Kinoux/french-spam-ham-detection-free-2k

French Spam/Ham Detection Free 2K

French Spam/Ham Detection Free 2K is a free 2,000-record sample extracted from the full commercial dataset French Spam/Ham Detection (56,400 synthetic messages) provided by Kinoux.

Each entry is a synthetic French message labeled with a binary classification:

• —spam
• —ham

The data is 100% synthetic (no personal data, no scraped emails, no real platform exports) and was generated specifically for training and evaluating French-native spam detection models.

Official product page for the full dataset and model: https://kinoux.com/datasets-models/french-spam-ham-detection/

Dataset Description

Summary

This dataset contains 2,000 French messages, each annotated with a single label: spam or ham.

It is designed as a free preview of the full commercial dataset (56,400 records) used to fine-tune high-performance French spam detection classifiers.

Supported Tasks

• —Task: Spam detection / binary text classification
• —Input: French message text (text)
• —Output: Label (label) in {spam, ham}

This sample can be used to:

• —Train and evaluate French spam classifiers
• —Benchmark CamemBERT / Transformers pipelines on binary classification
• —Build email filtering prototypes
• —Create SMS or contact-form filtering systems
• —Test moderation pipelines

Languages

• —Input language: French (fr)
• —Labels: English strings (spam, ham)

Dataset Structure

Data Instances

Each line is a JSON object like:

{"text": "Félicitations ! Vous avez gagné un iPhone 15. Cliquez ici pour réclamer votre prix.", "label": "spam"}

More examples:

{"text": "Bonjour, je vous envoie le devis demandé en pièce jointe.", "label": "ham"}
{"text": "Offre exceptionnelle valable aujourd'hui seulement ! Répondez STOP pour vous désinscrire.", "label": "spam"}
{"text": "Peux-tu me rappeler quand tu as un moment ?", "label": "ham"}

Data Fields

• —text (string): Synthetic French message (email, SMS-style message, marketing text, notification, etc.).

• —label (string): Binary label with two possible values:

• —spam
• —ham

Data Splits

This free 2K sample is provided as a single split.

Users are encouraged to create their own train/validation splits (e.g. 85/15 stratified by label).

The full 56,400-record dataset (commercial) includes a recommended stratified split and additional evaluation utilities.

Data Generation and Coverage

• —All records are synthetically generated using French-native linguistic patterns.
• —No real emails, phone numbers, or personal data are included.
• —Messages simulate multiple real-world scenarios:

• —Promotional spam
• —Financial scams
• —Phishing-style messages
• —Subscription traps
• —Marketing emails
• —Legitimate personal messages
• —Business communication

The objective is to provide a clean, balanced, and legally safe corpus for training and benchmarking spam classifiers in French.

Usage

Example: Loading with datasets

from datasets import load_dataset

ds = load_dataset(
	"kinoux/french-spam-ham-detection-free-2k",
	split="train"
)

print(ds[0])
# {'text': '...', 'label': 'spam'}

Example: Training a CamemBERT binary classifier (sketch)

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer

dataset = load_dataset("kinoux/french-spam-ham-detection-free-2k", split="train")
dataset = dataset.train_test_split(test_size=0.15, stratify_by_column="label")

tokenizer = AutoTokenizer.from_pretrained("camembert-base")

def tokenize_batch(batch):
	return tokenizer(batch["text"], truncation=True, padding="max_length")

tokenized = dataset.map(tokenize_batch, batched=True)

label2id = {"ham": 0, "spam": 1}
id2label = {v: k for k, v in label2id.items()}

def encode_labels(batch):
	batch["labels"] = [label2id[s] for s in batch["label"]]
	return batch

tokenized = tokenized.map(encode_labels, batched=True)

model = AutoModelForSequenceClassification.from_pretrained(
	"camembert-base",
	num_labels=2,
	id2label=id2label,
	label2id=label2id,
)

args = TrainingArguments(
	output_dir="./spam_fr_model",
	evaluation_strategy="epoch",
	learning_rate=2e-5,
	per_device_train_batch_size=16,
	per_device_eval_batch_size=16,
	num_train_epochs=3,
	weight_decay=0.01,
)

trainer = Trainer(
	model=model,
	args=args,
	train_dataset=tokenized["train"],
	eval_dataset=tokenized["test"],
)

trainer.train()

Limitations

• —This is a small sample (2k): suitable for experimentation and benchmarking, but not sufficient alone for production-grade filtering systems.
• —Messages are synthetic; real-world performance must always be validated on in-domain data.
• —Advanced evasion techniques (obfuscation, unicode tricks, heavy slang) may not be fully represented in this sample.

For production use cases, the full 56,400-record dataset is recommended.

Full Dataset & Model (Commercial)

The full French Spam/Ham Detection product from Kinoux includes:

• —56,400 synthetic French messages (JSONL)
• —Balanced binary labels (spam, ham)
• —Recommended stratified split
• —Fine-tuned transformer model
• —ONNX export guidance
• —Clear commercial license for on-prem / private cloud usage

More details and licensing options on our website.

Licensing Information

This dataset is distributed under the Kinoux Dataset License (Version 2025-06-01).

• —You may use this sample for internal research, prototyping, model training and evaluation.
• —Redistribution of the dataset, in whole or in part, is prohibited, including uploading copies to other platforms or public repos.
• —For detailed terms, please refer to the LICENSE.md file in this repository.

To obtain commercial access to the full dataset and model, please visit our website.

Citation

If you use this dataset in a publication, product, or internal report, please cite:

Kinoux – French Spam/Ham Detection Free 2K (synthetic dataset). Available as a sample on Hugging Face. Complete product and documentation on our website.