For the Chinese model loading, how can I load all the models while still be able to set the pkuseg and jieba settings?
nlp = Chinese() # Disable jieba through tokenizer config options
cfg = {"use_jieba": False}
nlp = Chinese(meta={"tokenizer": {"config": cfg}})
The 'nlp' created by Chinese() doesn't have other models besides segmentation models. This can only load the segmenter models. If I do this to get the 'nlp' object:
nlp = spacy.load('zh_core_web_sm')
This loads all the models. However, how can I control the pkuseg or jieba parameters in this load function?
You don't want to modify the segmentation setup in the loaded model.
It's technically possible to switch the loaded model from pkuseg to jieba, but if you do that, the model components will perform terribly because they've only been trained on pkuseg segmentation.
Related
so im facing a problem about deployment my custom sign-language recognition model. I converted my_ssd_mobnet with exporter_main_v2.py to saved_model.pb and then i tried to use the tensorflowjs convertor with this code:
from tensorflow import keras
import tensorflowjs as tfjs
def importModel(modelPath):
model = tf.keras.models.load_model(modelPath)
tfjs.converters.save_tf_model(model, "tfjsmodel")
importModel("saved_model")
#importModel("modelDirectory")
then i got an error like this..
ValueError: Unable to create a Keras model from this SavedModel. This SavedModel was created with tf.saved_model.save, and lacks the Keras metadata.Please save your Keras model by calling model.saveor tf.keras.models.save_model.
Finally i decide to convert my model to h5, but.. i don't know how.
How can i convert my_ssd_mobnet model to h5?
Thanks!
If you're creating a custom Keras layer in python and wanting to export it to tfjs for the browser to predict, then you'll most likely encounter "Unknown layer" and will have to implement them yourself in JS.
Instead of exporting the layers, it's best to export a graph since you're only using it for prediction and not training in the browser.
tf.saved_model.save(model, 'saved_model')
This will save the files in the saved_model folder and contains the .pb file.
Use the tensorflowjs_converter tool to convert the model into a graph tfjs model.
tensorflow_converter --input_format=tf_saved_model saved_model model
This will convert your saved model into the browser-compatible tfjs model without the custom layer. (The Keras layers will be built in.)
Move this folder to your website's public folder.
In the browser:
const model = await tf.loadGraphModel('/model/model.json')
const img = tf.browser.fromPixels(imageData, 3) // imageElement, videoElement, ImageData
.toFloat().resizeBilinear([224, 224]) // mobilenet dims
.div(tf.scalar(255)) // mobilenet [0,1] normalization
.expandDims()
const { values, indices } = model.predict(img).topk()
const label = indices.dataSync()[0]
const confidence = values.dataSync()[0]
NOTE: The .bin files will end up in the 10's of MB so put this inside a webworker. You can send a buffered data from the main thread to the worker thread for processing.
First and foremost, if you have used "exporter_main_v2.py" script to export the model, you will only get the model format in tensorflow model. This way of exporting is mainly used to make inference on the trained model. So the main problem in your code is that you are trying to import a "keras model" with that tf.keras.models.load_model() function. Instead of using "exporter_main_v2.py" you have to use tf.keras.models.save_model() function to export/save your model.
I am also giving you a simple video explanation link to clarify a few things for you
https://www.youtube.com/watch?v=Lx7OCFXPG8o
After watching the video you might want to checkout the following colab notebook
https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l07c01_saving_and_loading_models.ipynb
This is a material provided by Udacity from its introduction to tensorflow training course. That should be very helpful in your case to understand the difference between tensorflow model file and keras model file.
Have a nice day.
Edit:
HDF5 format
Keras provides a basic save format using the HDF5 standard.
Create and train a new model instance.
model = create_model()
model.fit(train_images, train_labels, epochs=5)
Save the entire model to a HDF5 file.
The '.h5' extension indicates that the model should be saved to HDF5.
model.save('my_model.h5')
You should add '.h5' extension to filename when calling model.save function, by this way the model will be saved in h5 format.
Let's say that I want to include distilbert https://huggingface.co/distilbert-base-uncased from Hugging Face into spaCy 3.0 pipeline. I think that this is possible and I found some code on how to convert this model for spaCy 2.0 but it doesn't work in v3.0. What I really want is to load this model using something like this
nlp = spacy.load('path_to_distilbert')
Is it even possible and could you please provide the exact steps to do that.
You can use spacy-transformers to this end. In spaCy v3, you can train custom pipelines using a config file, where you would define the transformer component using any HF model you like in components.transformer.model.name:
[components.transformer]
factory = "transformer"
max_batch_items = 4096
[components.transformer.model]
#architectures = "spacy-transformers.TransformerModel.v1"
name = "bert-base-cased"
tokenizer_config = {"use_fast": true}
[components.transformer.model.get_spans]
#span_getters = "spacy-transformers.doc_spans.v1"
[components.transformer.set_extra_annotations]
#annotation_setters = "spacy-transformers.null_annotation_setter.v1"
You can then train any other component (NER, textcat, ...) on top of this pretrained transformer model, and the transformer weights will be further finetuned, too.
You can read more about this in the docs here: https://spacy.io/usage/embeddings-transformers#transformers-training
It appears that the only transformer that will work out of the box is their roberta-base model. In the docs it mentions being able to connect thousands of Huggingface models but there is no mention of how to add them to a SpaCy pipeline.
In the meantime if you wanted to use the roberta model you can do the following.
# install using spacy transformers
pip install spacy[transformers]
python -m spacy download en_core_web_trf
import spacy
nlp = spacy.load("en_core_web_trf")
Currently I am trying to develop a spaCy model for NER in the romanian legal domain. I was suggested to use specific WE that are presented at the following link (the links to download the WE are on the last pages - slides 25, 26, 27):
https://www1.ids-mannheim.de/fileadmin/kl/CoRoLa_based_Word_Embeddings.pdf
I already trained and tested a model without "touching" the pre-implemented WE but I do not know how to use external WE in computing a new spaCy model. Any relevant advice is appreciated. Although, an example of code will be preferable.
Yes, convert your vectors from word2vec text format with spacy init vectors and then specify that model as [initialize.vectors] in your config along with include_static_vectors = true for the relevant tok2vec models.
A config excerpt:
[components.tok2vec.model.embed]
#architectures = "spacy.MultiHashEmbed.v1"
width = ${components.tok2vec.model.encode.width}
attrs = ["ORTH", "SHAPE"]
rows = [5000, 2500]
include_static_vectors = true
[initialize]
vectors = "my_vector_model"
You can also use spacy init config -o accuracy config.cfg to generate a sample config including vectors that you can edit and adjust as you need.
See:
https://spacy.io/api/cli#init-vectors
https://spacy.io/usage/embeddings-transformers#static-vectors
I'm using Spacy large model but it's incorrectly tagging entities with categories that are not relevant to my domain, eg 'work of art' can cause it not to recognise what should have been an Org.
Is it possible to restrict NER to only return People, Locations and Organisations ?
Short answer:
No, you cannot restrict NER to not tag specific Tags or the opposite.
What you can do is limit it in code or modify the model [see long answer].
Limiting it in code is just filtering the retrieved entities, but it won't solve your problem with missclassifications.
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")
entities = [ent for ent in doc.ents if ent.label_ == "ORG"]
Long answer:
You can restrict NER in spacy, but not with a simple parameter (currently).
Why not? Simple: NER is a supervised machine learning task. You provide text with tagged entities, it trains and then attempts to predict new instances from the parameters it learned beforehand.
If you want NER only to recognize certain entities, such as orgs, you have to train a new model only with org instances.
If you're familiar with Machine Learning concepts, you'll understand it this way: in a multi class classification task, you cannot simply remove a class without retraining the entire model with filtered train data.
Check this page for more info on NER training: https://spacy.io/usage/linguistic-features/#named-entities
i got a model converted from caffe by using MMDNN tool, it converted the caffe model into a saved_model tensorflow style. it's a resnet18 model, and i just strip out several layers in the last, i wish i could load this architecture in the model_fn in a tf.estimator, and manually add some extra layers to do my job.
As the tutorial recommended that I could use loader.load method to load the saved_model. But i just want to use it in a estimator, and i need to define the architecture in the model_fn function. I searched out the SO and github but there isn't a very specific workflow to do that thing, somebody could help me out?
Here is one way of fine tuning using tf.Estimator:
Define your model using the SAME variable names/scopes as in your saved model
Use tf.estimator's warm start functions to initialize your new model with the saved weights. Here is a code snippet :
if fine_tuning:
ws = tf.estimator.WarmStartSettings(ckpt_to_initialize_from=path_saved_model,
vars_to_warm_start='.*')
else:
ws = None
estimator = tf.estimator.Estimator(model_fn=model_function,
warm_start_from=ws,
...
)
This will initialize any variable that share names between your currently defined graph and the saved model.