QuCo-extractor-0.5B

Model Description

QuCo-extractor-0.5B is a specialized entity extraction model fine-tuned from Qwen2.5-0.5B-Instruct for the QuCo-RAG system. This model extracts knowledge triples (entity-relation-entity) from sentences to support corpus-grounded uncertainty quantification in Retrieval-Augmented Generation.

This model is part of the QuCo-RAG project presented in:

QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation

Dehai Min, Kailin Zhang, Tongtong Wu, Lu Cheng

[Paper] [Code]

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-06
• train_batch_size: 28
• eval_batch_size: 32
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 2
• total_train_batch_size: 56
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2.0

Framework versions

• Transformers 4.57.1
• Pytorch 2.5.1+cu121
• Datasets 4.0.0
• Tokenizers 0.22.1