TinyModels/Setfit-Banking-Spam

🧠 Tiny model. Tiny dataset. Real classification.

A compact few-shot spam classifier built with SetFit + BAAI/bge-small-en-v1.5.

---

⚡ TINY → FAST → USEFUL

This model is a small experiment in few-shot text classification.

It learns to separate:

✉️ HAM — legitimate email 🚨 SPAM — unwanted / suspicious email

The interesting part?

Only 16 labeled training examples.

                 ┌──────────────────────┐
                 │      16 EXAMPLES     │
                 │                      │
                 │   8 HAM  +  8 SPAM   │
                 └──────────┬───────────┘
                            │
                            ▼
                ┌───────────────────────┐
                │ BAAI/bge-small-en-v1.5│
                └───────────┬───────────┘
                            │
                            ▼
                   ┌────────────────┐
                   │     SetFit     │
                   │  Few-shot NLP  │
                   └───────┬────────┘
                           │
                           ▼
                ┌───────────────────────┐
                │ Logistic Regression   │
                └───────────┬───────────┘
                            │
                     ┌──────┴──────┐
                     ▼             ▼
                   HAM           SPAM

---

📡 Model Status

⚙️ Component	🔧 Configuration
Task	Spam Classification
Classes	2
Backbone	BAAI/bge-small-en-v1.5
Framework	SetFit
Dataset	SetFit/enron_spam
Training Examples	16
Examples / Class	8
Iterations	20
Epochs	1
Batch Size	16
Classification Head	Logistic Regression
Language	English

---

📊 Results

91.4% Accuracy

91.4% Macro F1

Accuracy   ██████████████████░░  91.4%
Macro F1   ██████████████████░░  91.4%

These results come from a very small few-shot training setup. They should not be interpreted as a benchmark against production spam-filtering systems.

---

🧬 The TinyModels Recipe

              ┌───────────────────┐
              │   SetFit/enron    │
              │      _spam        │
              └─────────┬─────────┘
                        │
                  16 examples
                        │
                        ▼
              ┌───────────────────┐
              │     BGE-small     │
              │   text encoder    │
              └─────────┬─────────┘
                        │
                   embeddings
                        │
                        ▼
              ┌───────────────────┐
              │      SetFit       │
              │ contrastive loss  │
              └─────────┬─────────┘
                        │
                        ▼
              ┌───────────────────┐
              │ LogisticRegression│
              └─────────┬─────────┘
                        │
                 ┌──────┴──────┐
                 ▼             ▼
              ✉️ HAM        🚨 SPAM

Training configuration

backbone: BAAI/bge-small-en-v1.5
dataset: SetFit/enron_spam

examples:
  total: 16
  per_class: 8

setfit:
  iterations: 20
  epochs: 1
  batch_size: 16
  loss: CosineSimilarityLoss
  distance_metric: cosine_distance

classifier:
  type: LogisticRegression

seed: 42

---

🚀 Run It

pip install -q setfit

from setfit import SetFitModel

model = SetFitModel.from_pretrained(
    "TinyModels/setfit-banking-spam"
)

text = """
Congratulations! You have won $1,000,000.
Click here immediately to claim your prize.
"""

prediction = model(text)

print(prediction)

Example output

1

---

🧪 Quick Examples

🚨 Spam

CONGRATULATIONS!!!

You have been selected to receive
$1,000,000. Click the link below
to claim your prize immediately.

→ SPAM

---

✉️ Legitimate

Please find attached the global markets
monitor for the week ending 12 January 2001.

→ HAM

---

🧠 Why SetFit?

Traditional supervised classification can require a large labeled dataset.

SetFit takes a different route:

        Large Dataset
             ✕
             │
             │
       ┌─────▼─────┐
       │  SetFit   │
       └─────┬─────┘
             │
             ▼
     Few labeled examples
             │
             ▼
       Useful classifier

This makes the experiment useful for exploring:

• ⚡ Few-shot learning
• 🧠 Sentence embeddings
• 📚 Text classification
• 🚨 Spam detection
• 🔬 Efficient training
• 🤗 SetFit

---

🧩 Architecture

INPUT
  │
  ▼
┌─────────────────────────────┐
│  BAAI/bge-small-en-v1.5     │
│                             │
│  Sentence Transformer       │
└──────────────┬──────────────┘
               │
               ▼
        Dense Embedding
               │
               ▼
┌─────────────────────────────┐
│    Logistic Regression      │
└──────────────┬──────────────┘
               │
        ┌──────┴──────┐
        ▼             ▼
      HAM           SPAM
       ✉️             🚨

---

⚠️ Limitations

This is intentionally a tiny experimental model.

Because only 16 examples were used for training:

• Performance can vary on unseen data.
• Domain shift can significantly affect predictions.
• Unusual spam may be missed.
• Enron-style email does not represent every modern spam pattern.
• The reported score comes from a lightweight few-shot experiment.

Do not use this model as the sole component of a security-critical email filtering system.

---

📦 Model Identity

╭──────────────────────────────────────╮
│              TINYMODEL               │
├──────────────────────────────────────┤
│                                      │
│  MODEL      SetFit Banking Spam      │
│  BACKBONE   BGE-small                │
│  TASK       Binary Classification    │
│  DATA       Enron Spam               │
│  EXAMPLES   16                       │
│  RESULT     91.4% Accuracy           │
│                                      │
│  STATUS     ● EXPERIMENTAL           │
│                                      │
╰──────────────────────────────────────╯

---

⚡ 16 Examples.

🧠 One Small Model.

🚨 One Real Task.

TinyModels — building small models that actually do things.