email-sorter/docs/chat-gippity-research.md

Yeah — your instinct is solid here, Brett. 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.

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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🧠 What Sentence Transformers Actually Do

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.

Once you have embeddings, classification becomes a simple, fast downstream problem:

• Instead of training a giant deep network,
• You train a small, lightweight model (like Logistic Regression, XGBoost, or a simple neural head) on top of those embeddings.

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🚀 Why This Is a Great Fit for Your Email Sorter

Sentence Transformers	Why it matters for you
Pretrained models already “understand” general language	So you don’t need massive datasets to get good results.
Fast inference	Embedding generation can run on CPU or GPU and is easy to parallelise.
Stable + deterministic	Embeddings are consistent across runs (unlike LLM zero-shot answers).
Lightweight training	You can train a classifier on top with a few thousand labelled samples.
Supports multi-class easily	Perfect for your 12 category types.
Re-usable	One universal model for all inboxes; just retrain the top layer occasionally.

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🏗️ How It Would Work in Your Pipeline

1. Use the LLM once to label your initial batch (e.g. 1.5k–5k emails).

This gives you your bootstrapped labelled dataset.

2. Generate embeddings

• Take subject, sender domain, and optionally a short body snippet.
• Pass through sentence transformer → get 768-dimensional vector (typical).
• Save those embeddings alongside labels.

3. Train a classifier on top

A lightweight model like:

• Logistic Regression (fastest),
• XGBoost / LightGBM (slightly heavier, more accuracy),
• or even a shallow MLP if you want.

This becomes your universal email classifier.

4. Run in production

• New email comes in → embed text → run classifier → get category + confidence.
• If below threshold → send to LLM for re-classification.

This gives you LLM semantic power at training time, and ML speed at runtime.

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🧪 Practical Setup (Minimal)

from sentence_transformers import SentenceTransformer
from sklearn.linear_model import LogisticRegression

# 1. Load a pre-trained embedding model
model = SentenceTransformer("all-MiniLM-L6-v2")  # fast, lightweight

# 2. Convert texts to embeddings
texts = [f"{subject} {sender}" for subject, sender in your_email_data]
embeddings = model.encode(texts, batch_size=64, show_progress_bar=True)

# 3. Train a lightweight classifier
clf = LogisticRegression(max_iter=1000)
clf.fit(embeddings, labels)  # labels come from your LLM bootstrap

# 4. Predict
new_embedding = model.encode(["Subject from new email"])
pred = clf.predict(new_embedding)
conf = clf.predict_proba(new_embedding)

• all-MiniLM-L6-v2 is a fantastic starting model — small, fast, and surprisingly accurate.
• You can fine-tune the sentence transformer later if you want extra precision.

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🧠 Why It’s Easier Than Full Fine-Tuning

You don’t need to train the transformer itself (at least not at the start). You’re just training the top layer (classifier). That means:

• Training takes minutes, not hours.
• You don’t need huge GPUs.
• You can refresh or retrain easily with new data.

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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⚡ Typical Results People See

• With 2–5k labelled samples, sentence transformer embeddings + logistic regression can hit 85–95 % accuracy on email category tasks.
• Inference time is <5 ms per email on CPU.
• Works well for both generic and user-specific inboxes.

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🪜 Suggested Path for You

1. Use your LLM pass to generate labels on your first big inbox.
2. Generate embeddings with a pretrained MiniLM.
3. Train a logistic regression or XGBoost model.
4. Run it on the next inbox → see how it performs.
5. (Optional) Fine-tune the transformer if you want to push performance higher.

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👉 In short: Yes — sentence transformers are perfect for this. 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.

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?