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import spacy | |
import random | |
TRAIN_DATA = [('what is the price of polo?', {'entities': [(21, 25, 'PrdName')]}), | |
('what is the price of ball?', {'entities': [(21, 25, 'PrdName')]}), | |
('what is the price of jegging?', {'entities': [(21, 28, 'PrdName')]}), | |
('what is the price of t-shirt?', {'entities': [(21, 28, 'PrdName')]}), | |
('what is the price of jeans?', {'entities': [(21, 26, 'PrdName')]}), | |
('what is the price of bat?', {'entities': [(21, 24, 'PrdName')]}), | |
('what is the price of shirt?', {'entities': [(21, 26, 'PrdName')]}), | |
('what is the price of bag?', {'entities': [(21, 24, 'PrdName')]}), | |
('what is the price of cup?', {'entities': [(21, 24, 'PrdName')]}), | |
('what is the price of jug?', {'entities': [(21, 24, 'PrdName')]}), | |
('what is the price of plate?', {'entities': [(21, 26, 'PrdName')]}), | |
('what is the price of glass?', {'entities': [(21, 26, 'PrdName')]}), | |
('what is the price of moniter?', {'entities': [(21, 28, 'PrdName')]}), | |
('what is the price of desktop?', {'entities': [(21, 28, 'PrdName')]}), | |
('what is the price of bottle?', {'entities': [(21, 27, 'PrdName')]}), | |
('what is the price of mouse?', {'entities': [(21, 26, 'PrdName')]}), | |
('what is the price of keyboad?', {'entities': [(21, 28, 'PrdName')]}), | |
('what is the price of chair?', {'entities': [(21, 26, 'PrdName')]}), | |
('what is the price of table?', {'entities': [(21, 26, 'PrdName')]}), | |
('what is the price of watch?', {'entities': [(21, 26, 'PrdName')]})] | |
def train_spacy(data,iterations): | |
TRAIN_DATA = data | |
nlp = spacy.blank('en') # create blank Language class | |
# create the built-in pipeline components and add them to the pipeline | |
# nlp.create_pipe works for built-ins that are registered with spaCy | |
if 'ner' not in nlp.pipe_names: | |
ner = nlp.create_pipe('ner') | |
nlp.add_pipe(ner, last=True) | |
# add labels | |
for _, annotations in TRAIN_DATA: | |
for ent in annotations.get('entities'): | |
ner.add_label(ent[2]) | |
# get names of other pipes to disable them during training | |
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner'] | |
with nlp.disable_pipes(*other_pipes): # only train NER | |
optimizer = nlp.begin_training() | |
for itn in range(iterations): | |
print("Statring iteration " + str(itn)) | |
random.shuffle(TRAIN_DATA) | |
losses = {} | |
for text, annotations in TRAIN_DATA: | |
nlp.update( | |
[text], # batch of texts | |
[annotations], # batch of annotations | |
drop=0.2, # dropout - make it harder to memorise data | |
sgd=optimizer, # callable to update weights | |
losses=losses) | |
print(losses) | |
return nlp | |
prdnlp = train_spacy(TRAIN_DATA, 20) | |
# Save our trained Model | |
modelfile = input("Enter your Model Name: ") | |
prdnlp.to_disk(modelfile) | |
#Test your text | |
test_text = input("Enter your testing text: ") | |
doc = prdnlp(test_text) | |
for ent in doc.ents: | |
print(ent.text, ent.start_char, ent.end_char, ent.label_) |
i tried with another data set and it show me this error could you help me !
ValueError Traceback (most recent call last)
in ()
36
37
---> 38 prdnlp = train_spacy(TRAIN_DATA, 20)
39
40 # Save our trained Model
2 frames
/usr/local/lib/python3.7/dist-packages/spacy/language.py in _format_docs_and_golds(self, docs, golds)
470 err = Errors.E151.format(unexp=unexpected, exp=expected_keys)
471 raise ValueError(err)
--> 472 gold = GoldParse(doc, **gold)
473 doc_objs.append(doc)
474 gold_objs.append(gold)
gold.pyx in spacy.gold.GoldParse.init()
gold.pyx in spacy.gold.biluo_tags_from_offsets()
ValueError: too many values to unpack (expected 3)
Hi,
Can you send me the TRAIN_DATA?
I just trained and gave "I want cup and bat" in general it should give both "cup and bat" but is only giving first identified entity "cup"
Please train with more dataset and diff variant..
Hi,
Use this python script to convert correct TRAIN_DATA. The input of the this script is json file which is you downloaded from annotation tool.
You are using the format is:
('nom du client sami adresse client 75000 paris france\xa0', {'entities': [(34, 52, 'adresse_client', 1, 'rgb(167, 248, 246)'), (14, 18, 'nom_client', 0, 'rgb(45, 50, 168)')]})
Original format is:
('nom du client sami adresse client 75000 paris france\xa0', {'entities': [(34, 52, 'adresse_client', 1), (14, 18, 'nom_client', 0)]})
I just trained and gave "I want cup and bat" in general it should give both "cup and bat" but is only giving first identified entity "cup"