🥷 Language Trainer Ninja v2.2

A keyboard-first Textual NLP workstation for sentiment analysis, model training, dataset inspection, notes, history, SQLite queries, and CSV/JSON export.

v2.2 model

The bundled model is now a calibrated classical NLP pipeline rather than the earlier small Naive Bayes/logistic baseline:

Word TF-IDF (1–3 grams) ─┐
                         ├─ FeatureUnion ─ Linear SVM ─ 5-fold sigmoid calibration ─ probabilities
Char TF-IDF (3–5 grams) ─┘

Why both feature families:

• Word n-grams capture phrases such as not good, really love, and longer contrast patterns.
• Character n-grams improve handling of contractions, punctuation, spelling variation, and unseen word forms.
• A balanced Linear SVM supplies a strong decision boundary for sparse text.
• Sigmoid calibration turns SVM margins into class probabilities for the confidence display.

The starter corpus contains 1,500 balanced examples:

positive  500
neutral   500
negative  500

It includes straightforward sentiment, conversational phrasing, negation, contrast, and neutral operational language.

The built-in holdout score is useful as a regression check, but the starter corpus contains generated/template-assisted examples. Treat its very high holdout accuracy as an internal sanity metric, not as proof of production-level real-world accuracy.

Install

cd ~/Downloads/language-trainer-ninja-master
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .
language-ninja

A trained models/sentiment_model.pkl is included, so Analyze can work immediately. Use Train → Train Model after changing the dataset.

Confidence

The TUI displays all three calibrated probabilities:

Prediction: NEGATIVE • HIGH CONFIDENCE
Winning probability: 82.8%
POS 9.5%   NEU 7.6%   NEG 82.8%

UI guidance:

HIGH       75–100%
MODERATE   55–74%
LOW        below 55%

These labels describe the model's probability separation, not a guarantee of correctness.

Pages

Analyze

Paste conversational text and inspect the winning class plus positive, neutral, and negative probabilities. Every prediction is written to History.

Train

Train from a text,label CSV. The status panel reports row counts, algorithm, feature count, holdout accuracy, macro F1, and per-class precision/recall/F1.

Dataset

Inspect the examples used to train the model and export them to CSV or JSON.

Notes

Save experiment notes or attach a note to the most recent prediction.

History

Review every prediction with POS / NEU / NEG probabilities and a HIGH/MODERATE/LOW confidence level.

Database

Run read-only SELECT, WITH, and PRAGMA queries against the SQLite database.

Export

Export predictions, notes, datasets, and query results to ./exports/.

Rebuild the bundled corpus

The deterministic corpus generator is included:

python tools/build_starter_corpus.py

Then retrain from the TUI, or use the Python ML module.

Run tests

python -m pytest -q