60 lines
2.0 KiB
Markdown
60 lines
2.0 KiB
Markdown
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---
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library_name: transformers
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license: other
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: entity_extraction_new
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# QuCo-extractor-0.5B
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[](https://arxiv.org/abs/2512.19134)
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[](https://github.com/ZhishanQ/QuCo-RAG)
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[](https://github.com/ZhishanQ/QuCo-RAG/blob/main/LICENSE)
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## Model Description
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**QuCo-extractor-0.5B** is a specialized entity extraction model fine-tuned from [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/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.
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This model is part of the QuCo-RAG project presented in:
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> **QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation**
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>
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> Dehai Min, Kailin Zhang, Tongtong Wu, Lu Cheng
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>
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> [[Paper]](https://arxiv.org/abs/2512.19134) [[Code]](https://github.com/ZhishanQ/QuCo-RAG)
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-06
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- train_batch_size: 28
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- eval_batch_size: 32
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- seed: 42
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- distributed_type: multi-GPU
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 56
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2.0
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### Framework versions
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- Transformers 4.57.1
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- Pytorch 2.5.1+cu121
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- Datasets 4.0.0
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- Tokenizers 0.22.1
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