running JiebaTokenizer.train. # train_JiebaTokenizer0 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook LoggingHook.on_after_node running for node train_JiebaTokenizer0. # train_JiebaTokenizer0 2023-11-10 23:27:25 INFO rasa.engine.training.hooks - Finished training component JiebaTokenizer. # 完成训练组件JiebaTokenizer。 2023-11-10 23:27:25 DEBUG rasa.engine.graph - Hook TrainingHook.on_after_node running for node train_JiebaTokenizer0. # train_JiebaTokenizer0 2023-11-10 23:27:25 DEBUG rasa.engine.training.hooks - Caching Resource with fingerprint_key: 963f41cf1cdb9cadc8914a14e070fb8e and output_fingerprint 141a681b80024953b9b7865284b9fece. 2023-11-10 23:27:25 DEBUG rasa.engine.storage.local_model_storage - Resource train_JiebaTokenizer0 was requested for reading. # train_JiebaTokenizer0 2023-11-10 23:27:25 DEBUG rasa.engine.storage.resource - Skipped caching resource train_JiebaTokenizer0 as no persisted data was found. # 跳过缓存资源train_JiebaTokenizer0因为找不到持久化数据。 2023-11-10 23:27:25 DEBUG rasa.engine.caching - Caching output of type Resource succeeded. # 缓存类型为Resource的输出成功。 2023-11-10 23:27:26 DEBUG rasa.engine.graph - Hook LoggingHook.on_before_node running for node run_JiebaTokenizer0. # run_JiebaTokenizer0 2023-11-10 23:27:26 DEBUG rasa.engine.graph - Hook TrainingHook.on_before_node running for node run_JiebaTokenizer0. # run_JiebaTokenizer0 2023-11-10 23:27:26 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key 496a8741f1dfb458bbfedb535d343623 for class JiebaTokenizer. # 计算类JiebaTokenizer的指纹密钥 2023-11-10 23:27:26 DEBUG rasa.engine.graph - Node run_JiebaTokenizer0 loading JiebaTokenizer.load and kwargs: {resource: Resource(nametrain_JiebaTokenizer0, output_fingerprint141a681b80024953b9b7865284b9fece)}. 2023-11-10 23:27:26 DEBUG rasa.engine.graph - Node run_JiebaTokenizer0 running JiebaTokenizer.process_training_data. # run_JiebaTokenizer0# jieba分词 Building prefix dict from the default dictionary ... 2023-11-10 23:27:26 DEBUG jieba - Building prefix dict from the default dictionary ... Loading model from cache C:\Users\ADMINI~1\AppData\Local\Temp\jieba.cache 2023-11-10 23:27:26 DEBUG jieba - Loading model from cache C:\Users\ADMINI~1\AppData\Local\Temp\jieba.cache Loading model cost 1.116 seconds. 2023-11-10 23:27:27 DEBUG jieba - Loading model cost 1.116 seconds. Prefix dict has been built successfully. 2023-11-10 23:27:27 DEBUG jieba - Prefix dict has been built successfully.2023-11-10 23:27:27 DEBUG rasa.engine.graph - Hook LoggingHook.on_after_node running for node run_JiebaTokenizer0. 2023-11-10 23:27:27 DEBUG rasa.engine.graph - Hook TrainingHook.on_after_node running for node run_JiebaTokenizer0. 2023-11-10 23:27:27 DEBUG rasa.engine.training.hooks - Caching TrainingData with fingerprint_key: 496a8741f1dfb458bbfedb535d343623 and output_fingerprint 1baa8435dc0351e013e3b8f3635e83d6. 2023-11-10 23:27:27 DEBUG rasa.engine.graph - Hook LoggingHook.on_before_node running for node run_LanguageModelFeaturizer1. 2023-11-10 23:27:27 DEBUG rasa.engine.graph - Hook TrainingHook.on_before_node running for node run_LanguageModelFeaturizer1. 2023-11-10 23:27:27 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key de5a4adf999a20fb8e5716903003508c for class LanguageModelFeaturizer. 2023-11-10 23:27:27 DEBUG rasa.engine.graph - Node run_LanguageModelFeaturizer1 loading LanguageModelFeaturizer.load and kwargs: {}. 2023-11-10 23:27:28 DEBUG rasa.nlu.featurizers.dense_featurizer.lm_featurizer - Loading Tokenizer and Model for bert2023-11-10 23:27:32 DEBUG rasa.engine.graph - Node run_LanguageModelFeaturizer1 running LanguageModelFeaturizer.process_training_data. 2023-11-10 23:27:41 DEBUG rasa.engine.graph - Hook LoggingHook.on_after_node running for node run_LanguageModelFeaturizer1. 2023-11-10 23:27:41 DEBUG rasa.engine.graph - Hook TrainingHook.on_after_node running for node run_LanguageModelFeaturizer1. 2023-11-10 23:27:41 DEBUG rasa.engine.training.hooks - Caching TrainingData with fingerprint_key: de5a4adf999a20fb8e5716903003508c and output_fingerprint 1192d8329eb2a6d87f6e965765d10871. 2023-11-10 23:27:41 DEBUG rasa.engine.graph - Hook LoggingHook.on_before_node running for node train_DIETClassifier2. 2023-11-10 23:27:41 INFO rasa.engine.training.hooks - Starting to train component DIETClassifier. 2023-11-10 23:27:41 DEBUG rasa.engine.graph - Hook TrainingHook.on_before_node running for node train_DIETClassifier2. 2023-11-10 23:27:41 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key 7d66b69a551ffbc2a45237a02ffc5aa7 for class DIETClassifier. 2023-11-10 23:27:41 DEBUG rasa.engine.graph - Node train_DIETClassifier2 loading DIETClassifier.create and kwargs: {}.2023-11-10 23:27:41 DEBUG rasa.engine.graph - Node train_DIETClassifier2 running DIETClassifier.train. 2023-11-10 23:27:41 DEBUG rasa.nlu.classifiers.diet_classifier - No label features found. Computing default label features. 2023-11-10 23:27:41 DEBUG rasa.nlu.classifiers.diet_classifier - You specified DIET to train entities, but no entities are present in the training data. Skipping training of entities. 2023-11-10 23:27:42 DEBUG rasa.nlu.classifiers.diet_classifier - Following metrics will be logged during training: 2023-11-10 23:27:42 DEBUG rasa.nlu.classifiers.diet_classifier - t_loss (total loss) 2023-11-10 23:27:42 DEBUG rasa.nlu.classifiers.diet_classifier - i_acc (intent acc) 2023-11-10 23:27:42 DEBUG rasa.nlu.classifiers.diet_classifier - i_loss (intent loss) 2023-11-10 23:27:42 DEBUG rasa.utils.tensorflow.data_generator - The provided batch size is a list, this data generator will use a linear increasing batch size.Epochs: 0%| | 0/100 [00:00?, ?it/s] Epochs: 100%|██████████| 100/100 [01:2600:00, 1.15it/s, t_loss0.258, i_loss0.0123, i_acc1] 2023-11-10 23:29:09 DEBUG rasa.engine.storage.local_model_storage - Resource train_DIETClassifier2 was requested for writing. 2023-11-10 23:29:09 DEBUG rasa.engine.storage.local_model_storage - Resource train_DIETClassifier2 was persisted. 2023-11-10 23:29:09 DEBUG rasa.engine.graph - Hook LoggingHook.on_after_node running for node train_DIETClassifier2. 2023-11-10 23:29:09 INFO rasa.engine.training.hooks - Finished training component DIETClassifier. 2023-11-10 23:29:09 DEBUG rasa.engine.graph - Hook TrainingHook.on_after_node running for node train_DIETClassifier2. 2023-11-10 23:29:09 DEBUG rasa.engine.training.hooks - Caching Resource with fingerprint_key: 7d66b69a551ffbc2a45237a02ffc5aa7 and output_fingerprint 9a50714386a54eebbd0b5eb4ab2fd23c. 2023-11-10 23:29:09 DEBUG rasa.engine.storage.local_model_storage - Resource train_DIETClassifier2 was requested for reading. 2023-11-10 23:29:09 DEBUG rasa.engine.caching - Caching output of type Resource succeeded. 2023-11-10 23:29:11 DEBUG rasa.engine.graph - Hook LoggingHook.on_before_node running for node train_ResponseSelector3. 2023-11-10 23:29:11 INFO rasa.engine.training.hooks - Starting to train component ResponseSelector. 2023-11-10 23:29:11 DEBUG rasa.engine.graph - Hook TrainingHook.on_before_node running for node train_ResponseSelector3. 2023-11-10 23:29:11 DEBUG rasa.engine.training.fingerprinting - Calculated fingerprint_key 0e102b0ba0b459b1556ae9eb4aaac987 for class ResponseSelector. 2023-11-10 23:29:11 DEBUG rasa.engine.graph - Node train_ResponseSelector3 loading ResponseSelector.create and kwargs: {}. 2023-11-10 23:29:11 DEBUG rasa.engine.graph - Node train_ResponseSelector3 running ResponseSelector.train. 2023-11-10 23:29:11 INFO rasa.nlu.selectors.response_selector - Retrieval intent parameter was left to its default value. This response selector will be trained on training examples combining all retrieval intents. 2023-11-10 23:29:11 DEBUG rasa.nlu.classifiers.diet_classifier - No label features found. Computing default label features. 2023-11-10 23:29:11 DEBUG rasa.nlu.selectors.response_selector - Following metrics will be logged during training: 2023-11-10 23:29:11 DEBUG rasa.nlu.selectors.response_selector - t_loss (total loss) 2023-11-10 23:29:11 DEBUG rasa.nlu.selectors.response_selector - r_acc (response acc) 2023-11-10 23:29:11 DEBUG rasa.nlu.selectors.response_selector - r_loss (response loss) 2023-11-10 23:29:11 DEBUG rasa.utils.tensorflow.data_generator - The provided batch size is a list, this data generator will use a linear increasing batch size. Epochs: 100%|██████████| 300/300 [00:3900:00, 7.55it/s, t_loss2.93, r_loss1.17, r_acc1] 2023-11-10 23:29:51 DEBUG rasa.engine.storage.local_model_storage - Resource train_ResponseSelector3 was requested for writing. 2023-11-10 23:29:51 DEBUG rasa.engine.storage.local_model_storage - Resource train_ResponseSelector3 was persisted. 2023-11-10 23:29:51 DEBUG rasa.engine.storage.local_model_storage - Resource train_ResponseSelector3 was requested for writing. 2023-11-10 23:29:51 DEBUG rasa.engine.storage.local_model_storage - Resource train_ResponseSelector3 was persisted. 2023-11-10 23:29:51 DEBUG rasa.engine.graph - Hook LoggingHook.on_after_node running for node train_ResponseSelector3. 2023-11-10 23:29:51 INFO rasa.engine.training.hooks - Finished training component ResponseSelector. 2023-11-10 23:29:51 DEBUG rasa.engine.graph - Hook TrainingHook.on_after_node running for node train_ResponseSelector3. 2023-11-10 23:29:51 DEBUG rasa.engine.training.hooks - Caching Resource with fingerprint_key: 0e102b0ba0b459b1556ae9eb4aaac987 and output_fingerprint 300fbcfe9f004bf2a6870e283e7b4f92. 2023-11-10 23:29:51 DEBUG rasa.engine.storage.local_model_storage - Resource train_ResponseSelector3 was requested for reading. 2023-11-10 23:29:51 DEBUG rasa.engine.caching - Caching output of type Resource succeeded. 2023-11-10 23:29:51 DEBUG rasa.engine.storage.local_model_storage - Start to created model package for path models\nlu-20231110-232632-arid-seasoning.tar.gz. 2023-11-10 23:29:58 DEBUG rasa.engine.storage.local_model_storage - Model package created in path models\nlu-20231110-232632-arid-seasoning.tar.gz. Your Rasa model is trained and saved at models\nlu-20231110-232632-arid-seasoning.tar.gz. 2023-11-10 23:29:58 DEBUG rasa.telemetry - Skipping telemetry reporting: no license hash found.Process finished with exit code 0参考文献 [1]《Rasa实战》