Merge branch 'main' into omri/new_transformers_engine

microsoft · Oct 17, 2023 · f2a4a29 · f2a4a29
2 parents 5822a00 + f19dbce
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diff --git a/docs/tutorial/05_languages.md b/docs/tutorial/05_languages.md
@@ -13,7 +13,7 @@ In this example we will configure Presidio to use spaCy as its underlying NLP fr
 
 <!--pytest-codeblocks:cont-->
 ```python
-from presidio import AnalyzerEngine
+from presidio_analyzer import AnalyzerEngine
 from presidio_analyzer.nlp_engine import NlpEngineProvider
 
 # import spacy

diff --git a/docs/tutorial/06_context.md b/docs/tutorial/06_context.md
@@ -2,7 +2,7 @@
 
 Presidio has an internal mechanism for leveraging context words. This mechanism would increase the detection confidence of a PII entity in case a specific word appears before or after it.
 
-Furthermore, it is possible to create your own context enhancer, if your require a different logic for identifying context terms. The default context-aware enhancer in Presidio is the `LemmaContextAwareEnhancer` which compares each recognizer's context terms with the lemma of each token in the sentence.
+Furthermore, it is possible to create your own context enhancer, if you require a different logic for identifying context terms. The default context-aware enhancer in Presidio is the `LemmaContextAwareEnhancer` which compares each recognizer's context terms with the lemma of each token in the sentence.
 
 In this example we would first implement a zip code recognizer without context, and then add context to see how the confidence changes. Zip regex patterns (essentially 5 digits) are very weak, so we would want the initial confidence to be low, and increased with the existence of context words.