metadata
language:
- en
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- lora
- adapter
- text-generation
- knowledge-graph
- information-extraction
- rdf
- fine-tuned
- peft
- trustworthy-ai
- hallucination-detection
datasets:
- BSVGK/Text_to_KG_Construction_Dataset
pipeline_tag: text-generation
Phi-3.5 Mini Instruct — LoRA Adapter (Text-to-KG)
Model Summary
This is the LoRA adapter for the Phi-3.5 Mini Instruct model fine-tuned to extract structured RDF knowledge graph triples from UK government procurement contract text.
For the full merged model ready for inference, use: 👉 BSVGK/phi35-mini-lora-text2kg-merged
Key Results
| Metric | Score |
|---|---|
| F1 Score | 0.9954 |
| BERTScore F1 | 0.9997 |
| Hallucination Rate | 0.00% (Zero) |
| Test Contracts | 1,387 unseen contracts |
Model Details
- Base Model: microsoft/Phi-3.5-mini-instruct
- Adapter Type: LoRA (Low-Rank Adaptation)
- Task: Text-to-KG — RDF triple extraction from contract text
- Domain: UK Government Procurement Contracts
- Training Dataset: 9,244 verified UK contracts
- Hardware: NVIDIA A100
- Framework: PyTorch, Hugging Face PEFT, TRL, SFTTrainer
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3.5-mini-instruct"
)
tokenizer = AutoTokenizer.from_pretrained(
"microsoft/Phi-3.5-mini-instruct"
)
# Load LoRA adapter
model = PeftModel.from_pretrained(
base_model,
"BSVGK/phi35-mini-lora-text2kg-adapter"
)
prompt = """Extract RDF triples from the following UK government contract:
Contract: [paste your contract text here]
RDF Triples:"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))