Like the title says, the example on pages 46 and 47 throws an error if you are using spaCy 3.0 as described here.

To that end, I changed the code using the template provided here like this:

import spacy   
nlp = spacy.load('en_core_web_md')   
nlp.get_pipe("attribute_ruler").add([[{"TEXT": "Angeltown"}]], {"LEMMA": "Los Angeles"})
doc = nlp("I am flying to Angeltown.")
for token in doc: 
    print(token.text, token.lemma_)

and got the desired output.

Hi everyone I am copying from the book the code that takes the NY times article but I cannot get the book's output and also the code is not in chapter 03

from bs4 import BeautifulSoup
import requests
import spacy
def url_text(url_string):
res = requests.get(url_string)
html = res.text
soup = BeautifulSoup(html, 'html5lib')
for script in soup(["script", "style", 'aside']):
script.extract()
text = soup.get_text()
return " ".join(text.split())
ny_art = url_text("https://www.nytimes.com/2021/01/12/opinion/trump-america-allies.html")
nlp = spacy.load("en_core_web_md")
doc = nlp(ny_art)
len(doc.ents)
from collections import Counter
labels = [ent.label_ for ent in doc.ents]
Counter(labels)

Thank you

This is the error, I have solved this using three things.

1. instead of using model 'en' we can use the "en_core_web_md" model.
2. You will get another error like it take only 2 positions but you have given 3. for that just remove the none.
3. You will get another error like list of dictionary not needed, you can enclose the matcher pattern inside the two dimension array(like nested list)

Here's my code!

Most likely due to a spaCy upgrade to 3.0 the book code doesn't work, here's the better version. Corrections in comments.

doc   = nlp("i want to make a reservation for a flight")
dObj  = None
tVerb = None

#extract the direct object and its transitive verb
for token in doc:
    if token.dep_ == "dobj":
        dObj = token      
        tVerb= token.head 

# extract the helper verb
intentVerb = None

verbList = ["want","like","need","order"]
if tVerb.text in verbList:
    intentVerb = tVerb
else:
    if tVerb.head.dep_ == "ROOT":
        intentVerb = tVerb.head # <---------- intentVerb instead of helperVerb
        
#extract the object of the intent
intentObj = None
objList = ["flight","meal","booking"]
if dObj.text in objList:
    intentObj=dObj 
else:
    for child in tVerb.children: # <---------- this was dObj instead of tVerb
        if child.dep_ == "prep":
            intentObj= list(child.children)[0]
            break
        elif child.dep_ == "compound":
            intentObj = child
            break

print(intentVerb.text+intentObj.text.capitalize())

The graph now looks a little different, so need to look for the prep link from the root's children.
displacy.render(doc,style='dep') shows:

Hi Duygu, on page 116:

doc = nlp("I suffered from Trichotillomania when I was in college. The doctor prescribed me psychosomatic medicine.")
pattern = [{"LENGTH": {">=":10}}]
matcher.add("longWords", [pattern])
matches = matcher(doc)
for mid, start, end in matches:
    print(start, end, doc[start:end])

I'm getting

0 1 I
3 4 Trichotillomania
5 6 I
12 13 prescribed
14 15 psychosomatic

Having "prescribed" in there is fine (page 116 however says differently), yet finding "I" is sort of unexplainable to me. I'm on spaCy version 3.1.3. Is this a bug or a feature?

Greets, rudisoft

I have all the code with me, I just want to give a pull request but I can't

Hi Duygu, in the book mastering spaCy, page 46, chapter 2 we have the following code:

import spacy
from spacy.symbols import ORTH, LEMMA
nlp=spacy.load('en')
special_case = [{ORTH: "Angeltown", LEMMA: "Los Angeles"}]
nlp.tokenizer.add_special_case(u'Angeltown',special_case)
doc=nlp(u'I am flying to Angeltown')
for token in doc:
    print(token.text, token.lemma_)

trying to import the language model en by

python -m spacy download en

doesn't work:

As of spaCy v3.0, shortcuts like 'en' are deprecated. Please use the full pipeline package name 'en_core_web_sm' instead.

Not sure if

special_case = [{ORTH: "Angeltown", LEMMA: "Los Angeles"}]
nlp.tokenizer.add_special_case(u'Angeltown',special_case)

fails with Unable to set attribute 'LEMMA' in tokenizer exception for 'Angeltown'. Tokenizer exceptions are only allowed to specify ORTH and NORM because of that.

However this get's the job done:

import spacy
from spacy.symbols import ORTH, LEMMA, NORM
nlp=spacy.load('en_core_web_md')
special_case = [{ORTH: "Angeltown", NORM: "Los Angeles"}]
nlp.tokenizer.add_special_case(u'Angeltown',special_case)
doc=nlp(u'I am flying to Angeltown')
for token in doc:
    print(token.text, token.lemma_)

Thanks for the good book!

It seems that almost every chapter in the repository have missing code, is there any plans to update the repo with some notebooks with all the books's code?

Thank you, I'm loving the book.

Hi Duygu, it's me again 😅, this time on page 155. It's for others that may have the same issue:

python -m spacy init-model en en_subwords_wiki_lg --vectors-loc wiki-news-300d-1M-subword.vec.zip

gave me:
Error: No such command 'init-model'

it seems changed after spaCy 3. This however got the job done:
python3 -m spacy init vectors en -n en_subwords_wiki_lg wiki-news-300d-1M-subword.vec en_subwords_wiki_lg

Good to close right away.

Hi in case you want to upload the emoji drill here is the code

import spacy
from spacy.matcher import Matcher
from spacymoji import Emoji
nlp = spacy.load("en_core_web_md")

pos_emoji = ["😀", "😃", "😂", "😊", "😊", "😍"]  
neg_emoji = ["😞", "😠", "😩", "😢", "😭", "😒"]
pos_patterns = [[{"ORTH": emoji}] for emoji in pos_emoji]
neg_patterns = [[{"ORTH": emoji}] for emoji in neg_emoji]
matcher = Matcher(nlp.vocab)
matcher.add("posEmoji", pos_patterns)
matcher.add("negEmoji", neg_patterns)
doc = nlp(" I love Zara 😍")
for mid, start, end in matcher(doc):
      print(start, end, doc[start:end])

Cheers