Training spaCy – NameError
Question:
I need to train a spaCy model to improve the accuracy to identify products. I’m struggling with training my spacy model. I have the following code:
TRAIN_DATA = [('..., {'entities': [(36,55,'PRODUCT')]})]
nlp = spacy.load("en_core_web_lg")
ner = nlp.get_pipe("ner")
optimizer = nlp.create_optimizer()
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
with nlp.disable_pipes(*other_pipes): # only train NER
for itn in range(50):
random.shuffle(TRAIN_DATA)
losses = {}
for text, annotations in TRAIN_DATA:
doc = nlp.make_doc(text)
example = Example.from_dict(doc, annotations)
nlp.update([example], drop=0.25, sgd=optimizer, losses=losses)
but it’s failing due to:
NameError Traceback (most recent call last)
<ipython-input-4-903f2be7114f> in <module>
15 for text, annotations in TRAIN_DATA:
16 doc = nlp.make_doc(text)
---> 17 example = Example.from_dict(doc, annotations)
18 nlp.update([example], drop=0.25, sgd=optimizer, losses=losses)
19 print(losses)
NameError: name 'Example' is not defined
How do I need to define Example?
Answers:
It’s hot here…
thanks for the hint I missed importing:
from spacy.training import Example
when moving the code from Jupyter to Visual Studio Code for the deployment
# 4] Format of the training examples
# training data
TRAIN_DATA = [
("Walmart is a leading e-commerce company", {"entities": [(0, 7, "ORG")]}),
("I reached Chennai yesterday.", {"entities": [(19, 28, "GPE")]}),
("I recently ordered a book from Amazon", {"entities": [(24,32, "ORG")]}),
("I was driving a BMW", {"entities": [(16,19, "PRODUCT")]}),
("I ordered this from ShopClues", {"entities": [(20,29, "ORG")]}),
("Fridge can be ordered in Amazon ", {"entities": [(0,6, "PRODUCT")]}),
("I bought a new Washer", {"entities": [(16,22, "PRODUCT")]}),
("I bought a old table", {"entities": [(16,21, "PRODUCT")]}),
("I bought a fancy dress", {"entities": [(18,23, "PRODUCT")]}),
("I rented a camera", {"entities": [(12,18, "PRODUCT")]}),
("I rented a tent for our trip", {"entities": [(12,16, "PRODUCT")]}),
("I rented a screwdriver from our neighbour", {"entities": [(12,22, "PRODUCT")]}),
("I repaired my computer", {"entities": [(15,23, "PRODUCT")]}),
("I got my clock fixed", {"entities": [(16,21, "PRODUCT")]}),
("I got my truck fixed", {"entities": [(16,21, "PRODUCT")]}),
("Flipkart started it's journey from zero", {"entities": [(0,8, "ORG")]}),
("I recently ordered from Max", {"entities": [(24,27, "ORG")]}),
("Flipkart is recognized as leader in market",{"entities": [(0,8, "ORG")]}),
("I recently ordered from Swiggy", {"entities": [(24,29, "ORG")]})
]
# Adding labels to the `ner`
for _, annotations in TRAIN_DATA:
for ent in annotations.get("entities"):
ner.add_label(ent[2])
# =======================># 5] Training the NER model
# Disable pipeline components you dont need to change
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
unaffected_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
# Import requirements
import random
from spacy.util import minibatch, compounding
from pathlib import Path
# TRAINING THE MODEL
#from spacy.training import Example
#with nlp.disable_pipes(*unaffected_pipes):
# =======================
nlp = spacy.blank('en')
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe('ner', last=True)
else:
ner = nlp.get_pipe('ner')
for _, annotations in TRAIN_DATA:
for label in annotations['entities']:
ner.add_label(label[2])
# TRAINING THE MODEL
from spacy.training import Example
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes):
optimizer = nlp.begin_training()
for epoch in range(30):
random.shuffle(TRAIN_DATA)
losses = {}
print(f'Epoch {epoch+1} of {30}:')
for text, annotations in TRAIN_DATA:
doc = nlp.make_doc(text)
example = Example.from_dict(doc, annotations)
nlp.update([example], drop=0.2, sgd=optimizer, losses=losses) #SGD
print(losses) #Print losses after each epoch
I need to train a spaCy model to improve the accuracy to identify products. I’m struggling with training my spacy model. I have the following code:
TRAIN_DATA = [('..., {'entities': [(36,55,'PRODUCT')]})]
nlp = spacy.load("en_core_web_lg")
ner = nlp.get_pipe("ner")
optimizer = nlp.create_optimizer()
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
with nlp.disable_pipes(*other_pipes): # only train NER
for itn in range(50):
random.shuffle(TRAIN_DATA)
losses = {}
for text, annotations in TRAIN_DATA:
doc = nlp.make_doc(text)
example = Example.from_dict(doc, annotations)
nlp.update([example], drop=0.25, sgd=optimizer, losses=losses)
but it’s failing due to:
NameError Traceback (most recent call last)
<ipython-input-4-903f2be7114f> in <module>
15 for text, annotations in TRAIN_DATA:
16 doc = nlp.make_doc(text)
---> 17 example = Example.from_dict(doc, annotations)
18 nlp.update([example], drop=0.25, sgd=optimizer, losses=losses)
19 print(losses)
NameError: name 'Example' is not defined
How do I need to define Example?
It’s hot here…
thanks for the hint I missed importing:
from spacy.training import Example
when moving the code from Jupyter to Visual Studio Code for the deployment
# 4] Format of the training examples
# training data
TRAIN_DATA = [
("Walmart is a leading e-commerce company", {"entities": [(0, 7, "ORG")]}),
("I reached Chennai yesterday.", {"entities": [(19, 28, "GPE")]}),
("I recently ordered a book from Amazon", {"entities": [(24,32, "ORG")]}),
("I was driving a BMW", {"entities": [(16,19, "PRODUCT")]}),
("I ordered this from ShopClues", {"entities": [(20,29, "ORG")]}),
("Fridge can be ordered in Amazon ", {"entities": [(0,6, "PRODUCT")]}),
("I bought a new Washer", {"entities": [(16,22, "PRODUCT")]}),
("I bought a old table", {"entities": [(16,21, "PRODUCT")]}),
("I bought a fancy dress", {"entities": [(18,23, "PRODUCT")]}),
("I rented a camera", {"entities": [(12,18, "PRODUCT")]}),
("I rented a tent for our trip", {"entities": [(12,16, "PRODUCT")]}),
("I rented a screwdriver from our neighbour", {"entities": [(12,22, "PRODUCT")]}),
("I repaired my computer", {"entities": [(15,23, "PRODUCT")]}),
("I got my clock fixed", {"entities": [(16,21, "PRODUCT")]}),
("I got my truck fixed", {"entities": [(16,21, "PRODUCT")]}),
("Flipkart started it's journey from zero", {"entities": [(0,8, "ORG")]}),
("I recently ordered from Max", {"entities": [(24,27, "ORG")]}),
("Flipkart is recognized as leader in market",{"entities": [(0,8, "ORG")]}),
("I recently ordered from Swiggy", {"entities": [(24,29, "ORG")]})
]
# Adding labels to the `ner`
for _, annotations in TRAIN_DATA:
for ent in annotations.get("entities"):
ner.add_label(ent[2])
# =======================># 5] Training the NER model
# Disable pipeline components you dont need to change
pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
unaffected_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
# Import requirements
import random
from spacy.util import minibatch, compounding
from pathlib import Path
# TRAINING THE MODEL
#from spacy.training import Example
#with nlp.disable_pipes(*unaffected_pipes):
# =======================
nlp = spacy.blank('en')
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe('ner', last=True)
else:
ner = nlp.get_pipe('ner')
for _, annotations in TRAIN_DATA:
for label in annotations['entities']:
ner.add_label(label[2])
# TRAINING THE MODEL
from spacy.training import Example
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes):
optimizer = nlp.begin_training()
for epoch in range(30):
random.shuffle(TRAIN_DATA)
losses = {}
print(f'Epoch {epoch+1} of {30}:')
for text, annotations in TRAIN_DATA:
doc = nlp.make_doc(text)
example = Example.from_dict(doc, annotations)
nlp.update([example], drop=0.2, sgd=optimizer, losses=losses) #SGD
print(losses) #Print losses after each epoch