deleted description, config.yaml

config.yaml

@@ -2,81 +2,29 @@ models:
   qwen3-0.6b:
     name: "Qwen/Qwen3-Embedding-0.6B"
     type: "embeddings"
-    dimension: 1024
-    max_tokens: 32768
-    description: |
-      The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. 
-      This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.  
-      We recommend that developers customize the instruct according to their specific scenarios, tasks, and languages. 
-      Our tests have shown that in most retrieval scenarios, not using an instruct on the query side can lead to a drop in retrieval 
-      performance by approximately 1% to 5%.
-    language: ["multilingual"]
     repository: "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B"
 
   gemma-300M:
     name: "google/embeddinggemma-300M"
     type: "embeddings"
-    dimension: 768
-    max_tokens: 2048
-    description: |
-      EmbeddingGemma can generate optimized embeddings for various use cases—such as document retrieval, question answering, 
-      and fact verification—or for specific input types—either a query or a document—using prompts that are prepended to the 
-      input strings. Query prompts follow the form task: {task description} | query: where the task description varies by the use case, 
-      with the default task description being search result. Document-style prompts follow the form title: {title | "none"} | text: 
-      where the title is either none (the default) or the actual title of the document. Note that providing a title, if available, 
-      will improve model performance for document prompts but may require manual formatting.
-    language: ["multilingual"]
     repository: "https://huggingface.co/google/embeddinggemma-300m"
 
   multilingual-e5-small:
     name: "intfloat/multilingual-e5-small"
     type: "embeddings"
-    dimension: 384
-    max_tokens: 512
-    description: |
-      This model is initialized from microsoft/Multilingual-MiniLM-L12-H384 and continually trained on a mixture of multilingual datasets. 
-      It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. 
-      Need instruction, please refer to huggingface repo.
-    language: ["multilingual"]
     repository: "https://huggingface.co/intfloat/multilingual-e5-small"
 
-  naver-splade-v3:
-    name: "naver/splade-v3"
+  splade-pp-v1:
+    name: "prithivida/Splade_PP_en_v1"
     type: "sparse-embeddings"
-    dimension: 1234   # must add this field
-    max_tokens: 1234
-    description: |
-      SPLADE models are a fine balance between retrieval effectiveness (quality) and retrieval efficiency (latency and $), 
-      with that in mind we did very minor retrieval efficiency tweaks to make it more suitable for a industry setting. 
-      (Pure MLE folks should not conflate efficiency to model inference efficiency. Our main focus is on retrieval efficiency. 
-      Hereinafter efficiency is a short hand for retrieval efficiency unless explicitly qualified otherwise. 
-      Not that inference efficiency is not important, we will address that subsequently.)
-    language: ["multilingual"]
-    repository: "https://huggingface.co/naver/splade-v3"
+    repository: "https://huggingface.co/prithivida/Splade_PP_en_v1"
 
   splade-pp-v2:
     name: "prithivida/Splade_PP_en_v2"
     type: "sparse-embeddings"
-    dimension: 1234   # must add this field
-    max_tokens: 1234
-    description: |
-      SPLADE models are a fine balance between retrieval effectiveness (quality) and retrieval efficiency (latency and $), 
-      with that in mind we did very minor retrieval efficiency tweaks to make it more suitable for a industry setting. 
-      (Pure MLE folks should not conflate efficiency to model inference efficiency. Our main focus is on retrieval efficiency. 
-      Hereinafter efficiency is a short hand for retrieval efficiency unless explicitly qualified otherwise. 
-      Not that inference efficiency is not important, we will address that subsequently.)
-    language: ["multilingual"]
     repository: "https://huggingface.co/prithivida/Splade_PP_en_v2"
 
-  splade-pp-v1:
-    name: "prithivida/Splade_PP_en_v1"
+  naver-splade-v3:
+    name: "naver/splade-v3"
     type: "sparse-embeddings"
-    dimension: 1234   # must add this field
-    max_tokens: 1234
-    description: |
-       Granite-Embedding-30m-Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings. 
-       This model produces variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights and is trained using a combination of open source relevance-pair datasets with permissive,
-       enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. 
-       This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation for improved performance.
-    language: ["En"]
-    repository: "https://huggingface.co/prithivida/Splade_PP_en_v1"
+    repository: "https://huggingface.co/naver/splade-v3"