Minor grammar fix (UKPLab#2604)

examples/domain_adaptation/README.md

@@ -61,7 +61,7 @@ GPL works in three phases:
 ![GPL Architecture](https://raw.githubusercontent.com/UKPLab/sentence-transformers/master/docs/img/gpl_architecture.png) 
 
 - **Query Generation**: For a given text from our domain, we first use a T5 model that generates a possible query for the given text. E.g. when your text is *"Python is a high-level general-purpose programming language"*, the model might generate a query like *"What is Python"*. You can find various query generators on our [doc2query-hub](https://huggingface.co/doc2query).
-- **Negative Mining**: Next, for the generate query *"What is Python"* we mine negative passages from our corpus, i.e. passages that are similar to the query but don't which a user would not consider relevant. Such a negative passage could be *"Java is a high-level, class-based, object-oriented programming language."*. We do this mining using dense retrieval, i.e. we use one of the existing text embedding models and retrieve relevant paragraphs for the given query.
+- **Negative Mining**: Next, for the generated query *"What is Python"* we mine negative passages from our corpus, i.e. passages that are similar to the query but which a user would not consider relevant. Such a negative passage could be *"Java is a high-level, class-based, object-oriented programming language."*. We do this mining using dense retrieval, i.e. we use one of the existing text embedding models and retrieve relevant paragraphs for the given query.
 - **Pseudo Labeling**: It might be that in the negative mining step we retrieve a passage that is actually relevant for the query (like another definition for *"What is Python"*). To overcome this issue, we use a [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) to score all (query, passage)-pairs. 
 - **Training**: Once we have the triplets *(generated query, positive passage, mined negative passage)* and the Cross-Encoder scores for *(query, positive)* and *(query, negative)* we can start training the text embedding model using [MarginMSELoss](https://www.sbert.net/docs/package_reference/losses.html#marginmseloss).