Bmretriever 410M

🚀 BMRetriever-410M 模型

该模型是基于论文 BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers（发表于 EMNLP 2024）中描述的方法进行微调的。相关的 GitHub 仓库可在 此处 查看。此模型具有 4.1 亿个参数，更多详细信息请参阅论文 链接。

📄 许可证

本项目采用 MIT 许可证。

📦 训练数据集

• MedRAG/textbooks
• MedRAG/pubmed
• MedRAG/statpearls
• mteb/raw_biorxiv
• mteb/raw_medrxiv
• ms_marco
• BMRetriever/biomed_retrieval_dataset

🚀 快速开始

预训练模型可以通过 HuggingFace 的 transformers 库进行加载。

💻 使用示例

基础用法

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("BMRetriever/BMRetriever-410M") 
tokenizer = AutoTokenizer.from_pretrained("BMRetriever/BMRetriever-410M") 

高级用法

以下代码展示了如何获取不同句子的嵌入向量以及计算句子之间的相似度得分：

import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        embedding = last_hidden[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden.shape[0]
        embedding = last_hidden[torch.arange(batch_size, device=last_hidden.device), sequence_lengths]
    return embedding

def get_detailed_instruct_query(task_description: str, query: str) -> str:
    return f'{task_description}\nQuery: {query}'

def get_detailed_instruct_passage(passage: str) -> str:
    return f'Represent this passage\npassage: {passage}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a scientific claim, retrieve documents that support or refute the claim'
queries = [
    get_detailed_instruct_query(task, 'Cis-acting lncRNAs control the expression of genes that are positioned in the vicinity of their transcription sites.'),
    get_detailed_instruct_query(task, 'Forkhead 0 (fox0) transcription factors are involved in apoptosis.')
]

# No need to add instruction for retrieval documents
documents = [
    get_detailed_instruct_passage("Gene regulation by the act of long non-coding RNA transcription Long non-protein-coding RNAs (lncRNAs) are proposed to be the largest transcript class in the mouse and human transcriptomes. Two important questions are whether all lncRNAs are functional and how they could exert a function. Several lncRNAs have been shown to function through their product, but this is not the only possible mode of action. In this review we focus on a role for the process of lncRNA transcription, independent of the lncRNA product, in regulating protein-coding-gene activity in cis. We discuss examples where lncRNA transcription leads to gene silencing or activation, and describe strategies to determine if the lncRNA product or its transcription causes the regulatory effect."),
    get_detailed_instruct_passage("Noncoding transcription at enhancers: general principles and functional models. Mammalian genomes are extensively transcribed outside the borders of protein-coding genes. Genome-wide studies recently demonstrated that cis-regulatory genomic elements implicated in transcriptional control, such as enhancers and locus-control regions, represent major sites of extragenic noncoding transcription. Enhancer-templated transcripts provide a quantitatively small contribution to the total amount of cellular nonribosomal RNA; nevertheless, the possibility that enhancer transcription and the resulting enhancer RNAs may, in some cases, have functional roles, rather than represent mere transcriptional noise at accessible genomic regions, is supported by an increasing amount of experimental data. In this article we review the current knowledge on enhancer transcription and its functional implications.")
]
input_texts = queries + documents

max_length = 512

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length-1, padding=True, truncation=True, return_tensors='pt')

# Important! Adding EOS token at the end
batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt').to("cuda")

model.eval()
with torch.no_grad():
    outputs = model(**batch_dict)
    embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())

📚 引用

如果您觉得这个仓库有帮助，请考虑引用相应的论文，谢谢！

@inproceedings{xu2024bmretriever,
      title={BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers}, 
      author={Ran Xu and Wenqi Shi and Yue Yu and Yuchen Zhuang and Yanqiao Zhu and May D. Wang and Joyce C. Ho and Chao Zhang and Carl Yang},
      year={2024},
      booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
}