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humbleworth/
reformer-character-domain-generator

Name: reformer-character-domain-generator
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Domain Name Generator - Reformer Character-Level Model

A character-level Reformer model trained to generate domain names based on descriptive tags. The model takes a set of content and style tags as input and generates appropriate, creative domain names.

Model Description

This model is a fine-tuned version of google/reformer-enwik8 specifically adapted for domain name generation. It uses a pure tag-based approach where both content descriptors (e.g., "tech", "health") and style descriptors (e.g., "modern", "minimal") are treated as equal tags.

Key Features

Character-level generation: Generates domains character by character for maximum flexibility
Tag-based prompting: Uses 3-4 descriptive tags to guide generation
Style-aware: Understands style tags like "modern", "minimal", "playful"
Position-independent: Tag order doesn't matter due to training-time shuffling

Model Details

Architecture: Reformer with LSH attention
Base Model: google/reformer-enwik8
Model Size: ~597M parameters
Vocabulary Size: 258 (byte-level encoding)
Max Sequence Length: 256 characters
Hidden Size: 1024
Layers: 12
Attention Heads: 8

Training Details

Training Data

Primary Dataset: 250k real domains from BrandBucket
Synthetic Dataset: 1.75M AI-generated domains
Total Examples: ~2M domains
Data Split: 80% synthetic, 20% real

Training Configuration

Epochs: 5
Batch Size: 256 (128 × 2 gradient accumulation)
Learning Rate: 5e-05
Tag Dropout: 10%
Style Tag Probability: 30%
Hardware: NVIDIA H100 GPU
Training Time: 17.6 hours

Training Results

Final Training Loss: 1.1113
Best Validation Loss: 0.9716
Loss Reduction: 75%
Training Stability: std=0.0014 (very stable)

Intended Use

Primary Use Cases

Generate domain names for startups and businesses
Brainstorm creative domain ideas based on keywords
Explore domain variations with different styles

Input Format

tags: tag1;tag2;tag3 domain:

Supported Tags

Content Tags (examples):

tech, ai, startup, app, software
health, wellness, fitness, medical
eco, green, sustainable, organic
fashion, beauty, style, boutique
food, restaurant, cafe, delivery

Style Tags:

modern - Clean, contemporary
classic - Traditional, timeless
playful - Fun, casual
bold - Strong, impactful
elegant - Sophisticated, refined
techy - Technical, digital
eco - Environmental, green
luxury - Premium, high-end
minimal - Simple, short
creative - Artistic, unique
professional - Business-oriented
casual - Relaxed, informal
trendy - Current, fashionable
simple - Straightforward
unique - Distinctive

Usage

With Transformers Library

python

from transformers import ReformerModelWithLMHead, AutoTokenizer
import torch

# Load model
model = ReformerModelWithLMHead.from_pretrained("humbleworth/reformer-character-domain-generator")
model.eval()

# Character encoding (Reformer standard)
def encode_text(text):
    return [c + 2 for c in text.encode('utf-8')]

def decode_ids(ids):
    return bytes([max(0, id - 2) for id in ids if id > 2]).decode('utf-8', errors='ignore')

# Generate domain
prompt = "tags: tech;startup;modern domain:"
input_ids = torch.tensor([encode_text(prompt)])

with torch.no_grad():
    output = model.generate(
        input_ids,
        max_new_tokens=50,
        temperature=1.2,
        top_p=0.95,
        do_sample=True,
        pad_token_id=0,
        eos_token_id=2
    )

generated = decode_ids(output[0].tolist())
domain = generated.split("domain:")[-1].strip()
print(f"Generated: {domain}")

Generation Parameters

Temperature: 1.2 (recommended for creativity)
Top-p: 0.95
Max Length: 50 tokens after prompt

Examples

Input → Output Examples

tags: tech;startup;ai → techflow.ai
tags: eco;sustainable;modern → greenleaf.eco
tags: health;wellness;minimal → purelife.health
tags: fashion;luxury;elegant → velvetrose.com
tags: food;delivery;playful → snackdash.io

Limitations

Best results with 3-4 tags (trained range)
May occasionally generate non-standard TLDs
Domain availability not guaranteed
Works best with English keywords

Ethical Considerations

Generated domains should be checked for trademark conflicts
May reflect biases present in training data
Should not be used to generate misleading or deceptive domains

Model Card Contact

For questions or issues, please open an issue in the repository.

Citation

If you use this model, please cite:

bibtex

@software{domain_generator_reformer,
  title = {Domain Generator - Character-Level Reformer},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/humbleworth/reformer-character-domain-generator}
}

Changelog

v1.0 (2024-01): Initial release
- 5 epochs training on combined dataset
- 0.9716 validation loss
- Stable generation quality