I am deploying an app that specifically requires spaCy==3.3.1 to Streamlit cloud, which I added to the requirement.txt as well as the link to download and install en_core_web_sm which is
https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz#egg=en_core_web_sm.
The import is okay but when I load the model i.e. nlp = spacy.load("en_core_web_sm"), I will get the OSError below:
OSError: [E053] Could not read config file from /home/appuser/venv/lib/python3.9/site-packages/en_core_web_sm/en_core_web_sm-2.2.0/config.cfg
What am I doing wrong?
Thanks in advance for your help.
My requirements.txt:
spacy==3.3.1
https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz#egg=en_core_web_sm
Main code:
import en_core_web_sm
nlp = spacy.load("en_core_web_sm")
I expected no error but got the OSError: [E053] above.
Packages trained with spaCy v2.x are not compatible with spaCy v3.x
(source)
Note that the model has version 2.2.0 but spacy has version 3.3.1.
I'd suggest to use a newer version of the model. The requirements could look like:
spacy==3.3.1
en-core-web-sm # https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0-py3-none-any.whl
Related
I am trying to save a model with tensorflow-agents. First I define the following:
collect_policy = tf_agent.collect_policy
saver = PolicySaver(collect_policy, batch_size=None)
and then save the model like this:
saver.save('my_directory/')
This works ok in google colab but I am getting the following error in my local PC.
AttributeError: module 'tensorflow.python.saved_model.nested_structure_coder' has no attribute 'StructureCoder'
These are the library versions I am using:
tensorflow 2.9.1
tf-agents 0.11.0
Tl;dr
Make sure you have the right tensorflow-probability version that is compatible for 2.9.x and tf-agents 0.11.0
pip uninstall tensorflow-probability
pip install tensorflow-probability==0.17.0
(0.19.0 for TF 2.11, 0.18.0 for TF 2.10 or look at the release notes)
Also make sure to restart your kernel from the notebook.
What the problem was
StructureCoder has been moved to tensorflow API. Therefore, other dependent libraries have made changes like this in tf-agent and like this in tensorflow-probability. Your machine is somehow picking up an older version that depends on the previous version of nested_structure_coder.
For me, I was using
tensorflow 2.9.0
tf-agents 0.13.0
tensorflow-probabilities 0.17.0
Try making an explicit import in your notebook:
import tensorflow_probability
print(tensorflow_probability.__version__) # was 0.17.0 for me
I'm trying to run spaCY's lemmatizer on a text by running the command nlp = spacy.load("en_core_web_sm", disable=["parser", "ner"]), but then I get the following error:
OSError: [E053] Could not read config.cfg from C:\Users.
I'm using spaCy version 3.2.1, and I also installed en-core-web-sm-2.2.0.
I also get this warning, but I'm not sure what it means:
UserWarning: [W094] Model 'en_core_web_sm' (2.2.0) specifies an under-constrained spaCy version requirement: >=2.2.0. This can lead to compatibility problems with older versions, or as new spaCy versions are released, because the model may say it's compatible when it's not. Consider changing the "spacy_version" in your meta.json to a version range, with a lower and upper pin. For example: >=3.2.1,<3.3.0.
Hope someone can help me.
A v2 spaCy model won't work with spaCy v3 - you need to update your model. Do this:
spacy download en_core_web_sm
The error could be easier to understand, but it's not a situation that comes up much - usually you'd have to upgrade from spaCy v2 to v3 but not upgrade your models for that to happen. Not sure how you got in that state.
I encountered a weird problem in Spacy and I am not sure whether I am doing something wrong or it is a genuine bug.
I use Spacy project and create a default config file via:
python -m spacy init config spacy.cfg
Then I try to load an NLP object using this config:
import spacy
config = spacy.util.load_config('./spacy.cfg')
nlp = spacy.load("en_core_web_sm", config=config)
When do this I get the following error:
ConfigValidationError:
Error parsing config overrides
nlp -> tokenizer -> #tokenizers not a section value that can be overwritten
By looking at what is going on inside through PDB I noticed that section nlp.tokenizer is not created. Instead the config stores the following ugly item within the NLP section:
'tokenizer': '{"#tokenizers":"spacy.Tokenizer.v1"}'
which does not seem to look allright.
I am using Spacy v3.0.3 on Ubuntu 20.04.2 LTS.
There's nothing wrong with that tokenizer setting. en_core_web_sm already contains its own config including a tokenizer, which is what you can't override.
You want to load your config starting from a blank pipeline instead of a pretrained pipeline:
nlp = spacy.blank("en", config=config)
Be aware that the language en needs to match here with the spacy init config language setting or it won't be able to load the config.
I want to use FaceNet as a embedding layer (which won't be trainable).
I tried loading FaceNet like so :
tf.keras.models.load_model('./path/tf_facenet')
where directory ./path/tf_facenet contains 4 files that can be downloaded at https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk/edit
but a message error shows up :
OSError: SavedModel file does not exist at: ./path/tf_facenet/{saved_model.pbtxt|saved_model.pb}
And the h5 files downloaded from https://github.com/nyoki-mtl/keras-facenet doesn't seem to work either (they use tensorflow 1.3)
I had issued like you when load model facenet-keras. Maybe you python env missing h5py modules.
So you should install that conda install h5py
Hope you success!!!
I am trying to convert .onxx model to .pb model. I have written the code but i am getting error:
#tf_func(tf.ceil)AttributeError: module 'tensorflow' has no attribute 'ceil'
Code:
import onnx
from tensorflow.python.tools.import_pb_to_tensorboard import import_to_tensorboard
from onnx_tf.backend import prepare
onnx_model = onnx.load("original_3dlm.onnx")
tf_rep = prepare(onnx_model)
tf_rep.export_graph("model_var.pb")
import_to_tensorboard("model_var.pb", "tb_log")
How to resolve this issue? Is there any other way to convert Onxx to Tensorflow?
I solve this issue with this.
Tensorflow Backend for ONNX.
Let me know if you have any issue.
Change from tensorflow 2.0 to 1.14.Maybe solve the problem.
your code as far as I can tell should be fine. The problem probably lies in the onnx-tf version you currently use. pip currently installs a version that only supports TensorFlow <= 1.15.
run this in the terminal to install a more up-to-date version of onnx-tf.
pip uninstall onnx_tf
pip install git+https://github.com/onnx/onnx-tensorflow.git
refer to this issue for further details