fix(docs): SEO improvements for page titles, internal links, and URL structure (#7224)

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+---
+title: Sentence Transformer
+description: 'Local reranking with HuggingFace cross-encoder models'
+---
+
+Sentence Transformer reranker provides local reranking using HuggingFace cross-encoder models, perfect for privacy-focused deployments where you want to keep data on-premises.
+
+## Models
+
+Any HuggingFace cross-encoder model can be used. Popular choices include:
+
+- **`cross-encoder/ms-marco-MiniLM-L-6-v2`**: Default, good balance of speed and accuracy
+- **`cross-encoder/ms-marco-TinyBERT-L-2-v2`**: Fastest, smaller model size
+- **`cross-encoder/ms-marco-electra-base`**: Higher accuracy, larger model
+- **`cross-encoder/stsb-distilroberta-base`**: Good for semantic similarity tasks
+
+## Installation
+
+```bash
+pip install sentence-transformers
+```
+
+## Configuration
+
+```python Python
+from mem0 import Memory
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "my_memories",
+            "path": "./chroma_db"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-5-mini"
+        }
+    },
+    "rerank": {
+        "provider": "sentence_transformer",
+        "config": {
+            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+            "device": "cpu",  # or "cuda" for GPU
+            "batch_size": 32,
+            "show_progress_bar": False,
+            "top_k": 5
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+```
+
+## TypeScript (self-hosted)
+
+The [TypeScript OSS SDK](/open-source/features/reranker-search#typescript-sdk) (`mem0ai/oss`) runs this reranker locally with [Transformers.js](https://huggingface.co/docs/transformers.js). Because it executes ONNX weights, the default model is the ONNX mirror of the Python default: `Xenova/ms-marco-MiniLM-L-6-v2`. Point `model` at any ONNX-exported cross-encoder on the Hub (a raw `cross-encoder/...` PyTorch checkpoint will not load in this runtime).
+
+```bash
+pnpm add @huggingface/transformers
+```
+
+```typescript
+import { Memory } from "mem0ai/oss";
+
+const memory = new Memory({
+  reranker: {
+    provider: "sentence_transformer",
+    config: {
+      // model: "Xenova/ms-marco-MiniLM-L-6-v2", // default (ONNX)
+      device: "cpu", // "cpu" | "wasm" | "webgpu"
+      maxLength: 512, // max tokens per query-document pair
+      normalize: true, // sigmoid-normalize logits to [0, 1] (default)
+      topK: 5,
+    },
+  },
+});
+
+const results = await memory.search("What books does the user like?", {
+  filters: { userId: "charlie" },
+  rerank: true,
+});
+```
+
+<Note>
+  `batchSize` and `showProgressBar` are accepted for parity with the Python SDK but are no-ops in the TypeScript runtime, because a search reranks a small candidate set in a single in-process forward pass. The model downloads once and is cached in-process.
+</Note>
+
+## GPU Acceleration
+
+For better performance, use GPU acceleration:
+
+```python Python
+config = {
+    "rerank": {
+        "provider": "sentence_transformer",
+        "config": {
+            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+            "device": "cuda",  # Use GPU
+            "batch_size": 64   # high batch size for high memory GPUs
+        }
+    }
+}
+```
+
+## Usage Example
+
+```python Python
+from mem0 import Memory
+
+# Initialize memory with local reranker
+config = {
+    "vector_store": {"provider": "chroma"},
+    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
+    "rerank": {
+        "provider": "sentence_transformer",
+        "config": {
+            "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+            "device": "cpu"
+        }
+    }
+}
+
+memory = Memory.from_config(config)
+
+# Add memories
+messages = [
+    {"role": "user", "content": "I love reading science fiction novels"},
+    {"role": "user", "content": "My favorite author is Isaac Asimov"},
+    {"role": "user", "content": "I also enjoy watching sci-fi movies"}
+]
+
+memory.add(messages, user_id="charlie")
+
+# Search with local reranking
+results = memory.search("What books does the user like?", filters={"user_id": "charlie"})
+
+for result in results['results']:
+    print(f"Memory: {result['memory']}")
+    print(f"Vector Score: {result['score']:.3f}")
+    print(f"Rerank Score: {result['rerank_score']:.3f}")
+    print()
+```
+
+## Custom Models
+
+You can use any HuggingFace cross-encoder model:
+
+```python Python
+# Using a different model
+config = {
+    "rerank": {
+        "provider": "sentence_transformer", 
+        "config": {
+            "model": "cross-encoder/stsb-distilroberta-base",
+            "device": "cpu"
+        }
+    }
+}
+```
+
+## Configuration Parameters
+
+| Parameter | Description | Type | Default |
+|-----------|-------------|------|---------|
+| `model` | HuggingFace cross-encoder model name | `str` | `"cross-encoder/ms-marco-MiniLM-L-6-v2"` |
+| `device` | Device to run model on (`cpu`, `cuda`, etc.) | `str` | `None` |
+| `batch_size` | Batch size for processing documents | `int` | `32` |
+| `show_progress_bar` | Show progress bar during processing | `bool` | `False` |
+| `top_k` | Maximum documents to return | `int` | `None` |
+
+## Advantages
+
+- **Privacy**: Complete local processing, no external API calls
+- **Cost**: No per-token charges after initial model download
+- **Customization**: Use any HuggingFace cross-encoder model
+- **Offline**: Works without internet connection after model download
+
+## Performance Considerations
+
+- **First Run**: Model download may take time initially
+- **Memory Usage**: Models require GPU/CPU memory
+- **Batch Size**: Optimize batch size based on available memory
+- **Device**: GPU acceleration significantly improves speed
+
+## Best Practices
+
+1. **Model Selection**: Choose model based on accuracy vs speed requirements
+2. **Device Management**: Use GPU when available for better performance
+3. **Batch Processing**: Process multiple documents together for efficiency
+4. **Memory Monitoring**: Monitor system memory usage with larger models