Update README.md

README.md

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 # all-mpnet-base-v2-negation
-This is a fine-tuned [sentence-transformers](https://www.SBERT.net) model to perform better with negated pairs of sentences.
+**This is a fine-tuned [sentence-transformers](https://www.SBERT.net) model to perform better on negated pairs of sentences.**
 
 It maps sentences and paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -851,8 +851,5 @@ We used [`sentence-transformers/all-mpnet-base-v2`](https://huggingface.co/sente
 We fine-tuned the model on the [CANNOT dataset](https://huggingface.co/datasets/tum-nlp/cannot-dataset) using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs.
 
 #### Hyper parameters
-We followed an analogous approach to [how other Sentence Transformers were trained](https://github.com/UKPLab/sentence-transformers/blob/3e1929fddef16df94f8bc6e3b10598a98f46e62d/examples/training/nli/training_nli_v2.py).
-
-We took the first 90% of samples from the CANNOT dataset as the training split.
-
+We followed an analogous approach to [how other Sentence Transformers were trained](https://github.com/UKPLab/sentence-transformers/blob/3e1929fddef16df94f8bc6e3b10598a98f46e62d/examples/training/nli/training_nli_v2.py). We took the first 90% of samples from the CANNOT dataset as the training split. 
 We used a batch size of 64 and trained for 1 epoch.