---
tags:
- transformers
- question-answering
- russian
- constructicon
- nlp
- linguistics
base_model: DeepPavlov/xlm-roberta-large-en-ru
language:
- ru
pipeline_tag: question-answering
widget:
- text: "Что вам здесь нужно?"
  context: "Что вам здесь нужно?"
  example_title: "Question construction span"
- text: "Петр так и замер на месте."
  context: "Петр так и замер на месте."
  example_title: "Adverbial construction span"
---

# Russian Constructicon Span Predictor

A question-answering model for identifying construction spans in Russian text. Fine-tuned to locate specific Constructicon pattern implementations within text examples.

## Model Details

- **Base model:** [DeepPavlov/xlm-roberta-large-en-ru](https://huggingface.co/DeepPavlov/xlm-roberta-large-en-ru)
- **Task:** Question Answering / Span Prediction
- **Language:** Russian
- **Training:** QA task where context=example, question=construction pattern, answer=construction span

## Usage

### Primary Usage (RusCxnPipe Library)

This model is designed for use with the [RusCxnPipe](https://github.com/your-username/ruscxnpipe) library:

```python
from ruscxnpipe import SpanPredictor

predictor = SpanPredictor(model_name="Futyn-Maker/ruscxn-span-predictor")

examples_with_patterns = [
    {
        "example": "Что вам здесь нужно?",
        "patterns": [{"id": "pattern1", "pattern": "что NP-Dat Cop нужно?"}]
    }
]

results = predictor.predict_spans(examples_with_patterns)
span_info = results[0]['patterns'][0]['span']
print(f"Span: '{span_info['span_string']}' at {span_info['span_start']}-{span_info['span_end']}")
```

### Direct Usage (SimpleTransformers)

```python
from simpletransformers.question_answering import QuestionAnsweringModel

model = QuestionAnsweringModel('xlmroberta', 'Futyn-Maker/ruscxn-span-predictor')

# Format: context = Russian text, question = construction pattern
to_predict = [
    {
        "context": "Что вам здесь нужно?",
        "qas": [
            {
                "question": "что NP-Dat Cop нужно?",
                "id": "0"
            }
        ]
    }
]

predictions, _ = model.predict(to_predict)
print(f"Predicted span: {predictions[0]['answer'][0]}")
```

## Training Data

The model was trained on a question-answering dataset where:
- **Context:** Russian text examples containing constructions
- **Question:** Constructicon patterns
- **Answer:** Text spans implementing the construction in the example

## Output

Returns the exact text span where a construction pattern is realized in the input text, including start and end character positions.

## Framework Versions

- SimpleTransformers: 0.70.1
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu126