Rooba
/
spaCy
mirror of https://github.com/explosion/spaCy.git
1
1
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Docs: clarify abstract spacy.load examples (#12889)

This commit is contained in:
Adriane Boyd 2023-08-16 17:28:34 +02:00 committed by GitHub
parent 64b8ee2dbe
commit 76a9f9c6c6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
website/docs/api/top-level.mdx
View File

@ -68,7 +68,7 @@ weights, and returns it.
cls = spacy.util.get_lang_class(lang) # 1. Get Language class, e.g. English cls = spacy.util.get_lang_class(lang) # 1. Get Language class, e.g. English
nlp = cls() # 2. Initialize it nlp = cls() # 2. Initialize it
for name in pipeline: for name in pipeline:
nlp.add_pipe(name) # 3. Add the component to the pipeline nlp.add_pipe(name, config={...}) # 3. Add the component to the pipeline
nlp.from_disk(data_path) # 4. Load in the binary data nlp.from_disk(data_path) # 4. Load in the binary data
``` ```

4
website/docs/usage/processing-pipelines.mdx
View File

@ -244,7 +244,7 @@ tagging pipeline. This is also why the pipeline state is always held by the
together and returns an instance of `Language` with a pipeline set and access to together and returns an instance of `Language` with a pipeline set and access to
the binary data: the binary data:
```python {title="spacy.load under the hood"} ```python {title="spacy.load under the hood (abstract example)"}
lang = "en" lang = "en"
pipeline = ["tok2vec", "tagger", "parser", "ner", "attribute_ruler", "lemmatizer"] pipeline = ["tok2vec", "tagger", "parser", "ner", "attribute_ruler", "lemmatizer"]
data_path = "path/to/en_core_web_sm/en_core_web_sm-3.0.0" data_path = "path/to/en_core_web_sm/en_core_web_sm-3.0.0"
@ -252,7 +252,7 @@ data_path = "path/to/en_core_web_sm/en_core_web_sm-3.0.0"
cls = spacy.util.get_lang_class(lang) # 1. Get Language class, e.g. English cls = spacy.util.get_lang_class(lang) # 1. Get Language class, e.g. English
nlp = cls() # 2. Initialize it nlp = cls() # 2. Initialize it
for name in pipeline: for name in pipeline:
nlp.add_pipe(name) # 3. Add the component to the pipeline nlp.add_pipe(name, config={...}) # 3. Add the component to the pipeline
nlp.from_disk(data_path) # 4. Load in the binary data nlp.from_disk(data_path) # 4. Load in the binary data
``` ```