Update README.md

README.md

@@ -10,10 +10,12 @@ tags:
 ---
 
 # hkunlp/instructor-xl
-This is a general embedding model: It maps **any** piece of text (e.g., a title, a sentence, a document, etc.) to a fixed-length vector in test time **without further training**. With instructions, the embeddings are **domain-specific** (e.g., specialized for science, finance, etc.) and **task-aware** (e.g., customized for classification, information retrieval, etc.)
-
+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!
 The model is easy to use with `sentence-transformer` library.
 
+## Quick start
+<hr />
+
 ## Installation
 ```bash
 git clone https://github.com/HKUNLP/instructor-embedding
@@ -32,14 +34,25 @@ embeddings = model.encode([[instruction,sentence,0]])
 print(embeddings)
 ```
 
+## Use cases
+<hr />
+
+## Calculate embeddings for your customized texts
+If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: 
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`; Input:
+* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
+* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
+* `task_objective` is optional, and it specifies the objective of emebdding, e.g., retrieve a document, classify the sentence, etc.
+
 ## Calculate Sentence similarities
 You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
 ```python
 from sklearn.metrics.pairwise import cosine_similarity
 sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0], 
-               ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]
+               ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]]
 sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0],
-               ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]
+               ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]]
 embeddings_a = model.encode(sentences_a)
 embeddings_b = model.encode(sentences_b)
 similarities = cosine_similarity(embeddings_a,embeddings_b)