104 lines
3.2 KiB
Markdown
104 lines
3.2 KiB
Markdown
---
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license: mit
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metrics:
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- f1
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- exact_match
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: question-answering
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library_name: transformers
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---
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# EviOmni-nq_train-1.5B
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## Introduction
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EviOmni is a rational evidence extraction model. Compared to vanilla evidence extraction models, EviOmni demonstrates the superiority in terms of performance, generalization, efficiency, and robustness.
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## Requirements
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The code of EviOmni has been in the latest Huggingface `transformers` and we advise you to use the latest version of `transformers`.
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With `transformers<4.37.0`, you will encounter the following error:
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KeyError: 'qwen2'
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## Quickstart
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers import StoppingCriteria, StoppingCriteriaList
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import re
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class MultiTokenStoppingCriteria(StoppingCriteria):
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def __init__(self, stop_ids, device):
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self.stop_ids = stop_ids
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self.stop_len = len(stop_ids)
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def __call__(self, input_ids, scores, **kwargs):
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if len(input_ids[0]) >= self.stop_len:
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last_tokens = input_ids[0][-self.stop_len:].tolist()
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return last_tokens == self.stop_ids
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return False
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model_name = "HIT-TMG/EviOmni-nq_train-1.5B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = open("eviomni_prompt", "r").read()
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question = "..."
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passages = "..."
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instruction = prompt.format(question=question, passages=passages)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": instruction}
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]
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stop_token = "</extract>\n\n"
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stop_ids = tokenizer.encode(stop_token, add_special_tokens=False)
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stopping_criteria = StoppingCriteriaList([
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MultiTokenStoppingCriteria(stop_ids, model.device)
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])
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512,
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stopping_criteria=stopping_criteria
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
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match = re.search(r"<extract>(.*?)</extract>", response, re.DOTALL)
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evidence = match.group(1).strip()
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## Performance
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Main results.
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## Citation
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If you find our work helpful, feel free to give us a cite.
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@misc{EviOmni,
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title={Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation},
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author={Xinping Zhao and Shouzheng Huang and Yan Zhong and Xinshuo Hu and Meishan Zhang and Baotian Hu and Min Zhang},
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year={2025},
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eprint={2507.15586},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2507.15586},
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} |