Hypencoder.2 Layer

🚀 Hypencoder模型

本模型是基于超网络的信息检索模型，可将文本转换为向量并输出相关性得分，适用于信息检索场景。

🚀 快速开始

使用预训练的Hypencoders作为独立模型

from hypencoder_cb.modeling.hypencoder import Hypencoder, HypencoderDualEncoder, TextEncoder
from transformers import AutoTokenizer

dual_encoder = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.6_layer")
tokenizer = AutoTokenizer.from_pretrained("jfkback/hypencoder.6_layer")

query_encoder: Hypencoder = dual_encoder.query_encoder
passage_encoder: TextEncoder = dual_encoder.passage_encoder

queries = [
    "how many states are there in india",
    "when do concussion symptoms appear",
]

passages = [
    "India has 28 states and 8 union territories.",
    "Concussion symptoms can appear immediately or up to 72 hours after the injury.",
]

query_inputs = tokenizer(queries, return_tensors="pt", padding=True, truncation=True)
passage_inputs = tokenizer(passages, return_tensors="pt", padding=True, truncation=True)

q_nets = query_encoder(input_ids=query_inputs["input_ids"], attention_mask=query_inputs["attention_mask"]).representation
passage_embeddings = passage_encoder(input_ids=passage_inputs["input_ids"], attention_mask=passage_inputs["attention_mask"]).representation

# The passage_embeddings has shape (2, 768), but the q_nets expect the shape
# (num_queries, num_items_per_query, input_hidden_size) so we need to reshape
# the passage_embeddings.

# In the simple case where each q_net only takes one passage, we can just
# reshape the passage_embeddings to (num_queries, 1, input_hidden_size).
passage_embeddings_single = passage_embeddings.unsqueeze(1)
scores = q_nets(passage_embeddings_single)  # Shape (2, 1, 1)
# [
#    [[-12.1192]],
#    [[-13.5832]]
# ]

# In the case where each q_net takes both passages we can reshape the
# passage_embeddings to (num_queries, 2, input_hidden_size).
passage_embeddings_double = passage_embeddings.repeat(2, 1).reshape(2, 2, -1)
scores = q_nets(passage_embeddings_double)  # Shape (2, 2, 1)
# [
#    [[-12.1192], [-32.7046]],
#    [[-34.0934], [-13.5832]]
# ]

✨ 主要特性

这是一个Hypencoder双编码器，包含文本编码器和Hypencoder两个主干。文本编码器将项目转换为768维向量，而Hypencoder将文本转换为一个小型神经网络，该网络以文本编码器的768维向量为输入，最终输出相关性得分。

模型变体

我们发布了论文中使用的四个模型。除了我们称为q-nets的小型神经网络具有不同数量的隐藏层外，每个模型都是相同的。

📚 详细文档

这是论文 Hypencoder: Hypernetworks for Information Retrieval 中的官方模型。若要使用此模型，请查看 Github 页面，其中包含所需代码和模型运行细节。

📄 许可证

本项目采用Apache 2.0许可证。

📖 引用

BibTeX:

@misc{killingback2025hypencoderhypernetworksinformationretrieval,
      title={Hypencoder: Hypernetworks for Information Retrieval}, 
      author={Julian Killingback and Hansi Zeng and Hamed Zamani},
      year={2025},
      eprint={2502.05364},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2502.05364}, 
}

📦 模型信息