hongducle96/vietnamese-name-gender-classification

Vietnamese Name Gender Classification

Model name: vn-gender-name-classification
Model type: Classical ML (TF-IDF + Logistic Regression + Linear SVM)
Task: Gender classification from Vietnamese full names
Author: Le Hong Duc - [email protected] - https://www.linkedin.com/in/hongduc96/

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1. Overview

This model predicts binary gender (Nam / Nữ – male/female) from Vietnamese full names. It is trained on a dataset of Vietnamese names labeled with binary gender, using character n-gram TF-IDF features and two base models:

• Logistic Regression (LR)
• Linear SVM
  This model is designed to be:
• Lightweight – classical ML, no GPUs required
• Easy to integrate – scikit-learn pipelines saved as .joblib
• Practical – includes a clear production decision rule and an ambiguity flag
• Accuracy Model: ~96.5%

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2. Training data

• Source: collection of Vietnamese full names with gender labels on a public website
• Labels: Giới tính ∈ {Nam, Nữ} (binary male/female)
• Size:
  • Training: ~50,000 labeled names
  • Test: ~15,000 names (held-out evaluation set)

2.1. Preprocessing

For each name:

• Normalize whitespace (strip, collapse multiple spaces).
• Lowercase the text, but keep Vietnamese accents (diacritics).
• Create an additional variant name_wo_surname by:
  • Splitting on spaces.
  • Dropping the first token (surname).
  • Keeping middle + given name. Example:
• "Nguyễn Hoàng Phúc" →
  • full_name_clean = "nguyễn hoàng phúc"
  • name_wo_surname = "hoàng phúc" Both forms are used for training to make the model more robust to how names are provided.

2.2. Evaluation

On the held-out test set (~15K names), with results:

• Accuracy: ~96.5%
• Proportion of names marked ambiguous: ~2.6%

  Class	Precision	Recall	F1-score	Support
  Nam	0.956	0.964	0.960	6637
  Nữ	0.972	0.965	0.968	8409
  Accuracy			0.965	15046
  Macro avg	0.964	0.965	0.964	15046
  Weighted avg	0.965	0.965	0.965	15046

Confusion matrix (Nam/Nữ) – PROD:

Actual \ Predicted	Nam	Nữ
Nam	6401	236
Nữ	298	8111

• Nam: 6,401 correctly predicted vs. 236 misclassified as “Nữ”.
• Nữ: 8,111 correctly predicted vs. 298 misclassified as “Nam”. Number of names marked ambiguous by PROD: 395 / 15046 (2.63%)

3. Model architecture

3.1. Features

Both LR and SVM use the same character-level TF-IDF features:

• TfidfVectorizer with:
  • analyzer="char_wb" (character n-grams within word boundaries)
  • ngram_range=(2, 6) (bigrams to 6-grams)
  • min_df=2 (ignore n-grams that appear in fewer than 2 documents) This allows the model to learn patterns from substrings of names, which is very effective for Vietnamese given names.

3.2. Base models

Two scikit-learn pipelines are trained:

• model_lr.joblib
  • TfidfVectorizer → LogisticRegression(max_iter=1000, n_jobs=-1)
  • Supports predict_proba for probability estimates.
• model_svm.joblib
  • TfidfVectorizer → LinearSVC
  • Strong linear classifier, but outputs only class labels (no probabilities).

3.3. Production ensemble logic (simplified)

For an input full name full_name_raw:

1. Preprocessing

   • full_name_clean = normalize_name_keep_accent(full_name_raw)
   • name_wo_surname = drop_surname(full_name_clean)

2. SVM predictions

   • SVM_FULL = model_svm.predict([full_name_clean])
   • SVM_WO = model_svm.predict([name_wo_surname])

3. LR combined probabilities

   • Get probabilities from LR on both text variants:
     • p_Nam_full, p_Nu_full = LR(full_name_clean)
     • p_Nam_wo, p_Nu_wo = LR(name_wo_surname)
   • Average the Nam probability:
     • p_Nam_avg = 0.5 * (p_Nam_full + p_Nam_wo)
     • p_Nu_avg = 1.0 - p_Nam_avg
   • LR_label = "Nam" if p_Nam_avg >= p_Nu_avg else "Nữ"
   • LR_conf = max(p_Nam_avg, p_Nu_avg)

4. Decision rule

• If SVM_FULL == SVM_WO (SVMs agree):
  • final_label = SVM_FULL
  • Mark as ambiguous if LR_conf < lr_conf_clear (default 0.60).
• If SVM_FULL != SVM_WO (SVMs disagree, hard case):
  • If LR_conf >= lr_conf_as_referee (default 0.70), follow LR:
    final_label = LR_label
  • Otherwise, default to SVM_WO:
    final_label = SVM_WO
  • Mark as ambiguous = True. The final output includes:
• final_label – production gender prediction ("Nam" or "Nữ")
• LR_conf – LR combined confidence
• is_ambiguous – whether this name is considered ambiguous by the ensemble

Intended use & limitations Intended use

• Analytics and statistical exploration of Vietnamese name data.
• Educational demos of classical ML methods on Vietnamese text.
• Lightweight baseline for experiments involving Vietnamese full names.

Limitations

• The model predicts only binary gender (Nam / Nữ) from naming patterns.
• It does not capture the full diversity of gender identities or self-determined gender.
• It may be inaccurate or biased for:
  • Rare or gender-neutral names
  • Non-Vietnamese names, abbreviations, nicknames
  • Names from under-represented regions or time periods in the training data

This model should not be used for high-stakes decisions that affect individual rights, access to services, or opportunities, or in any context where misclassification could cause harm or discrimination.