Update model card: Add GitHub link and refine pipeline tag for EmbeddingGemma

README.md

@@ -1,16 +1,16 @@
 ---
-license: gemma
-pipeline_tag: sentence-similarity
 library_name: sentence-transformers
+license: gemma
+pipeline_tag: feature-extraction
 tags:
 - sentence-transformers
 - sentence-similarity
 - feature-extraction
 - text-embeddings-inference
 extra_gated_heading: Access EmbeddingGemma on Hugging Face
-extra_gated_prompt: To access EmbeddingGemma on Hugging Face, you’re required to review and
-  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
-  Face and click below. Requests are processed immediately.
+extra_gated_prompt: To access EmbeddingGemma on Hugging Face, you’re required to review
+  and agree to Google’s usage license. To do this, please ensure you’re logged in
+  to Hugging Face and click below. Requests are processed immediately.
 extra_gated_button_content: Acknowledge license
 ---
 
@@ -23,6 +23,7 @@ extra_gated_button_content: Acknowledge license
 *   [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
 *   [EmbeddingGemma on Kaggle](https://www.kaggle.com/models/google/embeddinggemma/)
 *   [EmbeddingGemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/embeddinggemma)
+*   Code: [Gemma Cookbook GitHub](https://github.com/google-gemini/gemma-cookbook)
 
 **Terms of Use**: [Terms](https://ai.google.dev/gemma/terms)
 
@@ -283,7 +284,7 @@ The model was evaluated against a large collection of different datasets and met
   <tbody>
     <tr>
       <td><strong>Quant config (dimensionality)</strong></td>
-      <td><strong>Mean (Task)</strong></td>
+      <td><strong>Mean (Task)</td>
       <td><strong>Mean (TaskType)</strong></td>
     </tr>
     <tr>
@@ -428,7 +429,7 @@ Open embedding models have a wide range of applications across various industrie
 
 -   **Semantic Similarity**: Embeddings optimized to assess text similarity, such as recommendation systems and duplicate detection
 -   **Classification**: Embeddings optimized to classify texts according to preset labels, such as sentiment analysis and spam detection
--  **Clustering**: Embeddings optimized to cluster texts based on their similarities, such as document organization, market research, and anomaly detection
+-   **Clustering**: Embeddings optimized to cluster texts based on their similarities, such as document organization, market research, and anomaly detection
 -   **Retrieval**
     -   **Document**: Embeddings optimized for document search, such as indexing articles, books, or web pages for search
     -   **Query**: Embeddings optimized for general search queries, such as custom search