def __init__(self, nlp, companies=tuple(), label="<STR_LIT>"):
self.label = nlp.vocab.strings[label]  <EOL>patterns = [nlp(org) for org in companies]<EOL>self.matcher = PhraseMatcher(nlp.vocab)<EOL>self.matcher.add("<STR_LIT>", None, *patterns)<EOL>Token.set_extension("<STR_LIT>", default=False)<EOL>Doc.set_extension("<STR_LIT>", getter=self.has_tech_org)<EOL>Span.set_extension("<STR_LIT>", getter=self.has_tech_org)<EOL>
Initialise the pipeline component. The shared nlp instance is used
        to initialise the matcher with the shared vocab, get the label ID and
        generate Doc objects as phrase match patterns.