Update README.md

README.md

@@ -11,29 +11,29 @@ pipeline_tag: sentence-similarity
 
 # Bloomz-3b Reranking
 
-This reranking model is built from [cmarkea/bloomz-3b-dpo-chat](https://huggingface.co/cmarkea/bloomz-3b-dpo-chat) model and aims to measure the semantic correspondence between
-a question (query) and a context. With its normalized scoring, it helps to filter the query/context matchings outputted by a retriever in an ODQA (Open-Domain Question Answering)context.
-Moreover, it allows to reorder the results using a more efficient modeling approach than the retriever one. However, this modeling type is not conducive to direct
-database searching due to its high computational cost.
+This reranking model is built from [cmarkea/bloomz-3b-dpo-chat](https://huggingface.co/cmarkea/bloomz-3b-dpo-chat) model and aims to measure the semantic correspondence
+between a question (query) and a context. With its normalized scoring, it helps to filter the query/context matchings outputted by a retriever in an ODQA (Open-Domain
+Question Answering) context. Moreover, it allows to reorder the results using a more efficient modeling approach than the retriever one. However, this modeling type is
+not conducive to direct database searching due to its high computational cost.
 
 Developed to be language-agnostic, this model supports both French and English. Consequently, it can effectively score in a cross-language context without being
 influenced by its behavior in a monolingual context (English or French).
 
 ## Dataset
-The training dataset is composed of the [mMARCO dataset](https://huggingface.co/datasets/unicamp-dl/mmarco), consisting of query/positive/hard negative triplets. Additionally,
-we have included [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) data from the "train" split, forming query/positive/hard negative triplets. In order to generate hard
-negative data for SQuAD, we considered contexts from the same theme as the query but from a different set of queries. Hence, the negative observations belong to the same
-themes as the queries but presumably do not contain the answer to the question.
+The training dataset is composed of the [mMARCO dataset](https://huggingface.co/datasets/unicamp-dl/mmarco), consisting of query/positive/hard negative triplets.
+Additionally, we have included [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) data from the "train" split, forming query/positive/hard negative triplets. In
+order to generate hard negative data for SQuAD, we considered contexts from the same theme as the query but from a different set of queries. Hence, the negative
+observations belong to the same themes as the queries but presumably do not contain the answer to the question.
 
 Finally, the triplets are flattened to obtain pairs of query/context sentences with a label 1 if query/positive and a label 0 if query/negative. In each element of the
 pair (query and context), the language, French or English, is randomly and uniformly chosen.
 
 ## Evaluation
 
-To assess the performance of the reranker, we will make use of the "validation" split of the [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) dataset. We will select
-the first question from each paragraph, along with the paragraph constituting the context that should be ranked Top-1 for an Oracle modeling. What's intriguing is that
-the number of themes is limited, and each context from a corresponding theme that does not match the query is considered as a hard negative (other contexts outside the theme are
-simple negatives). Thus, we can construct the following table, with each theme showing the number of contexts and associated query:
+To assess the performance of the reranker, we will make use of the "validation" split of the [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) dataset. We will
+select the first question from each paragraph, along with the paragraph constituting the context that should be ranked Top-1 for an Oracle modeling. What's intriguing
+is that the number of themes is limited, and each context from a corresponding theme that does not match the query is considered as a hard negative (other contexts
+outside the theme are simple negatives). Thus, we can construct the following table, with each theme showing the number of contexts and associated query:
 
 | Theme name                                   | Context number |
 |---------------------------------------------:|:---------------|