distilbert-base-uncased-adult-article-classifier-v1

DistilBERT NSFW Article Classifier

A DistilBERT-based classifier for NSFW (Not Safe For Work) content detection in articles using domain, title, and description fields.

Model Details

• Model Type: DistilBERT with custom classification head (head swap architecture)
• Base Model: distilbert-base-uncased
• Task: Text Classification (Binary: NSFW/Not NSFW)
• Number of Classes: 2
• Labels: FALSE (Not NSFW), TRUE (NSFW)

Model Architecture

This model uses a custom architecture that:

• Loads the DistilBERT base model (without default head)
• Replaces the classification head with a custom linear layer
• Uses dropout (0.3) for regularization
• Takes structured input combining domain, title, and description fields

Usage

Using Transformers Pipeline

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="your-username/distilbert-nsfw-classifier",
    tokenizer="your-username/distilbert-nsfw-classifier"
)

# Prepare input text (combine domain, title, description)
text = """domain: example.com
title: Article Title Here
description: Article description text goes here"""

result = classifier(text)
print(result)

Using Auto Classes

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "your-username/distilbert-nsfw-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input text
text = """domain: example.com
title: Article Title Here
description: Article description text goes here"""

# Tokenize
inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    padding=True,
    max_length=512
)

# Predict
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class_id = predictions.argmax().item()
    confidence = predictions[0][predicted_class_id].item()

# Get label
id2label = model.config.id2label
predicted_label = id2label[predicted_class_id]

print(f"Predicted: {predicted_label} (confidence: {confidence:.4f})")

Input Format

The model expects input text in a structured format with three fields:

domain: example.com
title: Article Title Here
description: Article description text goes here

Important:

• Use newlines to separate fields
• Format: field_name: value
• Empty fields will be skipped automatically
• Maximum sequence length: 512 tokens

Training Details

Training Data

• Dataset: Custom NSFW classification dataset
• Format: TSV file with domain, title, description, and is_nsfw columns
• Training samples: ~1,000+
• Test accuracy: 100% (on test set)

Training Procedure

• Optimizer: AdamW
• Learning Rate: 2e-5
• Batch Size: 16
• Epochs: 3
• Max Sequence Length: 512
• Dropout Rate: 0.3

Evaluation Results

Test Accuracy: 1.0000
Test Loss: 0.0016

Classification Report:
              precision    recall  f1-score   support
       FALSE       1.00      1.00      1.00       120
        TRUE       1.00      1.00      1.00       133
    accuracy                           1.00       253

Limitations and Bias

• This model was trained on a specific dataset and may not generalize to all domains or content types
• Performance may vary on content from domains not seen during training
• The model may have biases present in the training data
• High accuracy on test set may indicate overfitting - use with caution on real-world data

Intended Use

This model is intended for:

• Content moderation systems
• Article classification pipelines
• NSFW content filtering

Not intended for:

• Real-time production systems without additional validation
• Legal or compliance decisions without human review
• Content that significantly differs from training distribution

How to Cite

@misc{distilbert-nsfw-classifier,
  title={DistilBERT NSFW Article Classifier},
  author={Your Name},
  year={2024},
  howpublished={\url{https://huggingface.co/your-username/distilbert-nsfw-classifier}}
}

License

Apache 2.0