Tensorflow Wrapping #12837
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by
rmitsch
mhyeonsoo
asked this question in
Help: Model Advice
Tensorflow Wrapping
#12837
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How to reproduce the behaviourI trained my own custom spacy ner model with the code below. import random
from spacy.util import minibatch, compounding
from pathlib import Path
from spacy.training import Example
# TRAINING THE MODEL
nlp_ner=spacy.load('en_core_web_sm')
with nlp_ner.disable_pipes(*unaffected_pipes):
# Training for 30 iterations
for iteration in range(30):
# shuufling examples before every iteration
random.shuffle(NER_TRAINING_DATA)
losses = {}
for batch in spacy.util.minibatch(NER_TRAINING_DATA, size=2):
for text, annotations in batch:
# create Example
doc = nlp_ner.make_doc(text)
example = Example.from_dict(doc, annotations)
# Update the model
nlp_ner.update([example], losses=losses, drop=0.3) I would liked to convert this model into tensorflow format, but couldn't understand or follow the guides. Your Environment
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Answered by
rmitsch
Jul 18, 2023
Replies: 2 comments
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Hey @mhyeonsoo, a conversion of spaCy/Thinc models to TF is not supported at the moment. |
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Answer selected by
rmitsch
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Actually, let me convert this into a discussion. |
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Hey @mhyeonsoo, a conversion of spaCy/Thinc models to TF is not supported at the moment.