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# chiliground-base-modernbert-v1

A sentence classification model for extracting relevant spans from documents based on a question.

## Model Details
- Base model: answerdotai/ModernBERT-base
- Hidden dimension: 768
- Number of labels: 2
- Best validation F1: 0.7038
- Saved on: 2025-03-29 19:17:14

## Usage

```python
from transformers import AutoTokenizer
from verbatim_rag.extractor_models.model import QAModel
from verbatim_rag.extractors import ModelSpanExtractor
from verbatim_rag.document import Document

# Initialize the extractor
extractor = ModelSpanExtractor(
    model_path="chiliground-base-modernbert-v1",
    threshold=0.5
)

# Create documents
documents = [
    Document(
        content="Climate change is a significant issue. Rising sea levels threaten coastal areas.",
        metadata={"source": "example"}
    )
]

# Extract relevant spans
question = "What are the effects of climate change?"
results = extractor.extract_spans(question, documents)

# Print the results
for doc_content, spans in results.items():
    for span in spans:
        print(f"- {span}")
```

## Training Data

This model was trained on a QA dataset to classify sentences as relevant or not relevant to a given question.

## Limitations

- The model works at the sentence level and may miss relevant spans that cross sentence boundaries
- Performance depends on the quality and relevance of the training data
- The model is designed for English text only