Looked it up: python - zsh: no matches found: requests[security] - Stack Overflow
Try noglob pip install rasa[spacy] or pip install 'rasa[spacy]' (or full instead of spacy).
after applying:
pip install 'rasa[spacy]`
i did the train and got:
Training NLU model...
MissingDependencyException: Not all required importable packages are installed to use the configured NLU pipeline. To use this pipeline, you need to install the missing modules:
- spacy (needed for SpacyNLP)
Please install the packages that contain the missing modules.
pip install rasa[spacy]
not working
(venv) (base) avihaviv@apples-MacBook-Pro-4478 financial-demo % pip install rasa[spacy]
zsh: no matches found: rasa[spacy]
Be careful of the second quote here. It’s
pip install 'rasa[spacy]'
not
pip install 'rasa[spacy]`
Yes that’s what we tried before. I said
with noglob before pip.
Just try this:
noglob pip install rasa[spacy] spacy rasa
it seems to work good but when i train i get this:
Training NLU model...
MissingDependencyException: Not all required importable packages are installed to use the configured NLU pipeline. To use this pipeline, you need to install the missing modules:
- spacy (needed for SpacyNLP)
Please install the packages that contain the missing modules.
Bruh…
Can you do pip freeze and if spacy is present and what its version is?
venv) (base) avihaviv@apples-MacBook-Pro-4478 financial-demo % pip freeze
absl-py==0.13.0
aio-pika==6.8.0
aiofiles==0.7.0
aiohttp==3.7.4
aiormq==3.3.1
APScheduler==3.7.0
astunparse==1.6.3
async-generator==1.10
async-timeout==3.0.1
attrs==21.2.0
bidict==0.21.4
blis==0.7.5
boto3==1.20.12
botocore==1.23.12
CacheControl==0.12.10
cached-property==1.5.2
cachetools==4.2.4
catalogue==2.0.6
certifi==2021.10.8
cffi==1.15.0
chardet==3.0.4
charset-normalizer==2.0.7
clang==5.0
click==8.0.3
cloudpickle==1.6.0
colorclass==2.2.0
coloredlogs==15.0.1
colorhash==1.0.3
cryptography==3.4.8
cycler==0.11.0
cymem==2.0.6
dask==2021.7.2
decorator==5.1.0
dm-tree==0.1.6
dnspython==1.16.0
docopt==0.6.2
en-core-web-md @ https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.2.0/en_core_web_md-3.2.0-py3-none-any.whl
fbmessenger==6.0.0
filelock==3.4.0
fire==0.4.0
flatbuffers==1.12
fsspec==2021.11.0
future==0.18.2
gast==0.4.0
google-auth==2.3.3
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
greenlet==1.1.2
grpcio==1.42.0
h5py==3.1.0
httptools==0.3.0
humanfriendly==10.0
idna==2.10
importlib-metadata==4.8.2
jieba==0.42.1
Jinja2==3.0.3
jmespath==0.10.0
joblib==1.0.1
jsonpickle==2.0.0
jsonschema==3.2.0
kafka-python==2.0.2
keras==2.6.0
Keras-Preprocessing==1.1.2
kiwisolver==1.3.2
langcodes==3.3.0
locket==0.2.1
Markdown==3.3.6
MarkupSafe==2.0.1
matplotlib==3.3.4
mattermostwrapper==2.2
msgpack==1.0.2
multidict==5.2.0
murmurhash==1.0.6
networkx==2.6.3
numpy==1.19.5
oauthlib==3.1.1
opt-einsum==3.3.0
packaging==20.9
pamqp==2.3.0
partd==1.2.0
pathy==0.6.1
Pillow==8.4.0
preshed==3.0.6
prompt-toolkit==2.0.10
protobuf==3.19.1
psycopg2-binary==2.9.2
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycparser==2.21
pydantic==1.8.2
pydot==1.4.2
PyJWT==2.1.0
pykwalify==1.8.0
pymongo==3.10.1
pyparsing==3.0.6
pyrsistent==0.18.0
pyTelegramBotAPI==3.8.3
python-crfsuite==0.9.7
python-dateutil==2.8.2
python-engineio==4.3.0
python-socketio==5.5.0
pytz==2021.3
PyYAML==6.0
questionary==1.10.0
randomname==0.1.5
rasa==3.0.0
rasa-sdk==3.0.0
redis==3.5.3
regex==2021.8.28
requests==2.25.1
requests-oauthlib==1.3.0
requests-toolbelt==0.9.1
rocketchat-API==1.16.0
rsa==4.8
ruamel.yaml==0.16.13
ruamel.yaml.clib==0.2.6
s3transfer==0.5.0
sacremoses==0.0.46
sanic==21.9.3
Sanic-Cors==1.0.1
sanic-jwt==1.7.0
sanic-plugin-toolkit==1.2.0
sanic-routing==0.7.2
scikit-learn==0.24.2
scipy==1.7.2
sentencepiece==0.1.96
sentry-sdk==1.3.1
six==1.15.0
sklearn-crfsuite==0.3.6
slackclient==2.9.3
smart-open==5.2.1
spacy==3.2.0
spacy-legacy==3.0.8
spacy-loggers==1.0.1
SQLAlchemy==1.4.27
srsly==2.4.2
tabulate==0.8.9
tarsafe==0.0.3
tensorboard==2.7.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.6.1
tensorflow-addons==0.14.0
tensorflow-estimator==2.6.0
tensorflow-hub==0.12.0
tensorflow-probability==0.13.0
tensorflow-text==2.6.0
termcolor==1.1.0
terminaltables==3.1.0
thinc==8.0.13
threadpoolctl==3.0.0
tokenizers==0.7.0
toolz==0.11.2
tqdm==4.62.3
transformers==2.11.0
twilio==6.50.1
typeguard==2.13.2
typer==0.4.0
typing-extensions==3.7.4.3
typing-utils==0.1.0
tzlocal==2.1
ujson==4.3.0
urllib3==1.26.7
uvloop==0.14.0
wasabi==0.8.2
wcwidth==0.2.5
webexteamssdk==1.6
websockets==10.1
Werkzeug==2.0.2
wrapt==1.12.1
yarl==1.7.2
zipp==3.6.0
All’s good here…
Well, it’s not a solution, but can you do without SpacyNLP in your config?
agree, but was trying to avoid it to check the bot with spacyNLP it should work better, correct?
so just delete the line, or should I replace it with spacyNLP with spact?
mean spacy
Yes, but not that much by experience (if you have good training data at least)
Can you show me your pipeline/config to make sure?
version: "2.0"
language: en_core_web_md
pipeline:
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer
analyzer: "char_wb"
min_ngram: 1
max_ngram: 4
- name: DIETClassifier
epochs: 100
- name: FallbackClassifier
threshold: 0.7
- name: DucklingEntityExtractor
url: http://duckling.rasa.com:8000
dimensions:
- amount-of-money
- time
- number
- name: SpacyNLP
model: "en_core_web_md"
case_sensitive: false
- name: "SpacyEntityExtractor"
# Note: It is not possible to use the SpacyTokenizer + SpacyFeaturizer in
# combination with the WhitespaceTokenizer, and as a result the
# PERSON extraction by Spacy is not very robust.
# Because of this, the nlu training data is annotated as well, and the
# DIETClassifier will also extract PERSON entities .
dimensions: ["PERSON"]
- name: EntitySynonymMapper
policies:
- name: AugmentedMemoizationPolicy
- name: TEDPolicy
epochs: 40
- name: RulePolicy
core_fallback_threshold: 0.4
core_fallback_action_name: "action_default_fallback"
enable_fallback_prediction: True
Maybe you can use this meanwhile.
version: "2.0"
language: en_core_web_md
pipeline:
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer
analyzer: "char_wb"
min_ngram: 1
max_ngram: 4
- name: DIETClassifier
epochs: 100
- name: FallbackClassifier
threshold: 0.7
- name: DucklingEntityExtractor
url: http://duckling.rasa.com:8000
dimensions:
- amount-of-money
- time
- number
- name: EntitySynonymMapper
policies:
- name: AugmentedMemoizationPolicy
- name: TEDPolicy
epochs: 40
- name: RulePolicy
core_fallback_threshold: 0.4
core_fallback_action_name: "action_default_fallback"
enable_fallback_prediction: True
I don’t know who from Rasa Team I should tag for this, I’ll try pinging @koaning since he answered a similar question.
Alternatively, not sure if it would work, but try keeping your original pipeline but only change line 2 from
language: en_core_web_md
to
language: en
it didnt work…
but with your’s scripts it trained. so i can continue with the course at least.
1 Like
The language: en configuration in pipeline.yml is mainly used in pipeline components to throw an error if a language is not supported. For example, if you indicated the pipeline is for a Chinese assistant then the WhitespaceTokenizer would throw an error because this form of tokenisation does not work for a language with no whitespace.
It’s good practice to list a modern iso language abbreviation there, but it doesn’t influence the pipeline beyond throwing errors and en_core_web_md is not an iso standard. Rather, it’s a name that spaCy uses to refer to it’s medium model.
1 Like
In general, I’d advice folks to install everything via;
python -m pip install "rasa[spacy]"
python -m spacy download en_core_web_md
By using python -m there’s less confusion between virtual environments. If you’re interested in learning more about this phenomenon you may appreciate the small course that I’ve made here.
1 Like
Not Really, but U took your suggestion to apply the below and continue from there. so at least I could proceed with the course:
version: "2.0"
language: en_core_web_md
pipeline:
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer
analyzer: "char_wb"
min_ngram: 1
max_ngram: 4
- name: DIETClassifier
epochs: 100
- name: FallbackClassifier
threshold: 0.7
- name: DucklingEntityExtractor
url: http://duckling.rasa.com:8000
dimensions:
- amount-of-money
- time
- number
- name: EntitySynonymMapper
policies:
- name: AugmentedMemoizationPolicy
- name: TEDPolicy
epochs: 40
- name: RulePolicy
core_fallback_threshold: 0.4
core_fallback_action_name: "action_default_fallback"
enable_fallback_prediction: True
1 Like