Named entity recognition for product search queries. Identifies brand, product category, product name, and origin spans in free-text queries.
Fine-tuned from bltlab/queryner-bert-base-uncased, which was trained on Amazon ESCI queries. This model extends it with domain-specific vocabulary drawn from a European product database — brand names, multilingual product titles, and origin countries.
Labels
The model predicts the full 17-type label set from the base queryner model. The four types most relevant to product search are:
Label
HF tag
Example span
Brand
B-creator / I-creator
Ecover, Dr. Bronner's
Product category
B-core_product_type / I-core_product_type
washing up liquid, shampoo
Product name
B-product_name / I-product_name
Skin Food, Men 48H Deodorant
Origin
B-origin / I-origin
Germany, Italy
All other queryner types (modifier, department, UoM, color, material, etc.) are preserved from the base model.
Usage
Via evident_decode (recommended)
The evident_decode Python package applies required pre/post-processing on top of the raw
pipeline: apostrophe normalisation, offset-based span reconstruction, and punctuation
collapsing. Using the raw pipeline without these fixes will produce degraded output on
queries containing apostrophes (e.g. Dr. Bronner's) or diacritics (e.g. Spülmittel).
Template-generated from brand/category/product vocabulary; includes 1,000 substitute-frame (multilingual)
Synthetic examples are generated by generate_db_dataset.py from a European product database. Brand names come from EU-registered brands; product names are extracted from all language variants stored in product.name (en, de, fr, it, es, nl, and others). Product names that are exact matches of English category strings are excluded to avoid contradictory training signal.
Label balance and product name vs category
The two most commonly confused labels are core_product_type (product category) and product_name
(specific named product). The model's only reliable cue for distinguishing them is positional:
text following a known brand is a candidate for product_name, while standalone noun phrases are
typically core_product_type. This positional signal is structural, not lexical — "Dove shampoo"
and "Dove Skin Food" look identical to the model at the template level.
Why category dominates in training (~2:1 target)
Real product search queries are category-heavy by a large margin. Most users type "shampoo",
"olive oil", or "washing powder", not "Fuji Green Tea Refreshingly Hydrating Conditioner".
Training data should approximate inference-time distribution; over-representing product_name
creates a mismatch that degrades category precision on the majority of queries.
The base model (bltlab/queryner-bert-base-uncased) was trained on Amazon ESCI queries, which
are also category-heavy. The marginal value of additional core_product_type examples is lower
than the marginal value of product_name examples, but collapsing to 1:1 risks the model
labeling any noun phrase after a brand as product_name — including generic category words like
"shampoo" or "washing up liquid".
Current ratio: ~2.3:1 (core_product_type : product_name). Target: ~2:1.
Why going below 2:1 requires better data, not just more examples
Increasing product_name examples without addressing lexical quality introduces contradictory
signal:
A product named "Shampoo" and a category called "shampoo" become competing labels for the
same string. The model cannot resolve this without knowing whether the token is generic or
specific — information that is not present in the query.
The category cross-reference filter (dropping product names that are exact English category
matches) addresses the worst cases, but morphological variants ("Shampoos", "Crème") and
multi-language overlaps remain.
To move significantly below 2:1 safely, the product_name training data would need to satisfy:
Requirement
Why
Lexically distinct from category vocabulary
Prevents the model learning a single label for identical strings
High word-count names (3+ tokens)
Single and two-token product names are indistinguishable from short category slugs by surface form alone
Brand diversity
The positional cue (brand precedes product name) only generalises if many different brands are paired with many different product names — a narrow brand set leads to brand-specific memorisation
Multilingual coverage proportional to expected query mix
Training on English product names only means the model will underperform on French/German/Italian queries even though multilingual product names exist in the DB
Minimal repetition
A product name seen 20 times with the same brand drowns signal from rarer names
Until those conditions are met, product_name_ratio should stay at 0.25–0.30 and the 2:1
overall ratio maintained by generating more total synthetic examples rather than increasing the
ratio.
Training procedure
Base model: bltlab/queryner-bert-base-uncased
Tokenizer: BERT WordPiece; subword tokens after the first in each word are masked (-100)
Max sequence length: 128
Label set: collected from training data (all 17 queryner types preserved)
Optimiser: AdamW, weight decay 0.01, warmup ratio 0.1
Segmented training: brand/product/origin first, then certification O-token signal at lower LR
Ecover brand FN (4 fixtures): country_code IS NULL in DB excludes it from the training data generator.
Deutschland / Allemagne / Duitsland origin FN: generator uses English country names only.
Resolved at inference time (not model gaps):
Apostrophe artifact (dr. bronner ' s): handled by evident_decode.ner apostrophe normaliser before tokenisation.
Umlaut span mismatch (Spülmittel → spulmittel): resolved by offset-based span reconstruction in evident_decode.ner.
Quantized variant
An int8 ONNX-quantized variant (104.6 MB vs 415 MB full) is available at
thepian/product-query-ner-int8.
Uses dynamic int8 quantization via ONNX Runtime (avx512_vnni). Validated against the
acceptance fixture set (≤1% F1 regression allowed). Requires optimum[onnxruntime] to load.
Paper
A paper describing the product query decoding architecture (deterministic decoder + NER +
NLI intent) is in preparation. Link will be added here on publication.
Limitations
Extraction patterns are primarily English; avoidance frames in other languages (ohne, sans, senza) are not NER targets — they are handled by a separate parser
Multilingual product names are included in training but evaluation is English-only
Origin recognition covers ~13 European countries drawn from product records; global coverage is partial
Barcode and price extraction are not NER tasks — handled by a dedicated parser
Citation
If you use this model, please cite the base model:
@misc{queryner,
author = {Björklund, Love and Ljunglöf, Peter},
title = {QueryNER: Named Entity Recognition for Product Search Queries},
year = {2024},
publisher = {HuggingFace},
url = {https://huggingface.co/bltlab/queryner-bert-base-uncased}
}
