README.md

metadata

language:
  - en
license: cc-by-4.0
tags:
  - relation-extraction
  - knowledge-graph
  - triplet-extraction
  - REBEL
  - quantum-physics
  - seq2seq
base_model: Babelscape/rebel-large
datasets:
  - konsman/quantum-rebel-mixed-training
pipeline_tag: text-generation
widget:
  - text: >-
      Albert Einstein was a German-born physicist who developed the theory of
      relativity.
    example_title: Einstein Example
  - text: >-
      Quantum entanglement is a physical phenomenon that occurs when pairs of
      particles interact.
    example_title: Quantum Physics Example

REBEL Quantum Physics (Mixed Training)

This is a fine-tuned REBEL model for relation extraction and knowledge graph triplet generation, specialized for quantum physics domain while maintaining general knowledge extraction capabilities.

Model Description

REBEL (Relation Extraction By End-to-end Language generation) is a seq2seq model that performs end-to-end relation extraction for more than 200 different relation types. This model has been fine-tuned on a mixed dataset combining domain-specific quantum physics triplets with general knowledge triplets.

• Base Model: Babelscape/rebel-large
• Fine-tuned on: Mixed dataset (quantum physics + general REBEL data)
• Training Data: ~203k examples (191k train, 6k val, 6k test)
• Language: English
• Task: Relation Extraction / Knowledge Graph Triplet Generation

Training Data

The model was fine-tuned on a carefully curated mixed dataset:

• Domain-specific data: ~48,000 quantum physics triplets
• General data: ~144,000 general knowledge triplets (1:3 ratio)
• Validation: ~6,000 domain-only quantum physics examples
• Test: ~6,000 domain-only quantum physics examples

This mixed training approach allows the model to:

1. Excel at quantum physics domain extraction
2. Maintain strong general knowledge extraction capabilities
3. Avoid catastrophic forgetting of general relations

Usage

Direct Inference

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "konsman/rebel-quantum-mixed"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

text = "Quantum entanglement is a physical phenomenon that occurs when pairs of particles interact."

# Tokenize input
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=256)

# Generate triplets
outputs = model.generate(
    inputs["input_ids"],
    max_length=256,
    num_beams=5,
    early_stopping=True
)

# Decode output
triplets_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
print(triplets_text)

Parsing Triplets

import re

def extract_triplets(text):
    """Extract structured triplets from REBEL output."""
    triplets = []
    pattern = r'<triplet> (.+?) <subj> (.+?) <obj> (.+?)(?=<triplet>|</s>|$)'

    for match in re.finditer(pattern, text):
        subject = match.group(1).strip()
        obj = match.group(2).strip()
        relation = match.group(3).strip()
        triplets.append((subject, obj, relation))

    return triplets

# Parse the output
triplets = extract_triplets(triplets_text)
for subj, obj, rel in triplets:
    print(f"({subj} ; {obj} ; {rel})")

Output Format

The model generates triplets in the following format:

<triplet> SUBJECT <subj> OBJECT <obj> RELATION <triplet> ...

Example output:

<triplet> Albert Einstein <subj> German <obj> country of citizenship <triplet> theory of relativity <subj> Albert Einstein <obj> discoverer or inventor

Evaluation

The model achieves strong performance on both domain-specific and general relation extraction tasks due to the mixed training approach.

Intended Use

• Knowledge graph construction from scientific texts
• Relation extraction from quantum physics literature
• General purpose triplet extraction
• Domain adaptation for information extraction

Limitations

• Primarily trained on English text
• May have reduced performance on domains very different from quantum physics and general Wikipedia-style text
• Triplet extraction quality depends on input text quality and clarity

Training Details

• Base Model: Babelscape/rebel-large
• Training Framework: PyTorch Lightning
• Training Hardware: NVIDIA H200 GPU
• Batch Size: 1 (with gradient accumulation)
• Optimizer: AdamW
• Learning Rate: 3e-5
• Epochs: 3
• Mixed Precision: bf16

Citation

If you use this model, please cite:

@model{rebel_quantum_mixed,
  author = {Konsman},
  title = {REBEL Quantum Physics (Mixed Training)},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/konsman/rebel-quantum-mixed}
}

Also cite the original REBEL paper:

@inproceedings{huguet-cabot-navigli-2021-rebel-relation,
    title = "{REBEL}: Relation Extraction By End-to-end Language generation",
    author = "Huguet Cabot, Pere-Lluis and Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.204",
    pages = "2370--2381",
}

License

CC-BY-4.0

Contact

For questions or issues, please open an issue on the model repository.