Update README.md to improve clarity and enhance K-Means clustering instructions

README.md

@@ -6,12 +6,15 @@ colorTo: yellow
 sdk: static
 pinned: false
 license: apache-2.0
-short_description: 'Exploring text embeddings and group similarity '
+short_description: 'Exploring text embeddings and group similarity'
+models:
+  - onnx-community/Qwen3-0.6B-ONNX
+  - onnx-community/Qwen3-Embedding-0.6B-ONNX
 ---
 
 # Embedding WebGPU Playground
 
-This is a browser-based playground for exploring text embeddings and group similarity using WebGPU and ONNX models.
+This is a browser-based playground for exploring text embeddings, group similarity, and clustering using WebGPU and ONNX models.
 
 ## How it works
 - **Input text** in the textarea. Use single newlines (`\n`) to separate lines within a group, and triple newlines (`\n\n\n`) to separate groups.
@@ -23,18 +26,21 @@ This is a browser-based playground for exploring text embeddings and group simil
 - Cosine similarity is calculated between all group embeddings, resulting in a group-by-group similarity matrix.
 - The similarity matrix is visualized as a heatmap using Plotly (color range locked to 0–1).
 
-
-K-Means Clustering
-re group text by using K-Means and number of group
+## K-Means Clustering & UMAP
+- Enter the number of clusters in the **Clusters:** input.
+- Click **Cluster & Show UMAP** to cluster all lines (ignoring groups) using K-Means and visualize the result with UMAP in a scatter plot.
+- After clustering, the textarea is updated to group lines by cluster (triple newlines between clusters), and the heatmap is automatically refreshed to reflect the new groupings.
 
 ## Tech stack
 - [@huggingface/transformers](https://www.npmjs.com/package/@huggingface/transformers) (ESM, WebGPU)
 - [ONNX Qwen3-Embedding-0.6B-ONNX](https://huggingface.co/onnx-community/Qwen3-Embedding-0.6B-ONNX)
 - [Plotly.js](https://plotly.com/javascript/) (UMD)
+- [umap-js](https://github.com/PAIR-code/umap-js) (for 2D projection)
 
 ## Usage
 1. Enter or paste your text in the textarea.
-2. Separate groups with triple newlines.
-3. Click **Run** to compute and visualize group similarities.
+2. Separate groups with triple newlines if you want to compare group similarity.
+3. Click **Show Similarity Heatmap** to compute and visualize group similarities.
+4. To cluster all lines, set the number of clusters and click **Cluster & Show UMAP**. The textarea and heatmap will update to reflect the new clusters.
 
 ---