Hebrew Grocery Item Classifier v2

Fine-tuned multilingual-MiniLMv2-L6-mnli-xnli for classifying Hebrew grocery shopping list items into 12 supermarket categories. This is an improved version over v1 with a larger, more diverse training set and targeted augmentation for weak classes. finetune code at https://github.com/davidit17/shopping_list_bot

What's new in v2

Larger dataset — 3043 total items (2434 train / 609 test), up from ~600 in v1
4 training sources — manual lists, Excel grocery data, Gemini-labelled web data, and a ChatGPT-labelled set
Synthetic augmentation — rule-based Hebrew item generator to balance under-represented categories
Weak-class booster — extra hand-curated examples for משקאות, קפואים וסלטים, and רטבים וממרחים
Per-class F1 tracking — evaluation now reports per-category F1 to surface weak spots

Usage

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model=f"davidit17/minilm-grocery-hebrew-v2",
)

items = ["גבינה צהובה", "עגבניות שרי", "שמפו", "קוקה קולה", "חזה עוף"]
for item in items:
    result = classifier(item)[0]
    print(item, "→", result["label"], f"({result['score']:.2f})")

With confidence threshold (recommended)

CONFIDENCE_THRESHOLD = 0.5

def predict(text: str) -> str:
    result = classifier(text)[0]
    return result["label"] if result["score"] >= CONFIDENCE_THRESHOLD else "אחר"

Training

Parameter	Value
Base model	multilingual-MiniLMv2-L6-mnli-xnli
Max epochs	50 (early stopping, patience=8)
Learning rate	2e-5
Weight decay	0.01
Warmup ratio	0.1
Batch size	16
Max sequence length	32
Train / test split	80% / 20% stratified
Hardware	GPU (CUDA)

Data sources

Source	Description
Source A	Manual Hebrew grocery lists from Israeli forum
Source B	Excel-format grocery list, cleaned and melted
Source C	Web grocery list labelled with Gemini
Source D	ChatGPT-labelled items across all 12 categories
Source E	Synthetic items generated with rule-based Hebrew augmentor
Source F	Hand-curated booster set for weak categories

Evaluation

Overall

Metric	Base (zero-shot)	Fine-tuned v2
Accuracy	`0.1494`	`0.8818`
Weighted F1	`0.1363`	`0.8808`

Per-class F1 (fine-tuned v2)

Category	F1
`מוצרי חלב וביצים`	`0.9057`
`ירקות ופירות`	`0.8696`
`לחם מאפים ודגנים`	`0.8767`
`חטיפים ומתוקים`	`0.9091`
`ניקיון טיפוח וחד פעמי`	`0.9020`
`משקאות`	`0.8750`
`מוצרים לאפייה`	`0.8687`
`בשר ודגים`	`0.9333`
`קפואים וסלטים`	`0.8632`
`יבשים`	`0.8000`
`רטבים וממרחים`	`0.8913`
`אחר`	`0.9024`

Limitations

Primarily covers Israeli supermarket conventions and Hebrew product naming.
Brand-specific or very niche items may fall back to אחר.
Low-confidence predictions (score < 0.5) should be treated as אחר.
Synthetic training examples may not fully reflect natural shopping list variation.
The אחר category is a catch-all and may have lower precision than domain-specific categories.

davidit17/minilm-grocery-hebrew-v2