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	Docs: clarify abstract spacy.load examples (#12889)
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				|  | @ -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 | ||||||
| ``` | ``` | ||||||
| 
 | 
 | ||||||
|  |  | ||||||
|  | @ -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 | ||||||
| ``` | ``` | ||||||
| 
 | 
 | ||||||
|  |  | ||||||
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