Hello, I have created a AWS server, and connected to Rasa X. From here I connected my assistant to it.
The problem is, that when I talk to my bot, I can see that it says that the particular spacy model which I use is not found.
I don’t know if the problem is a spacy or Rasa problem, so I have created a discussion on spacys’ github aswell.
Update: I fixed the error by changing the language to english and back to danish, but the error returns if restart my server
Traceback (most recent call last): File "/opt/venv/lib/python3.8/site-packages/rasa/core/agent.py", line 161, in _update_model_from_server _load_and_set_updated_model(agent, model_directory, new_fingerprint) File "/opt/venv/lib/python3.8/site-packages/rasa/core/agent.py", line 134, in _load_and_set_updated_model interpreter = _load_interpreter(agent, nlu_path) File "/opt/venv/lib/python3.8/site-packages/rasa/core/agent.py", line 93, in _load_interpreter return rasa.core.interpreter.create_interpreter(nlu_path) File "/opt/venv/lib/python3.8/site-packages/rasa/core/interpreter.py", line 33, in create_interpreter return RasaNLUInterpreter(model_directory=obj) File "/opt/venv/lib/python3.8/site-packages/rasa/core/interpreter.py", line 127, in __init__ self._load_interpreter() File "/opt/venv/lib/python3.8/site-packages/rasa/core/interpreter.py", line 165, in _load_interpreter self.interpreter = Interpreter.load(self.model_directory) File "/opt/venv/lib/python3.8/site-packages/rasa/nlu/model.py", line 331, in load return Interpreter.create( File "/opt/venv/lib/python3.8/site-packages/rasa/nlu/model.py", line 405, in create component = component_builder.load_component( File "/opt/venv/lib/python3.8/site-packages/rasa/nlu/components.py", line 824, in load_component component = registry.load_component_by_meta( File "/opt/venv/lib/python3.8/site-packages/rasa/nlu/registry.py", line 177, in load_component_by_meta return component_class.load( File "/opt/venv/lib/python3.8/site-packages/rasa/nlu/utils/spacy_utils.py", line 309, in load nlp = cls.load_model(model_name) File "/opt/venv/lib/python3.8/site-packages/rasa/nlu/utils/spacy_utils.py", line 52, in load_model raise InvalidModelError( rasa.nlu.model.InvalidModelError: Please confirm that da_core_news_md is an available spaCy model. You need to download one upfront. For example: python -m spacy download en_core_web_md More informaton can be found on https://rasa.com/docs/rasa/components#spacynlp
This problem is also occuring when I add an intent. The assisant will only recognise the new intent if I change language → train it → upload it → change the language back to the original language → and upload again.
I didnt specifically download my model - I just connected it through github. Locally I ran the python -m spacy download command. I dont link it, due to the warning message saying it will be depricated, but should I link the model?
> Configuration for Rasa NLU.
> https://rasa.com/docs/rasa/nlu/components/
> language: da
>
>
> No configuration for the NLU pipeline was provided. The following default pipeline was used to train your model.
> If you'd like to customize it, uncomment and adjust the pipeline.
> See https://rasa.com/docs/rasa/tuning-your-model for more information.
> pipeline:
> - name: SpacyNLP
> model: da_core_news_md
> - name: SpacyTokenizer
> - name: SpacyEntityExtractor
> - name: SpacyFeaturizer
> pooling: mean
> - name: CountVectorsFeaturizer
> analyzer: char_wb
> min_ngram: 1
> max_ngram: 2
> - name: DIETClassifier
> - name: FallbackClassifier
> threshold: 0.8
> ambiguity_threshold: 0.1
> epochs: 1
>
>
> Configuration for Rasa Core.
> https://rasa.com/docs/rasa/core/policies/
> policies:
> No configuration for policies was provided. The following default policies were used to train your model.
> If you'd like to customize them, uncomment and adjust the policies.
> See https://rasa.com/docs/rasa/policies for more information.
> - name: "AugmentedMemoizationPolicy"
> max_history: 10
> - name: TEDPolicy
> max_history: 5
> epochs: 100
> constrain_similarities: true
> - name: RulePolicy
> core_fallback_threshold: 0.4
> core_fallback_action_name: "action_default_fallback"
> enable_fallback_prediction: True