Futyn-Maker
/

ruscxn-classifier

@@ -1,199 +1,97 @@
 ---
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 ---
+tags:
+- transformers
+- text-classification
+- russian
+- constructicon
+- nlp
+- linguistics
+base_model: intfloat/multilingual-e5-large
+language:
+- ru
+pipeline_tag: text-classification
+widget:
+- text: "passage: NP-Nom так и VP-Pfv[Sep]query: Петр так и замер."
+  example_title: "Positive example"
+- text: "passage: NP-Nom так и VP-Pfv[Sep]query: Мы хорошо поработали."
+  example_title: "Negative example"
+- text: "passage: мягко говоря, Cl[Sep]query: Мягко говоря, это была ошибка."
+  example_title: "Positive example"
 ---
+# Russian Constructicon Classifier
+A binary classification model for determining whether a Russian Constructicon pattern is present in a given text example. Fine-tuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) in two stages: first as a semantic model on Russian Constructicon data, then for binary classification.
 ## Model Details
+- **Base model:** intfloat/multilingual-e5-large
+- **Task:** Binary text classification
+- **Language:** Russian
+- **Training:** Two-stage fine-tuning on Russian Constructicon data
+## Usage
+### Primary Usage (RusCxnPipe Library)
+This model is designed for use with the [RusCxnPipe](https://github.com/Futyn-Maker/ruscxnpipe) library:
+```python
+from ruscxnpipe import ConstructionClassifier
+classifier = ConstructionClassifier(
+    model_name="Futyn-Maker/ruscxn-classifier"
+)
+# Classify candidates (output from semantic search)
+queries = ["Петр так и замер."]
+candidates = [[{"id": "pattern1", "pattern": "NP-Nom так и VP-Pfv"}]]
+results = classifier.classify_candidates(queries, candidates)
+print(results[0][0]['is_present'])  # 1 if present, 0 if absent
+```
+### Direct Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model = AutoModelForSequenceClassification.from_pretrained("Futyn-Maker/ruscxn-classifier")
+tokenizer = AutoTokenizer.from_pretrained("Futyn-Maker/ruscxn-classifier")
+# Format: "passage: [pattern][Sep]query: [example]"
+text = "passage: NP-Nom так и VP-Pfv[Sep]query: Петр так и замер."
+inputs = tokenizer(text, return_tensors="pt", truncation=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+    prediction = torch.softmax(outputs.logits, dim=-1)
+    is_present = torch.argmax(prediction, dim=-1).item()
+print(f"Construction present: {is_present}")  # 1 = present, 0 = absent
+```
+## Input Format
+The model expects input in the format: `"passage: [pattern][Sep]query: [example]"`
+- **query:** The Russian text to analyze
+- **passage:** The constructicon pattern to check for
+## Training
+1. **Stage 1:** Semantic embedding training on Russian Constructicon examples and patterns
+2. **Stage 2:** Binary classification fine-tuning to predict construction presence
+## Output
+- **Label 0:** Construction is NOT present in the text
+- **Label 1:** Construction IS present in the text
+## Framework Versions
+- Transformers: 4.51.3
+- PyTorch: 2.7.0+cu126
+- Python: 3.10.12
+```