Rasa shell

Rasa Open Source
1

Hi, I was following the steps with the Rasa course on Udemy, that is presented by Mady Mantha. in section 3, lecture 1, she typed rasa shell and then she started to chat with the bot and it responded to her. I tried what she did and got a different results than her.

NLU model loaded. Type a message and press enter to parse it. Next message: hi { “text”: “hi”, “intent”: { “id”: -2175170181951886750, “name”: “greet”, “confidence”: 0.9976823925971985 }, “entities”: [], “intent_ranking”: [ { “id”: -2175170181951886750, “name”: “greet”, “confidence”: 0.9976823925971985 }, { “id”: -7296413657259518803, “name”: “goodbye”, “confidence”: 0.0005970306810922921 }, { “id”: -6676306855074094835, “name”: “ask_eat_healthy”, “confidence”: 0.0005204474437050521 }, { “id”: 5751836863188558239, “name”: “mood_great”, “confidence”: 0.00048421788960695267 }, { “id”: 5235019163836095929, “name”: “thankyou”, “confidence”: 0.00020467927970457822 }, { “id”: 5823801132937241105, “name”: “affirm”, “confidence”: 0.00019201762916054577 }, { “id”: 292196863936170350, “name”: “out_of_scope”, “confidence”: 0.00018072377133648843 }, { “id”: -4263807556109513959, “name”: “bot_challenge”, “confidence”: 7.941111834952608e-05 }, { “id”: 6952186192965535413, “name”: “inform”, “confidence”: 3.985824150731787e-05 }, { “id”: -4021515175722973480, “name”: “ask_lower_stress”, “confidence”: 1.924479875015095e-05 } ], “response_selector”: { “all_retrieval_intents”: [], “default”: { “response”: { “id”: null, “response_templates”: null, “confidence”: 0.0, “intent_response_key”: null, “template_name”: “utter_None” }, “ranking”: [] } } }

2

This looks like the output from rasa shell nlu.

Which command you are trying to run?

3

Did you get any specific warning messages when you ran rasa train? Seems like you accidentally trained an NLU-only model, perhaps because your domain or stories weren’t available.

4

I’m using the rasa shell command

5

Honestly, I tried as far as possible like the course. and no errors showed up; except the GPU errors which are unharmful.

6

Can you post the full output of the train command? Maybe rasa train --debug will be more helpful.

7

I’ll try

rasa train --debug

C:\Users\maher\rasa_course>rasa shell

fatal: bad revision ‘HEAD’ 2021-02-11 11:05:41.743796: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library ‘cudart64_101.dll’; dlerror: cudart64_101.dll not found 2021-02-11 11:05:41.744043: 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. 2021-02-11 11:05:45.875032: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library ‘nvcuda.dll’; dlerror: nvcuda.dll not found 2021-02-11 11:05:45.875183: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303) 2021-02-11 11:05:45.879180: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: MaherSalamin 2021-02-11 11:05:45.879480: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: MaherSalamin 2021-02-11 11:05:45 INFO rasa.model - Loading model models\nlu-20210211-110125.tar.gz… 2021-02-11 11:05:49.240832: 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. 2021-02-11 11:05:49.293483: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1f6af3eabc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2021-02-11 11:05:49.293955: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version NLU model loaded. Type a message and press enter to parse it. Next message:

8

This looks like the output of the rasa shell command, not the rasa train command, can you try again?

9

I’m sorry, my bad …

here it is… …

Training NLU model… 2021-02-11 10:59:22 INFO rasa.shared.nlu.training_data.training_data - Training data stats: 2021-02-11 10:59:22 INFO rasa.shared.nlu.training_data.training_data - Number of intent examples: 137 (13 distinct intents)

2021-02-11 10:59:22 INFO rasa.shared.nlu.training_data.training_data - Found intents: ‘affirm’, ‘inform’, ‘greet’, ‘out_of_scope’, ‘goodbye’, ‘ask_lower_stress’, ‘ask_exercise’, ‘bot_challenge’, ‘mood_great’, ‘mood_unhappy’, ‘thankyou’, ‘ask_eat_healthy’, ‘deny’ 2021-02-11 10:59:22 INFO rasa.shared.nlu.training_data.training_data - Number of response examples: 0 (0 distinct responses) 2021-02-11 10:59:22 INFO rasa.shared.nlu.training_data.training_data - Number of entity examples: 10 (3 distinct entities) 2021-02-11 10:59:22 INFO rasa.shared.nlu.training_data.training_data - Found entity types: ‘stress’, ‘sleep’, ‘exercise’ 2021-02-11 10:59:22 INFO rasa.nlu.model - Starting to train component WhitespaceTokenizer 2021-02-11 10:59:22 INFO rasa.nlu.model - Finished training component. 2021-02-11 10:59:22 INFO rasa.nlu.model - Starting to train component RegexFeaturizer 2021-02-11 10:59:22 INFO rasa.nlu.model - Finished training component. 2021-02-11 10:59:22 INFO rasa.nlu.model - Starting to train component LexicalSyntacticFeaturizer 2021-02-11 10:59:22 INFO rasa.nlu.model - Finished training component. 2021-02-11 10:59:22 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer 2021-02-11 10:59:22 INFO rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer - 200 vocabulary slots consumed out of 1200 slots configured for text attribute. 2021-02-11 10:59:22 INFO rasa.nlu.model - Finished training component. 2021-02-11 10:59:22 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer 2021-02-11 10:59:22 INFO rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer - 1481 vocabulary slots consumed out of 2481 slots configured for text attribute. 2021-02-11 10:59:23 INFO rasa.nlu.model - Finished training component. 2021-02-11 10:59:23 INFO rasa.nlu.model - Starting to train component DIETClassifier 2021-02-11 10:59:23.908075: 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. 2021-02-11 10:59:24.623700: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2c418571860 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2021-02-11 10:59:24.624184: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version Epochs: 100%|████████████| 100/100 [00:46<00:00, 2.14it/s, t_loss=1.924, i_acc=1.000, e_f1=1.000] 2021-02-11 11:01:15 INFO rasa.utils.tensorflow.models - Finished training. 2021-02-11 11:01:16 INFO rasa.nlu.model - Finished training component. 2021-02-11 11:01:16 INFO rasa.nlu.model - Starting to train component EntitySynonymMapper 2021-02-11 11:01:16 INFO rasa.nlu.model - Finished training component. 2021-02-11 11:01:16 INFO rasa.nlu.model - Starting to train component ResponseSelector 2021-02-11 11:01:16 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. 2021-02-11 11:01:16 INFO rasa.nlu.model - Finished training component. 2021-02-11 11:01:16 INFO rasa.nlu.model - Starting to train component FallbackClassifier 2021-02-11 11:01:16 INFO rasa.nlu.model - Finished training component. 2021-02-11 11:01:23 INFO rasa.nlu.model - Successfully saved model into ‘C:\Users\maher\AppData\Local\Temp\tmpcjdp06o9\nlu’ NLU model training completed. Your Rasa model is trained and saved at ‘C:\Users\maher\rasa_course\models\nlu-20210211-110125.tar.gz’. Core training was skipped because no valid domain file was found. Only an NLU-model was created. Please specify a valid domain using the ‘–domain’ argument or check if the provided domain file exists.

10

Here is why there is no core model being trained:

20210211-110125.tar.gz’. Core training was skipped because no valid domain file was found. Only an NLU-model was created. Please specify a valid domain using the ‘–domain’ argument or check if the provided domain file exists.

Maybe try running rasa data validate to check your domain file? Or does it live in a different location than just ./domain.yml?