spaCy
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explosion
💫 Industrial-strength Natural Language Processing (NLP) in Python
## How to reproduce the behaviour EntityRuler is run as in example from docs here https://spacy.io/usage/rule-based-matching#entityruler-ent-ids Suppose someone has a series of pipeline components that run after some entities are...
## Description This will add three visualisations of information from parsed documents: - a table with rows for consecutive tokens in the document; columns are feature values and/or dependency trees...
## The problem I am training a sentence classification model using a transformer and a pipeline that is based on the default config. I am doing it on the custom...
## How to reproduce the behaviour import spacy nlp = spacy.load('de') s1 = 'Der schöne Garten' doc = nlp(s1) [(t, t.lemma_) for t in doc] >> [(Der, 'der'), (schöne, 'schöne'),...
## How to reproduce the behaviour I'm trying to train a text classifier and at the first try I always got `OverflowError: Invalid Nan value when encoding double`. Turns out...
## How to reproduce the behaviour ``` cfg = {"model": DEFAULT_NER_MODEL} model = registry.resolve(cfg, validate=True)["model"] ner = EntityRecognizer(nlp.vocab, model) ner.initialize(lambda: [], nlp=nlp) ``` The error I get: ``` TypeError: [E930]...
EntityRecognizer throws IndexError when used in pipeline with Transformer and custom span getter during training: ``` File "/home/---/---/research_spacy_ru/.venv/lib/python3.8/site-packages/spacy/language.py", line 1122, in update proc.update(examples, sgd=None, losses=losses, **component_cfg[name]) File "spacy/pipeline/transition_parser.pyx", line 416,...
## Problem I am working with a two-level NER taxonomy, where I store the first one in `Span.label_` attribute, and the second one in an extension `Span._.type`. I have annotations...
I'm trying to perform relationship extraction between named entities where the named entities span multiple tokens. I've chosen not to merge the entities as that screws up the dependency parsing....
Version: spaCy 3.2. The lemma for the word "substantially" is "you" for some reason: ``` >>> import spacy >>> nlp = spacy.load("en_core_web_lg") >>> doc=nlp("The opportunity may not be huge for...
