作者：杨夕
项目地址：https://github.com/km1994/nlp_paper_study
个人介绍：大佬们好，我叫杨夕，该项目主要是本人在研读顶会论文和复现经典论文过程中，所见、所思、所想、所闻，可能存在一些理解错误，希望大佬们多多指正。
- 第一步是创建一个新的Rasa项目。要做到这一点，运行下面的代码:
rasa init --no-prompt注：rasa init命令创建rasa项目所需的所有文件，并根据一些示例数据训练一个简单的机器人。如果你省略了——no-prompt参数，将会询问你一些关于项目设置的问题。
- 运行过程
$ rasa init --no-prompt
>>>
Welcome to Rasa! 🤖
To get started quickly, an initial project will be created.
If you need some help, check out the documentation at https://rasa.com/docs/rasa.
Created project directory at '/web/workspace/yangkm/python_wp/nlu/DSWp'.
Finished creating project structure.
Training an initial model...
Training Core model...
Processed Story Blocks: 100%|█████████████████████████████████████████████| 5/5 [00:00<00:00, 3562.34it/s, # trackers=1]
Processed Story Blocks: 100%|█████████████████████████████████████████████| 5/5 [00:00<00:00, 1523.54it/s, # trackers=5]
Processed Story Blocks: 100%|█████████████████████████████████████████████| 5/5 [00:00<00:00, 380.28it/s, # trackers=20]
Processed Story Blocks: 100%|█████████████████████████████████████████████| 5/5 [00:00<00:00, 301.26it/s, # trackers=24]
Processed trackers: 100%|█████████████████████████████████████████████████| 5/5 [00:00<00:00, 2233.39it/s, # actions=16]
Processed actions: 16it [00:00, 14986.35it/s, # examples=16]
Processed trackers: 100%|█████████████████████████████████████████████| 231/231 [00:00<00:00, 899.80it/s, # actions=126]
Epochs: 0%| | 0/100 [00:00<?, ?it/s]/home/amy/.conda/envs/yangkm/lib/python3.6/site-packages/rasa/utils/tensorflow/model_data.py:386: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
final_data[k].append(np.concatenate(np.array(v)))
Epochs: 100%|████████████████████████████████████| 100/100 [00:06<00:00, 14.77it/s, t_loss=0.083, loss=0.009, acc=1.000]
2020-09-17 16:46:48 INFO rasa.utils.tensorflow.models - Finished training.
2020-09-17 16:46:48 INFO rasa.core.agent - Persisted model to '/tmp/tmpjkpkgun2/core'
Core model training completed.
Training NLU model...
2020-09-17 16:46:48 INFO rasa.nlu.training_data.training_data - Training data stats:
2020-09-17 16:46:48 INFO rasa.nlu.training_data.training_data - Number of intent examples: 43 (7 distinct intents)
2020-09-17 16:46:48 INFO rasa.nlu.training_data.training_data - Found intents: 'mood_unhappy', 'bot_challenge', 'deny', 'affirm', 'greet', 'mood_great', 'goodbye'
2020-09-17 16:46:48 INFO rasa.nlu.training_data.training_data - Number of response examples: 0 (0 distinct responses)
2020-09-17 16:46:48 INFO rasa.nlu.training_data.training_data - Number of entity examples: 0 (0 distinct entities)
2020-09-17 16:46:48 INFO rasa.nlu.model - Starting to train component WhitespaceTokenizer
2020-09-17 16:46:48 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:48 INFO rasa.nlu.model - Starting to train component RegexFeaturizer
2020-09-17 16:46:48 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:48 INFO rasa.nlu.model - Starting to train component LexicalSyntacticFeaturizer
2020-09-17 16:46:48 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:48 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer
2020-09-17 16:46:48 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:48 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer
2020-09-17 16:46:48 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:48 INFO rasa.nlu.model - Starting to train component DIETClassifier
/home/amy/.conda/envs/yangkm/lib/python3.6/site-packages/rasa/utils/common.py:363: UserWarning: You specified 'DIET' to train entities, but no entities are present in the training data. Skip training of entities.
Epochs: 0%| | 0/100 [00:00<?, ?it/s]/home/amy/.conda/envs/yangkm/lib/python3.6/site-packages/rasa/utils/tensorflow/model_data.py:386: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
final_data[k].append(np.concatenate(np.array(v)))
Epochs: 100%|████████████████████████████████| 100/100 [00:05<00:00, 18.36it/s, t_loss=1.475, i_loss=0.095, i_acc=1.000]
2020-09-17 16:46:58 INFO rasa.utils.tensorflow.models - Finished training.
2020-09-17 16:46:59 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:59 INFO rasa.nlu.model - Starting to train component EntitySynonymMapper
2020-09-17 16:46:59 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:59 INFO rasa.nlu.model - Starting to train component ResponseSelector
2020-09-17 16:46:59 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.
2020-09-17 16:46:59 INFO rasa.nlu.model - Finished training component.
2020-09-17 16:46:59 INFO rasa.nlu.model - Successfully saved model into '/tmp/tmpjkpkgun2/nlu'
NLU model training completed.
Your Rasa model is trained and saved at '/web/workspace/yangkm/python_wp/nlu/DSWp/models/20200917-164632.tar.gz'.
If you want to speak to the assistant, run 'rasa shell' at any time inside the project directory.将在该目录下参加以下文件：
| 文件名称 | 作用说明 |
| init.py | 帮助python查找操作的空文件 |
| actions.py | 为你的自定义操作编写代码 |
| config.yml ‘*’ | 配置NLU和Core模型 |
| credentials.yml | 连接到其他服务的详细信息 |
| data/nlu.md ‘*’ | 你的NLU训练数据 |
| data/stories.md ‘*’ | 你的故事 |
| config.yml ‘*’ | 配置NLU和Core模型 |
| domain.yml ‘*’ | 你的助手的域 |
| endpoints.yml | 接到fb messenger等通道的详细信息 |
| models/.tar.gz | 你的初始模型 |
注：最重要的文件用“*”标记。你将在本教程中了解所有这些文件。
$ rasa shell
>>>
2021-01-30 18:01:25.946702: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2021-01-30 18:01:25.946959: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-01-30 18:01:28.518362: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2021-01-30 18:01:28.938568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce 940MX computeCapability: 5.0
coreClock: 0.8605GHz coreCount: 4 deviceMemorySize: 2.00GiB deviceMemoryBandwidth: 37.33GiB/s
2021-01-30 18:01:28.939909: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2021-01-30 18:01:28.940935: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cublas64_10.dll'; dlerror: cublas64_10.dll not found
2021-01-30 18:01:28.942048: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found
2021-01-30 18:01:28.942970: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found
2021-01-30 18:01:28.943862: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2021-01-30 18:01:28.945788: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cusparse64_10.dll'; dlerror: cusparse64_10.dll not found
2021-01-30 18:01:28.950147: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudnn64_7.dll'; dlerror: cudnn64_7.dll not found
2021-01-30 18:01:28.950220: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1753] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-01-30 18:01:28 INFO rasa.model - Loading model models\20210130-175951.tar.gz...
2021-01-30 18:01:31 INFO root - Connecting to channel 'cmdline' which was specified by the '--connector' argument. Any other channels will be ignored. To connect to all given channels, omit the '--connector' argument.
2021-01-30 18:01:31 INFO root - Starting Rasa server on http://localhost:5005
2021-01-30 18:01:31 INFO rasa.model - Loading model models\20210130-175951.tar.gz...
2021-01-30 18:01:36.663436: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-01-30 18:01:36.676013: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x27e2f29b300 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-01-30 18:01:36.676366: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-01-30 18:01:36.677138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-30 18:01:36.677640: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]
2021-01-30 18:01:45 INFO root - Rasa server is up and running.
Bot loaded. Type a message and press enter (use '/stop' to exit):
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Hey! How are you?
Your input -> ok
Great, carry on!
Your input -> yes
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