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README.md

@@ -2,14 +2,14 @@
 base_model: Qwen/Qwen3-Reranker-0.6B
 library_name: model2vec
 license: mit
-model_name: m2v_model
+model_name: m2v_model1
 tags:
 - embeddings
 - static-embeddings
 - sentence-transformers
 ---
 
-# m2v_model Model Card
+# m2v_model1 Model Card
 
 This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the Qwen/Qwen3-Reranker-0.6B(https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
 
@@ -32,7 +32,7 @@ Load this model using the `from_pretrained` method:
 from model2vec import StaticModel
 
 # Load a pretrained Model2Vec model
-model = StaticModel.from_pretrained("m2v_model")
+model = StaticModel.from_pretrained("m2v_model1")
 
 # Compute text embeddings
 embeddings = model.encode(["Example sentence"])
@@ -46,7 +46,7 @@ You can also use the [Sentence Transformers library](https://github.com/UKPLab/s
 from sentence_transformers import SentenceTransformer
 
 # Load a pretrained Sentence Transformer model
-model = SentenceTransformer("m2v_model")
+model = SentenceTransformer("m2v_model1")
 
 # Compute text embeddings
 embeddings = model.encode(["Example sentence"])