FSSCoding
53174a34eb
Project Reorganization: - Created docs/ directory and moved all documentation - Created scripts/ directory for shell scripts - Created scripts/experimental/ for research scripts - Updated .gitignore for new structure - Updated README.md with MVP status and new structure New Features: - Category verification system (verify_model_categories) - --verify-categories flag for mailbox compatibility check - --no-llm-fallback flag for pure ML classification - Trained model saved in src/models/calibrated/ Threshold Optimization: - Reduced default threshold from 0.75 to 0.55 - Updated all category thresholds to 0.55 - Reduces LLM fallback rate by 40% (35% -> 21%) Documentation: - SYSTEM_FLOW.html - Complete system architecture - VERIFY_CATEGORIES_FEATURE.html - Feature documentation - LABEL_TRAINING_PHASE_DETAIL.html - Calibration breakdown - FAST_ML_ONLY_WORKFLOW.html - Pure ML guide - PROJECT_STATUS_AND_NEXT_STEPS.html - Roadmap - ROOT_CAUSE_ANALYSIS.md - Bug fixes MVP Status: - 10k emails in 4 minutes, 72.7% accuracy, 0 LLM calls - LLM-driven category discovery working - Embedding-based transfer learning confirmed - All model paths verified and working
127 lines
5.5 KiB
Markdown
127 lines
5.5 KiB
Markdown
Yeah — your instinct is solid here, Brett.
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For the kind of **multi-category, fast, local, high-volume** classification system you’re building, training your own model on your own labelled data will almost certainly give **better and more controllable results** than trying to stretch an off-the-shelf model.
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Let’s break this down clearly — especially what **sentence-transformers** brings to the table and why it’s a good fit for your hybrid architecture.
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---
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## 🧠 What Sentence Transformers Actually Do
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A **sentence transformer** is a model that converts a piece of text (e.g. subject line, short body snippet, sender info) into a **dense vector embedding** — basically a fixed-length numerical representation that captures its semantic meaning.
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Once you have embeddings, classification becomes a **simple, fast** downstream problem:
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* Instead of training a giant deep network,
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* You train a **small, lightweight model** (like Logistic Regression, XGBoost, or a simple neural head) on top of those embeddings.
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---
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## 🚀 Why This Is a Great Fit for Your Email Sorter
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| Sentence Transformers | Why it matters for you |
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| ----------------------------------------------------------- | ----------------------------------------------------------------------------- |
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| **Pretrained models** already “understand” general language | So you don’t need massive datasets to get good results. |
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| **Fast inference** | Embedding generation can run on CPU or GPU and is easy to parallelise. |
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| **Stable + deterministic** | Embeddings are consistent across runs (unlike LLM zero-shot answers). |
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| **Lightweight training** | You can train a classifier on top with a few thousand labelled samples. |
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| **Supports multi-class** easily | Perfect for your 12 category types. |
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| **Re-usable** | One universal model for all inboxes; just retrain the top layer occasionally. |
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---
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## 🏗️ How It Would Work in Your Pipeline
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### 1. **Use the LLM once** to label your initial batch (e.g. 1.5k–5k emails).
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This gives you your **bootstrapped labelled dataset**.
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### 2. **Generate embeddings**
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* Take **subject**, **sender domain**, and optionally a short **body snippet**.
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* Pass through sentence transformer → get 768-dimensional vector (typical).
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* Save those embeddings alongside labels.
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### 3. **Train a classifier** on top
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A lightweight model like:
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* **Logistic Regression** (fastest),
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* **XGBoost / LightGBM** (slightly heavier, more accuracy),
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* or even a shallow **MLP** if you want.
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This becomes your **universal email classifier**.
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### 4. **Run in production**
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* New email comes in → embed text → run classifier → get category + confidence.
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* If below threshold → send to LLM for re-classification.
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This gives you **LLM semantic power** at training time, and **ML speed** at runtime.
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---
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## 🧪 Practical Setup (Minimal)
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```python
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from sentence_transformers import SentenceTransformer
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from sklearn.linear_model import LogisticRegression
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# 1. Load a pre-trained embedding model
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model = SentenceTransformer("all-MiniLM-L6-v2") # fast, lightweight
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# 2. Convert texts to embeddings
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texts = [f"{subject} {sender}" for subject, sender in your_email_data]
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embeddings = model.encode(texts, batch_size=64, show_progress_bar=True)
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# 3. Train a lightweight classifier
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clf = LogisticRegression(max_iter=1000)
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clf.fit(embeddings, labels) # labels come from your LLM bootstrap
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# 4. Predict
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new_embedding = model.encode(["Subject from new email"])
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pred = clf.predict(new_embedding)
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conf = clf.predict_proba(new_embedding)
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```
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* `all-MiniLM-L6-v2` is a fantastic starting model — small, fast, and surprisingly accurate.
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* You can fine-tune the sentence transformer later if you want **extra precision**.
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---
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## 🧠 Why It’s Easier Than Full Fine-Tuning
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You *don’t* need to train the transformer itself (at least not at the start).
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You’re just training the **top layer** (classifier). That means:
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* Training takes minutes, not hours.
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* You don’t need huge GPUs.
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* You can refresh or retrain easily with new data.
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Later on, if you want to **fine-tune the transformer itself** (so it “understands emails” more deeply), that’s an optional next step.
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---
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## ⚡ Typical Results People See
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* With 2–5k labelled samples, sentence transformer embeddings + logistic regression can hit **85–95 % accuracy** on email category tasks.
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* Inference time is **<5 ms per email** on CPU.
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* Works well for both generic and user-specific inboxes.
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---
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## 🪜 Suggested Path for You
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1. Use your **LLM pass** to generate labels on your first big inbox.
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2. Generate embeddings with a pretrained MiniLM.
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3. Train a logistic regression or XGBoost model.
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4. Run it on the next inbox → see how it performs.
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5. (Optional) Fine-tune the transformer if you want to push performance higher.
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---
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👉 In short:
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Yes — sentence transformers are **perfect** for this.
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They give you **semantic power without LLM overhead**, are **easy to train**, and will make your hybrid classifier **extremely fast and accurate** after that first run.
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If you want, I can give you a **tiny starter training script** (30–40 lines) that does the embedding + classifier training from your first LLM-labelled dataset. Would you like that?
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