Tiny offline Python name-gender classifier. Predicts gender associations from names using ML. 49KB model, CPU only, no API needed.
pip install genderfluid-tiny
Documentation | PyPI | GitHub
genderfluid-tiny is a lightweight Python library that predicts whether a name is statistically associated with feminine or masculine naming conventions. It uses a character n-gram classifier trained on 102,927 real names from U.S. Social Security Administration data (1880-2020) and Census 2020 records.
Unlike API-based gender detection services, genderfluid-tiny runs entirely offline. No data leaves your machine. No API key required. The entire model is 49KB.
| Property | Value |
|---|---|
| Architecture | Character n-gram + logistic regression |
| Model size | 49 KB (0.05 MB) |
| Training data | 102,927 names (SSA + Census) |
| Inference | CPU only, no GPU needed |
| Internet | Not required |
| License | Polyform Noncommercial |
| Python | 3.10+ |
pip install genderfluid-tinyThat's it. The genderfluid command and Python API are available immediately.
genderfluid predict "Emma"Name: Emma
Girl-associated: 97.5%
Boy-associated: 0.0%
Uncertain: 2.5%
Classification: girl-associated
Confidence: high
from genderfluid import classify_name, is_girl_name, is_boy_name, name_probability
classify_name("Emma") # "girl-associated"
classify_name("James") # "boy-associated"
classify_name("Alex") # "uncertain"
is_girl_name("Emma") # True
is_boy_name("James") # True
name_probability("Emma") # 0.9731from genderfluid import predict_name, predict_names
result = predict_name("Isabella")
# {"name": "Isabella",
# "girl_associated_probability": 0.8929,
# "boy_associated_probability": 0.0486,
# "uncertain_probability": 0.0585,
# "classification": "girl-associated",
# "confidence": "medium"}
results = predict_names(["Emma", "James", "Alex"])
for r in results:
print(f"{r['name']}: {r['classification']}")from genderfluid import GenderfluidModel
model = GenderfluidModel() # loads once, cached
model.predict("Olivia")
model.predict_batch(["Emma", "James", "Alex", "Max", "Taylor"])genderfluid predict "Olivia" # human-readable
genderfluid predict --json "Alex" # JSON output
genderfluid predict --compare "Emma" "James" "Alex" # comparison table
genderfluid predict --file names.txt # batch from file
genderfluid interactive # interactive mode
genderfluid stats # model info
genderfluid benchmark # performance testInput name
|
Unicode normalization + lowercase
|
Character n-gram extraction (2-5 grams)
|
Hashing trick (4096-dim feature vector)
|
Logistic regression (3 classes)
|
Sigmoid calibration
|
Output: girl-associated / boy-associated / uncertain
The classifier extracts character-level patterns from names. Names ending in -a, -ia, -ine tend to be feminine. Names ending in -o, -us, -er tend to be masculine. The model learns these patterns from real data rather than hard-coding rules.
Tested on held-out test data (10,294 names):
| Metric | Value |
|---|---|
| Accuracy | 68.9% |
| Macro F1 | 0.629 |
| Girl-associated F1 | 0.844 |
| Boy-associated F1 | 0.664 |
| Uncertain F1 | 0.380 |
The model is trained on U.S./English naming conventions. Accuracy varies by cultural context.
Measured on Intel Celeron N4000 @ 1.10GHz:
Model size: 49 KB
Loading time: 0.3 ms
Single name: 0.93 ms
Batch (100): 18.7 ms (5,335 names/sec)
Batch (1000): 180.1 ms (5,551 names/sec)
Run genderfluid benchmark on your own hardware.
- Data pipelines: Classify gender associations in CSV/spreadsheet data
- Name validation: Check if a name follows typical gender patterns
- Research: Analyze naming trends across datasets
- Privacy-sensitive applications: Process names without sending data to external APIs
- Offline applications: Works without internet connectivity
- Embedded systems: 49KB model runs on low-resource devices
Built from real public data:
- U.S. Social Security Administration baby names (1880-2020): 100,364 unique names
- U.S. Census Bureau 2020 Census first names: 53,616 unique names
Combined: 102,927 names with 50+ occurrences. Names with 85%+ statistical association are labeled girl-associated or boy-associated. Below that threshold: uncertain.
python process_real_data.py # download and process SSA + Census data
python prepare_data.py # validate and split data
python train.py # train and save model
python evaluate.py # evaluate on validation/test splitsJSONL, one entry per line:
{"name": "Emma", "label": "girl-associated"}
{"name": "James", "label": "boy-associated"}
{"name": "Alex", "label": "uncertain"}Optional fields: weight, country, language, year.
| Feature | genderfluid-tiny | gender-guesser | chicksexer |
|---|---|---|---|
| Model size | 49 KB | 600 KB+ | 10 MB+ |
| License | Polyform NC | GPLv3 | -- |
| Last updated | 2026 | 2016 | -- |
| Approach | ML (n-gram + LR) | Lookup table | ML |
| Uncertain category | Yes | Partial | No |
| pip install | Yes | Yes | Yes |
| Offline | Yes | Yes | Yes |
- Works with full names: first, middle, and last
- U.S./English-centric training data
- Name associations vary by culture, language, and generation
- The
uncertaincategory exists for genuinely ambiguous names - Not suitable for high-stakes decisions
All inference runs locally. Names are not transmitted to any external service. Logging of names is disabled by default.
102,927 names from U.S. Social Security Administration baby names (1880-2020) and Census 2020 records.
68.9% accuracy on held-out test data. Girl-associated names: 84% F1. Boy-associated names: 66% F1. Uncertain/ambiguous names: 38% F1.
Yes. After pip install genderfluid-tiny, no internet connection is needed.
Python 3.10, 3.11, 3.12, 3.13.
Yes. See the Training from source section above. The training pipeline is included.
The model is trained on U.S./English naming data. It may not work well for names from other cultural contexts. The preprocessing preserves Unicode characters, so names with accents and special characters are handled.
genderfluid-tiny/
├── genderfluid/ # Python package
│ ├── __init__.py # Public API
│ ├── cli.py # Command-line interface
│ ├── inference.py # GenderfluidModel class
│ ├── classifier.py # Logistic regression + calibration
│ ├── features.py # Character n-gram extraction
│ ├── preprocessing.py # Name normalization
│ └── model_io.py # Binary save/load
├── data/ # Training dataset
├── models/ # Trained model
├── native/ # C++ inference (optional)
├── tests/ # 29 tests
├── pyproject.toml # Package config
└── README.md
Polyform Noncommercial License 1.0.0. Free for personal, educational, and noncommercial use. Commercial use requires a license. See COMMERCIAL_LICENSE.md.