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	---
	license: apache-2.0
	language:
	- en
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- spam-detection
	- email-classification
	- few-shot-learning
	- efficient
	- tinymodels
	pipeline_tag: text-classification
	library_name: setfit
	base_model: BAAI/bge-small-en-v1.5
	datasets:
	- SetFit/enron_spam
	---

	<div align="center">

	<img src="https://readme-typing-svg.demolab.com?font=JetBrains+Mono&size=30&duration=2500&pause=700&color=FF0080&center=true&vCenter=true&width=650&lines=%E2%9A%A1+TinyModels;SetFit+%C2%B7+Spam+Detection;16+Examples+%E2%86%92+Classifier;Small+Model.+Real+Task." alt="TinyModels animated header">

	<br>

	<img src="https://img.shields.io/badge/TinyModels-FF0080?style=for-the-badge&logoColor=white">
	<img src="https://img.shields.io/badge/SetFit-7928CA?style=for-the-badge">
	<img src="https://img.shields.io/badge/2--Class-Text%20Classification-FF0080?style=for-the-badge">
	<img src="https://img.shields.io/badge/Few--Shot-Training-7928CA?style=for-the-badge">
	<img src="https://img.shields.io/badge/Apache%202.0-000000?style=for-the-badge">

	<br><br>

	### 🧠 Tiny model. Tiny dataset. Real classification.

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

	</div>

	---

	<div align="center">

	## ⚡ `TINY → FAST → USEFUL`

	</div>

	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.

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

	---

	# 📡 Model Status

	<div align="center">

	\| ⚙️ 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 \|

	</div>

	---

	# 📊 Results

	<div align="center">

	### `91.4%` Accuracy

	### `91.4%` Macro F1

	<br>

	```text
	Accuracy ██████████████████░░ 91.4%
	Macro F1 ██████████████████░░ 91.4%
	```

	</div>

	> 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

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

	### Training configuration

	```yaml
	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

	```bash
	pip install -q setfit
	```

	```python
	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

	```text
	1
	```

	---

	# 🧪 Quick Examples

	### 🚨 Spam

	```text
	CONGRATULATIONS!!!

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

	```text
	→ SPAM
	```

	---

	### ✉️ Legitimate

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

	```text
	→ HAM
	```

	---

	# 🧠 Why SetFit?

	Traditional supervised classification can require a large labeled dataset.

	SetFit takes a different route:

	```text
	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

	<div align="center">

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

	</div>

	---

	# ⚠️ 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

	<div align="center">

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

	</div>

	---

	<div align="center">

	# ⚡ 16 Examples.

	# 🧠 One Small Model.

	# 🚨 One Real Task.

	<br>

	<img src="https://img.shields.io/badge/BUILT%20BY-TinyModels-FF0080?style=for-the-badge">
	<img src="https://img.shields.io/badge/POWERED%20BY-SetFit-7928CA?style=for-the-badge">

	<br><br>

	TinyModels — building small models that actually do things.

	</div>