tfidf_vectorizer.pkl — TF-IDF vectorizer (15k features, bigrams, sublinear TF)
mlb.pkl — MultiLabelBinarizer (28 GoEmotions labels)
logistic_regression.pkl — OneVsRest Logistic Regression
linearsvc.pkl — OneVsRest LinearSVC (Platt-calibrated)
xgboost.pkl — OneVsRest XGBoost
thresholds_lr.pkl — per-class optimal thresholds for LR (numpy array, shape 28)
thresholds_linearsvc.pkl — per-class optimal thresholds for LinearSVC (numpy array, shape 28)

Usage

python

import joblib, numpy as np

tfidf      = joblib.load("tfidf_vectorizer.pkl")
mlb        = joblib.load("mlb.pkl")
model      = joblib.load("xgboost.pkl")        # or logistic_regression / linearsvc
thresholds = joblib.load("thresholds_lr.pkl")  # for LR; skip for XGBoost (use 0.30)

X = tfidf.transform(["I am so happy and grateful today!"])
proba  = model.predict_proba(X)
preds  = (proba >= thresholds).astype(int)     # shape (1, 28)
labels = mlb.classes_[preds[0].astype(bool)]
print(labels)

YatishW79/classical-ml-goemotions

Classical ML — GoEmotions Emotion Classifier

Models

Files

Usage

YatishW79/
classical-ml-goemotions