Datasets update (#7)

- Made changes to the dataset, added sentence transformers citation back in (d725d2c17692489b57e8bf74be0c5e940f6fd151)

README.md

@@ -39,9 +39,9 @@ model-index:
       name: Cosine Ap
 ---
 
-# SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
+# Redis Semantic Caching embedding model based on Alibaba-NLP/gte-modernbert-base
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the Medical dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching in the medical domain.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [Medical]( https://www.kaggle.com/datasets/thedevastator/medical-question-pair-classification/data) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching in the medical domain.
 
 ## Model Details
 
@@ -117,7 +117,7 @@ print(similarities.shape)
 
 #### Medical
 
-* Dataset: Medical dataset
+* Dataset: [Medical dataset]( https://www.kaggle.com/datasets/thedevastator/medical-question-pair-classification/data)
 * Size:  
 * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
 
@@ -125,7 +125,7 @@ print(similarities.shape)
 
 #### Medical
 
-* Dataset: Medical dataset
+* Dataset: [Medical dataset]( https://www.kaggle.com/datasets/thedevastator/medical-question-pair-classification/data)
 * Size:
 * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
 
@@ -133,15 +133,18 @@ print(similarities.shape)
 
 ### BibTeX
 
+#### Redis Langcache-embed Models
+
 #### Sentence Transformers
 ```bibtex
-@inproceedings{redisetal.,
-    title = "",
-    author = "",
-    month = "",
-    year = "",
-    publisher = "",
-    url = "",
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
 }
 ```