---
language: en
license: mit
library_name: glirel
tags:
- relation-extraction
- zero-shot
- multi-domain
- glirel
- named-entity-recognition
datasets:
- custom-multi-domain
metrics:
- f1
- precision
- recall
pipeline_tag: token-classification
---

# GLiREL Multi-Domain Zero-Shot Relation Extraction

This model is a fine-tuned version of [jackboyla/glirel-large-v0](https://huggingface.co/jackboyla/glirel-large-v0) for multi-domain zero-shot relation extraction.

## Model Description

GLiREL (Generalist and Lightweight model for Relation Extraction) is a state-of-the-art model for zero-shot relation extraction. This version has been specifically fine-tuned on multi-domain data to improve performance across diverse domains in zero-shot scenarios.

## Training Data

The model was trained on a multi-domain dataset with domain-based splits to ensure true zero-shot evaluation:

- **Training Examples**: N/A
- **Training Domains**: N/A
- **Relation Types**: N/A
- **Entity Types**: N/A

## Key Features

- **Zero-shot relation extraction**: Can extract relations for unseen relation types
- **Multi-domain capability**: Trained on diverse domains for better generalization
- **Domain-based splitting**: Training and evaluation use different domains for true zero-shot evaluation
- **Lightweight**: Efficient inference while maintaining high performance

## Usage

```python
from glirel import GLiREL

# Load the model
model = GLiREL.from_pretrained("skv03/ner-span-glirel")

# Example usage
text = "John works at OpenAI in San Francisco."
labels = ["works_at", "located_in", "founded_by"]

# Extract relations
relations = model.predict_relations(text, labels)
print(relations)
```

## Training Configuration

- **Base Model**: jackboyla/glirel-large-v0
- **Training Steps**: 15,000
- **Batch Size**: 6
- **Learning Rate (Encoder)**: 1e-5
- **Learning Rate (Others)**: 5e-5
- **Max Length**: 512
- **Evaluation Strategy**: Every 4,000 steps
- **Zero-shot Setup**: Domain-based splits (no domain overlap between train/test)

## Model Architecture

- **Label Embedding Strategy**: both (label + entity token)
- **Loss Function**: Binary Cross Entropy
- **Scheduler**: Cosine with Warmup
- **Dropout**: 0.1
- **Max Types per Batch**: 50

## Performance

This model is designed for zero-shot relation extraction across multiple domains. Performance metrics will vary depending on the specific domains and relation types in your use case.

## Limitations

- Performance may vary significantly across different domains
- Best suited for English text
- Requires entity spans to be provided for relation extraction

## Citation

If you use this model, please cite the original GLiREL paper:

```bibtex
@misc{boylan2025glirelgeneralistmodel,
      title={GLiREL -- Generalist Model for Zero-Shot Relation Extraction}, 
      author={Jack Boylan and Chris Hokamp and Demian Gholipour Ghalandari},
      year={2025},
      eprint={2501.03172},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.03172},
}
```

## Model Card Authors

Created by the GLiREL fine-tuning team.