---
license: mit
language:
- en
tags:
- sentence-transformer
- embeddings
- mental-health
- intent-classification
pipeline_tag: feature-extraction
base_model: sentence-transformers/all-MiniLM-L6-v2
---

# Intent Encoder (MindPadi)

The `intent_encoder` is a Sentence Transformer model used in the MindPadi mental health assistant for **encoding user messages into dense embeddings**. These embeddings support intent classification, similarity search, and memory recall workflows. It plays a foundational role in the semantic understanding of user inputs across various MindPadi features.


## 🧠 Model Overview

- **Architecture:** Sentence-BERT (`all-MiniLM-L6-v2` base)
- **Task:** Sentence Embedding / Semantic Similarity
- **Purpose:** Embed user queries for intent classification, vector search, and memory retrieval
- **Size:** ~80M parameters
- **Files:**
  - `config.json`
  - `pytorch_model.bin` or `model.safetensors`
  - `tokenizer.json`, `vocab.txt`
  - `1_Pooling/`, `2_Normalize/` (Sentence-BERT components)


## 🧾 Intended Use

### ✔️ Primary Use Cases
- Semantic embedding of user inputs for intent recognition
- Matching new messages against known intent samples (`data/processed_intents.json`)
- Supporting vector similarity in MongoDB Atlas Search or ChromaDB
- Powering memory in LangGraph agentic workflows

### 🚫 Not Recommended For
- Direct intent classification (this model returns embeddings, not classes)
- Use outside of NLP (e.g., image, audio)


## 🧪 Integration in MindPadi

- `app/chatbot/intent_classifier.py`: Uses this model to compute sentence embeddings
- `app/chatbot/intent_router.py`: Leverages vector similarity for intent matching
- `database/vector_search.py`: Embeddings are stored or queried from MongoDB vector index
- `app/utils/embedding_search.py`: Embeds utterances for real-time nearest-neighbor lookup


## 🏋️ Training Details

- **Base Model:** `sentence-transformers/all-MiniLM-L6-v2` (pretrained)
- **Fine-tuning:** Optional domain-specific contrastive learning using pairs in `training/datasets/fallback_pairs.json`
- **Script:** `training/fine_tune_encoder.py` (if fine-tuned)
- **Tokenizer:** BERT-based WordPiece tokenizer
- **Max Token Length:** 128


## 📈 Evaluation

While this model is not evaluated via classification metrics, its **embedding quality** was assessed through:

- **Cosine similarity tests** (intent embedding similarity)
- **Intent clustering accuracy** with `KMeans` in vector space
- **Recall@K** for correct intent retrieval
- **Visualizations:** UMAP plots (`logs/intent_umap.png`)

Results indicate:
- High-quality clustering of semantically similar intents
- ~91% Top-3 Recall for known intents


## 💬 Example Usage

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("mindpadi/intent_encoder")

texts = ["I want to talk to a therapist", "Book a session", "I'm feeling anxious"]
embeddings = model.encode(texts)

print(embeddings.shape)  # (3, 384)
````


## 🧪 Deployment (API Example)

```python
import requests

endpoint = "https://api-inference.huggingface.co/models/mindpadi/intent_encoder"
headers = {"Authorization": f"Bearer <your-token>"}
payload = {"inputs": "I need help managing stress"}

response = requests.post(endpoint, json=payload, headers=headers)
embedding = response.json()
```


## ⚠️ Limitations

* English-only
* Short, clean sentences work best (not optimized for long documents)
* Does not directly return intent labels — must be paired with clustering or classification logic
* May yield ambiguous vectors for multi-intent or vague inputs


## 📜 License

MIT License – open for personal, academic, and commercial use with attribution.


## 📬 Contact

* **Project:** [MindPadi Mental Health Assistant](https://huggingface.co/mindpadi)
* **Team:** MindPadi Developers
* **Email:** \[[you@example.com](mailto:you@example.com)]
* **GitHub:** \[[https://github.com/mindpadi](https://github.com/mindpadi)]

*Last updated: May 2025*