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

This PR improves the model card for EmbeddingGemma by:
- Updating the `pipeline_tag` from `sentence-similarity` to `feature-extraction` to better reflect the model's primary function as an embedding model that produces vector representations of text for various downstream tasks. This ensures it appears in relevant searches on the Hugging Face Hub for feature extraction models. The `sentence-similarity` tag is retained within the general `tags` list.
- Adding a link to the official `google-gemini/gemma-cookbook` GitHub repository in the model card content, providing users with a comprehensive resource for examples and guides related to Gemma models, including 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