From ecc017bbbcdab5b063649c17f36d8c5b3720ae84 Mon Sep 17 00:00:00 2001 From: svlandeg Date: Thu, 7 Sep 2023 14:53:18 +0200 Subject: [PATCH] simplify Python example --- website/docs/usage/large-language-models.mdx | 22 +++++++------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/website/docs/usage/large-language-models.mdx b/website/docs/usage/large-language-models.mdx index 38b899261..2499f3dc0 100644 --- a/website/docs/usage/large-language-models.mdx +++ b/website/docs/usage/large-language-models.mdx @@ -169,25 +169,17 @@ to be `"databricks/dolly-v2-12b"` for better performance. ### Example 3: Create the component directly in Python {id="example-3"} -The `llm` component behaves as any other component does, so adding it to an -existing pipeline follows the same pattern: +The `llm` component behaves as any other component does, and there are +[task-specific components](/api/large-language-models#implementation) defined to +help you hit the ground running with a reasonable built-in task implementation. ```python import spacy nlp = spacy.blank("en") -nlp.add_pipe( - "llm", - config={ - "task": { - "@llm_tasks": "spacy.NER.v2", - "labels": ["PERSON", "ORGANISATION", "LOCATION"] - }, - "model": { - "@llm_models": "spacy.GPT-3-5.v1", - }, - }, -) +llm_ner = nlp.add_pipe("llm_ner") +llm_ner.add_label("PERSON") +llm_ner.add_label("LOCATION") nlp.initialize() doc = nlp("Jack and Jill rode up the hill in Les Deux Alpes") print([(ent.text, ent.label_) for ent in doc.ents]) @@ -314,7 +306,7 @@ COMPLIMENT ## API {id="api"} -`spacy-llm` exposes a `llm` factory with +`spacy-llm` exposes an `llm` factory with [configurable settings](/api/large-language-models#config). An `llm` component is defined by two main settings: