Update README.md

README.md

@@ -14,14 +14,14 @@ license: cc-by-nc-4.0
 <b>The code embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
 </p>
 
-# Jina Embeddings c1: A Small but Performant Code Embedding Model
+# Jina Code Embeddings: A Small but Performant Code Embedding Model
 
 ## Intended Usage & Model Info
-`jina-embeddings-c1` is an embedding model for code retrieval. 
+`jina-code-embeddings` is an embedding model for code retrieval. 
 The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages. 
 
 
-Built on [Qwen/Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B), `jina-embeddings-c1-0.5B` features:
+Built on [Qwen/Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B), `jina-code-embeddings-0.5b` features:
 
 - **Multilingual support** (15+ programming languages) and compatibility with a wide range of domains, including web development, software development, machine learning, data science, and educational coding problems.
 - **Task-specific instruction prefixes** for NL2Code, Code2Code, Code2NL, Code2Completion, and Technical QA, which can be selected at inference time.
@@ -30,7 +30,7 @@ Built on [Qwen/Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5
 
 Summary of features:
 
-| Feature   | Jina Embeddings C1 0.5B  |
+| Feature   | Jina Code Embeddings 0.5B  |
 |------------|------------|
 | Base Model | Qwen2.5-Coder-0.5B |
 | Supported Tasks | `nl2code`, `code2code`, `code2nl`, `code2completion`, `qa` |
@@ -66,7 +66,7 @@ from transformers import AutoModel
 import torch
 
 # Initialize the model
-model = AutoModel.from_pretrained("jinaai/jina-embeddings-c1-0.5B", trust_remote_code=True)
+model = AutoModel.from_pretrained("jinaai/jina-code-embeddings-0.5b", trust_remote_code=True)
 model.to("cuda")
 
 # Configure truncate_dim, max_length, batch_size in the encode function if needed
@@ -98,7 +98,7 @@ from sentence_transformers import SentenceTransformer
 
 # Load the model
 model = SentenceTransformer(
-    "jinaai/jina-embeddings-c1-0.5B",
+    "jinaai/jina-code-embeddings-0.5b",
     model_kwargs={
         "torch_dtype": torch.bfloat16,
         "attn_implementation": "flash_attention_2",
@@ -129,7 +129,7 @@ print(similarity)
 
 ## Training & Evaluation
 
-Please refer to our technical report of jina-embeddings-c1 for training details and benchmarks.
+Please refer to our technical report of jina-code-embeddings for training details and benchmarks.
 
 ## Contact