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RasaHQ / rasa-demo

:tiger: Sara - the Rasa Demo Bot: An example of a contextual AI assistant built with the open source Rasa Stack
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rasa demo - train issue #554

Open SreenijaK opened 4 years ago

SreenijaK commented 4 years ago

rasa-version: 1.10.4 python-version: Python 3.6.9 os: ubuntu 18.04

I'm following this tutorial https://blog.rasa.com/how-to-build-a-voice-assistant-with-open-source-rasa-and-mozilla-tools/

when i try 2.3 step : rasa train --augmentation 0

i get the following error. Training Core model... Processed Story Blocks: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 525/525 [00:00<00:00, 728.31it/s, # trackers=1] Processed Story Blocks: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 525/525 [00:28<00:00, 23.99it/s, # trackers=46] Processed Story Blocks: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 525/525 [00:36<00:00, 14.37it/s, # trackers=50] Processed Story Blocks: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 525/525 [00:35<00:00, 14.76it/s, # trackers=48] Processed trackers: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 939/939 [00:02<00:00, 393.22it/s, # actions=7549] 2020-07-02 11:44:21.885817: E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303) Epochs: 0%| | 0/20 [00:00<?, ?it/s]/home/sreenijak/python-envs/rasaenv/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%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [04:04<00:00, 9.97s/it, t_loss=0.485, loss=0.129, acc=0.999] 2020-07-02 11:48:42 INFO rasa.utils.tensorflow.models - Finished training. Processed trackers: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 786.88it/s, # actions=1639] Processed actions: 1639it [00:00, 6310.55it/s, # examples=1639] Processed trackers: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 439/439 [00:00<00:00, 969.91it/s, # actions=939] 2020-07-02 11:48:47 INFO rasa.core.agent - Persisted model to '/tmp/tmpercnpaoi/core' Core model training completed. Training NLU model... 2020-07-02 11:48:47 INFO absl - Using /tmp/tfhub_modules to cache modules. 2020-07-02 11:48:47 INFO absl - Downloading TF-Hub Module 'http://models.poly-ai.com/convert/v1/model.tar.gz'. 2020-07-02 11:48:50 INFO absl - Downloaded http://models.poly-ai.com/convert/v1/model.tar.gz, Total size: 152.02MB 2020-07-02 11:48:50 INFO absl - Downloaded TF-Hub Module 'http://models.poly-ai.com/convert/v1/model.tar.gz'. /home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/utils/common.py:363: UserWarning: Please configure the number of 'epochs' in your configuration file. We will change the default value of 'epochs' in the future to 1. 2020-07-02 11:49:10 INFO rasa.nlu.training_data.training_data - Training data stats: 2020-07-02 11:49:10 INFO rasa.nlu.training_data.training_data - Number of intent examples: 5277 (31 distinct intents) 2020-07-02 11:49:10 INFO rasa.nlu.training_data.training_data - Found intents: 'switch', 'ask_which_events', 'explain', 'bye', 'thank', 'contact_sales', 'ask_how_contribute', 'greet', 'ask_question_in_forum', 'install_rasa', 'deny', 'nlu_info', 'technical_question', 'next_step', 'nlu_generation_tool_recommendation', 'chitchat', 'react_positive', 'affirm', 'react_negative', 'why_rasa', 'ask_why_contribute', 'out_of_scope', 'restart', 'pipeline_recommendation', 'signup_newsletter', 'human_handoff', 'canthelp', 'source_code', 'faq', 'enter_data', 'how_to_get_started' 2020-07-02 11:49:10 INFO rasa.nlu.training_data.training_data - Number of response examples: 2357 (37 distinct responses) 2020-07-02 11:49:10 INFO rasa.nlu.training_data.training_data - Number of entity examples: 956 (10 distinct entities) 2020-07-02 11:49:10 INFO rasa.nlu.training_data.training_data - Found entity types: 'product', 'nlu_part', 'job_function', 'location', 'language', 'company', 'current_api', 'entity', 'name', 'user_type' 2020-07-02 11:49:10 INFO rasa.nlu.model - Starting to train component ConveRTTokenizer 2020-07-02 11:49:56 INFO rasa.nlu.model - Finished training component. 2020-07-02 11:49:56 INFO rasa.nlu.model - Starting to train component ConveRTFeaturizer Text batches: 0%| | 0/83 [00:00<?, ?it/s]/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/nlu/featurizers/dense_featurizer/convert_featurizer.py:129: 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 return np.array(final_embeddings) Text batches: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 83/83 [00:59<00:00, 1.21it/s] Response batches: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 37/37 [01:11<00:00, 1.13it/s] 2020-07-02 11:52:07 INFO rasa.nlu.model - Finished training component. 2020-07-02 11:52:07 INFO rasa.nlu.model - Starting to train component RegexFeaturizer 2020-07-02 11:52:09 INFO rasa.nlu.model - Finished training component. 2020-07-02 11:52:09 INFO rasa.nlu.model - Starting to train component LexicalSyntacticFeaturizer 2020-07-02 11:52:11 INFO rasa.nlu.model - Finished training component. 2020-07-02 11:52:11 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer /home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/utils/common.py:363: UserWarning: The out of vocabulary token 'oov' was configured, but could not be found in any one of the NLU training examples. All unseen words will be ignored during prediction. More info at https://rasa.com/docs/rasa/nlu/components/#countvectorsfeaturizer /home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/utils/common.py:363: UserWarning: The out of vocabulary token 'oov' was configured, but could not be found in any one of the ResponseSelector training examples. All unseen words will be ignored during prediction. More info at https://rasa.com/docs/rasa/nlu/components/#countvectorsfeaturizer 2020-07-02 11:52:17 INFO rasa.nlu.model - Finished training component. 2020-07-02 11:52:17 INFO rasa.nlu.model - Starting to train component CountVectorsFeaturizer 2020-07-02 11:52:25 INFO rasa.nlu.model - Finished training component. 2020-07-02 11:52:25 INFO rasa.nlu.model - Starting to train component DIETClassifier /home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/nlu/classifiers/diet_classifier.py:589: 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 X_dense = np.array(X_dense) Traceback (most recent call last): File "/home/sreenijak/python-envs/rasaenv/bin/rasa", line 8, in sys.exit(main()) File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/main.py", line 92, in main cmdline_arguments.func(cmdline_arguments) File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/cli/train.py", line 76, in train additional_arguments=extract_additional_arguments(args), File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/train.py", line 50, in train additional_arguments=additional_arguments, File "uvloop/loop.pyx", line 1456, in uvloop.loop.Loop.run_until_complete File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/train.py", line 101, in train_async additional_arguments, File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/train.py", line 188, in _train_async_internal additional_arguments=additional_arguments, File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/train.py", line 245, in _do_training persist_nlu_training_data=persist_nlu_training_data, File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/train.py", line 482, in _train_nlu_with_validated_data persist_nlu_training_data=persist_nlu_training_data, File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/nlu/train.py", line 90, in train interpreter = trainer.train(training_data, kwargs) File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/nlu/model.py", line 191, in train updates = component.train(working_data, self.config, context) File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/nlu/classifiers/diet_classifier.py", line 688, in train model_data = self.preprocess_train_data(training_data) File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/nlu/classifiers/diet_classifier.py", line 669, in preprocess_train_data label_attribute=label_attribute, File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/nlu/classifiers/diet_classifier.py", line 599, in _create_model_data model_data.add_features(TEXT_FEATURES, [X_sparse, X_dense]) File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/utils/tensorflow/model_data.py", line 145, in add_features self.num_examples = self.number_of_examples() File "/home/sreenijak/python-envs/rasaenv/lib/python3.6/site-packages/rasa/utils/tensorflow/model_data.py", line 107, in number_of_examples f"Number of examples differs for keys '{data.keys()}'. Number of " ValueError: Number of examples differs for keys 'dict_keys(['text_features'])'. Number of examples should be the same for all data.

sara-tagger commented 4 years ago

Thanks for the issue, @rctatman will get back to you about it soon!

You may find help in the docs and the forum, too 🤗
saykent commented 4 years ago

Hi, I'm getting same issue on macos with python 3.7. File "/Users/endi/Projects/Endi/Rasa/rasa-demo/venv3/lib/python3.7/site-packages/rasa/utils/tensorflow/model_data.py", line 107, in number_of_examples f"Number of examples differs for keys '{data.keys()}'. Number of " ValueError: Number of examples differs for keys 'dict_keys(['text_features'])'. Number of examples should be the same for all data.

saykent commented 4 years ago

Try pip install rasa==1.9.7 rasa train It works for me.

The error is raised from def _create_model_data at diet_classifier.py

1.10.0 for example in training_data:

1.9.7 for e in training_data: if label_attribute is None or e.get(label_attribute):