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使用ResponseSelector实现校园招聘FAQ机器人
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: goodbye examples: | - 拜拜 - 再见 - 拜 - 退出 - 结束 - intent: greet examples: | - 你好 - 您好 - hello - hi - 喂 - 在么 - intent: faq/notes examples: | - 应聘ACME校园招聘职位的注意事项? - intent: faq/work_location examples: | - 校园招聘录取的应届生主要工作地点在哪里? - intent: faq/max_job_request examples: | - 最多申请几个职位? - intent: faq/audit examples: | - 各阶段审核说明 - intent: faq/write_exam_participate examples: | - 怎样参加笔试? - intent: faq/write_exam_location examples: | - 笔试考试地点如何安排? - intent: faq/write_exam_again examples: | - 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-offer examples: | - 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_arrangement examples: | - 面试什么时候开始?会提前多少天通知面试安排? - intent: faq/interview_times examples: | - 一般会安排几次面试? - intent: faq/interview_from examples: | - 面试的形式是怎样的?是单独面试还是小组面试? - intent: faq/interview_clothing examples: | - 对面试的服装有什么具体的要求? - intent: faq/interview_paperwork examples: | - 面试时需要携带什么资料? - intent: faq/interview_result examples: | - 如何查询面试结果?- 1
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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。- 1
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三.data/stories.yml文件
story即场景编排，如下所示：version: "3.1" stories: - story: greet steps: - intent: greet - action: utter_greet - story: say goodbye steps: - intent: goodbye - action: utter_goodbye- 1
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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 FAQs steps: - intent: faq - action: utter_faq- 1
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五.domain.yml文件
该文件主要包含intents、responses和actions等信息，如下所示：version: "3.1" session_config: session_expiration_time: 60 carry_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- 1
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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: "zh" pipeline: - name: JiebaTokenizer - name: LanguageModelFeaturizer model_name: "bert" # model_weights: "bert-base-chinese" model_weights: "L:/20230713_HuggingFaceModel/20231004_BERT/bert-base-chinese" - name: "DIETClassifier" epochs: 100 tensorboard_log_directory: ./log learning_rate: 0.001 - name: "ResponseSelector" policies: - name: MemoizationPolicy - name: TEDPolicy - name: RulePolicy assistant_id: 20231109-225257-frayed-branch- 1
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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_core@latest # 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:# port: # db: # password: #tracker_store: # type: mongod # url: # db: # username: # password: # 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 - 1
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八.模型训练和运行Rasa服务器
1.模型训练rasa train- 1
2.运行Rasa服务器
rasa run --cors "*"- 1
3.开启http server服务
python -m http.server- 1
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-support=auto --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 7 2023-11-10 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_id=None, should_add_diagnostic_data=False, is_finetuning=False, node_name=None). 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_id=None, should_add_diagnostic_data=False, is_finetuning=False, node_name=None). 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(name='train_JiebaTokenizer0', output_fingerprint='141a681b80024953b9b7865284b9fece')}'. 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 bert 2023-11-10 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- 1
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参考文献：
[1]《Rasa实战》
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