Update README.md

README.md

@@ -2524,15 +2524,7 @@ model-index:
       value: 79.25143598295348
 ---
 
-# hkunlp/instructor-large
-We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
-The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! 
-
-**************************** **Updates** ****************************
-
-* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
-* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
-
+# nascenia/instruct-embedding
 ## Quick start
 <hr />
 
@@ -2545,7 +2537,7 @@ pip install InstructorEmbedding
 Then you can use the model like this to calculate domain-specific and task-aware embeddings:
 ```python
 from InstructorEmbedding import INSTRUCTOR
-model = INSTRUCTOR('hkunlp/instructor-large')
+model = INSTRUCTOR('nascenia/instruct-embedding')
 sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
 instruction = "Represent the Science title:"
 embeddings = model.encode([[instruction,sentence]])