---
language: ru
library_name: spacy
pipeline_tag: token-classification
tags:
- spacy
- ner
- token-classification
- product-classifier
- russian
license: other
metrics:
- precision
- recall
- f1
---

# Product Classifier NER v2

spaCy NER pipeline for extracting structured product attributes from Russian
product titles and catalog nomenclature.

## Model Details

- Language: `ru`
- Framework: spaCy
- Pipeline: `tok2vec`, `ner`
- Model path in source project: `models/ner-v2/model-best`
- spaCy compatibility: `>=3.8.14,<3.9.0`

## Entity Labels

The model predicts the following entity labels:

| Label | Description |
| --- | --- |
| `BRAND` | Brand, manufacturer, or vendor-like name. |
| `COLOR` | Product color. |
| `DENSITY` | Density value or density class. |
| `DIAMETER` | Diameter value. |
| `LENGTH` | Length value. |
| `MODEL` | Model, article-like model name, or product series. |
| `POWER` | Power rating. |
| `PRODUCT_TYPE` | Main product type or product noun phrase. |
| `PROTECTION_RATING` | Protection rating such as IP class. |
| `QUANTITY` | Count, pack quantity, or quantity-like value. |
| `SIZE` | Size, dimension, or size class. |
| `STANDARD` | Standard, specification, or normative class. |
| `VOLUME` | Volume value. |
| `WEIGHT` | Weight value. |

## Evaluation

Metrics are copied from the trained spaCy `meta.json`:

| Metric | Value |
| --- | ---: |
| Entity F1 | `0.8055687391` |
| Entity precision | `0.8131587352` |
| Entity recall | `0.7981191223` |

## Usage

Install the model package from Hugging Face after upload, then load it with
spaCy:

```bash
pip install https://huggingface.co/buyanov-tech/ru_ner_v2/resolve/main/ru_ner_v2-any-py3-none-any.whl
```

```python
import spacy

nlp = spacy.load("ru_ner_v2")
doc = nlp("Труба ПВХ D110 2м серая")

for ent in doc.ents:
    print(ent.text, ent.label_)
```

Example output from the local `model-best` checkpoint:

```text
Труба PRODUCT_TYPE
ПВХ SIZE
2м LENGTH
серая COLOR
```

You can also load the unpacked spaCy pipeline from a local directory:

```python
import spacy

nlp = spacy.load("models/ner-v2/model-best")
doc = nlp("Фильтр воздушный касетный ФВКас-1-590х590х45 G4")
print([(ent.text, ent.label_) for ent in doc.ents])
```

Example output:

```text
[('Фильтр воздушный касетный', 'PRODUCT_TYPE'), ('ФВКас-1-590х590х45', 'MODEL'), ('G4', 'BRAND')]
```

## Intended Use

This model is intended for internal product catalog processing, product title
normalization, feature extraction, and downstream category classification.

## Limitations

- The model is trained for Russian product/catalog titles.
- It is optimized for product attribute extraction, not general-purpose NER.
- Label meanings are domain-specific and may differ from generic NLP entity
  taxonomies.
- Predictions should be validated before use in high-impact catalog updates or
  automated data enrichment pipelines.