TypeError: add_label() takes exactly 2 positional arguments (3 given)

[Dan-Essin]

Hello,
I'm new to nlp and am working through a book with examples. I have reached a section called "Training our own POS-taggers".
There is a simple example that doesn't work for me. I'm still new enough at python that I cannot understand why it is complaining about the arguments to the tagger,add_label call. I can only see 2 but for some reason it things that there are 3. It would be really helpful if someone could explain this to me.

This is the code

import os
import gensim.models
import spacy
import spacy.lang.en.stop_words as STOP_WORDS
from gensim import corpora, models
import plac
import random
from pathlib import Path

TAG_MAP = {
'N': {'pos': 'NOUN'},
'V': {'pos': 'VERB'},
'J': {'pos': 'ADJ'}
}
TRAIN_DATA = [
("I like green eggs", {'tags': ['N', 'V', 'J', 'N']}),
("Eat blue ham", {'tags': ['V', 'J', 'N']})
]

@plac.annotations(
lang=("ISO Code of language to use", "option", "l", str),
output_dir=("Optional output directory", "option", "o", Path),
n_iter=("Number of training iterations", "option", "n", int))
def main(lang='en', output_dir=None, n_iter=25):
"""Main function to create a new model, set up the pipeline and train
the tagger. In order to train the tagger with a custom tag map,
we're creating a new Language instance with a custom vocab.
"""
nlp = spacy.blank(lang)
tagger = nlp.create_pipe('tagger')
print(TAG_MAP.items())
for tag, values in TAG_MAP.items():
print(tag)
print(values["pos"])
tagger.add_label(tag, values)

nlp.add_pipe(tagger)
optimizer = nlp.begin_training()
for i in range(n_iter):
    random.shuffle(TRAIN_DATA)
    losses = {}
    for text, annotations in TRAIN_DATA:
        nlp.update([text], [annotations], sgd=optimizer, losses=losses)
    print(losses)
test_text = "I like blue eggs"
doc = nlp(test_text)
print('Tags', [(t.text, t.tag_, t.pos_) for t in doc])

if output_dir is not None:
    output_dir = Path(output_dir)
    if not output_dir.exists():
        output_dir.mkdir()
    nlp.to_disk(output_dir)
    print("Saved model to", output_dir)

    # test the save model
    print("Loading from", output_dir)
    nlp2 = spacy.load(output_dir)
    doc = nlp2(test_text)
    print('Tags', [(t.text, t.tag_, t.pos_) for t in doc])

if name == 'main':
plac.call(main)

Expected output:

[

('I', 'N', 'NOUN'),

('like', 'V', 'VERB'),

('blue', 'J', 'ADJ'),

('eggs', 'N', 'NOUN')

]

It fails with the following output and traceback. :

dict_items([('N', {'pos': 'NOUN'}), ('V', {'pos': 'VERB'}), ('J', {'pos': 'ADJ'})])
N
NOUN

Traceback (most recent call last):
File "/home/essin/nlp/train/main.py", line 64, in
plac.call(main)

File "/home/essin/anaconda3/envs/nlp/lib/python3.9/site-packages/plac_core.py", line 348, in call
cmd, result = parser.consume(arglist)

File "/home/essin/anaconda3/envs/nlp/lib/python3.9/site-packages/plac_core.py", line 217, in consume
return cmd, self.func(*(args + varargs + extraopts), **kwargs)

File "/home/essin/nlp/train/main.py", line 36, in main
tagger.add_label(tag, values)

File "spacy/pipeline/tagger.pyx", line 276, in spacy.pipeline.tagger.Tagger.add_label
TypeError: add_label() takes exactly 2 positional arguments (3 given)

[ljvmiranda921]

Hi @Dan-Essin ! Will definitely appreciate it if you format the code block properly so that it's easier to debug. 🙇

It seems like you're following a book that uses the v2 API but the spaCy version you have in your computer is already v3. The function parameters have changed during that version bump. To confirm which spaCy version you have, try running the following:

python -m spacy info

You have two options here:

• Install a v2 version of spaCy and rerun the code: find out what spaCy version the book follows and install that instead
• Port your code to the v3 version: the Tagger API has changed, add_label doesn't accept a values param anymore. You can check the migration doc here, and follow this discussion here

---

Lastly, the reason why it says 3 arguments instead of 2 is because Python counts self when reporting the error. Because the add_label function belongs to an instantiated class, its signature looks like this:

def add_label(self, label):
    # some code

So it just expects 2. However, we are passing three arguments in the code: self (always happen whenever you instantiate, label, and the tag map.