Lenovo-Zhihui/Zhihui_LLM_Embedding

Zhihui_LLM_Embedding

Model Introduction

Zhihui_LLM_Embedding is an embedding model specifically designed to enhance Chinese text retrieval capabilities. It is built on a 7B LLM and enhanced bidirectional attention mechanism to improved contextual understanding. The model is trained on an extensive corpus from various fields within an extremely large batch. Zhihui_LLM_Embedding excels in retrieval tasks, ranking 1st position on the C-MTEB leaderboard with a leading performance score of 76.74 as of June 25, 2024.

Optimization points

• Data source enhancement: Leverages the knowledge of LLMs through three types of distillation methods.(GPT3.5 & GPT4)
  • Data Refinement: LLM scores candidate positive passages to select the most relevant examples.
  • Query Rewriting: LLM generates queries that can be answered by positive documents but are unrelated to negatives, thus enhancing the query's quality and diversity.
  • Query Expansion: Queries are expanded based on multiple topics for long documents.
• Negative example mining: Use multiple methods and different ranges of negative selection to mine hard negative examples.
• Improved Contrastive Loss: Design a novel InfoNCE loss assigns higher weights to the harder negative examples to improve the fine-grained feature representation of the model.
• Bidirectional-attention: Remove the causal attention of LLMs during contrastive training of decoder-only LLM to produce rich contextualized representations.
• Training efficiency: Using Gradient Cache to scale contrastive learning batches beyond GPU memory constraints allows the model to learn from more challenging negative examples.
• Others： Dataset-Homogenous Batching、cross-batch negative sampling

Model Details

• Base Decoder-only LLM: gte-Qwen2-7B-instruct
• Pooling Methods: Last token
• Embedding Dimension: 3584

Usage

Requirements

transformers>=4.40.2
flash_attn>=2.5.8
sentence-transformers>=2.7.0

How to use

Here is an example of how to encode queries and passages using Huggingface-transformer and Sentence-transformer.

Usage (HuggingFace Transformers)

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:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]


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



task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, "国家法定节假日共多少天"),
    get_detailed_instruct(task, "如何查看好友申请")
]

documents = [
    "一年国家法定节假日为11天。根据公布的国家法定节假日调整方案，调整的主要内容包括：元旦放假1天不变；春节放假3天，放假时间为农历正月初一、初二、初三；“五一”国际劳动节1天不变；“十一”国庆节放假3天；清明节、端午节、中秋节增设为国家法定节假日，各放假1天(农历节日如遇闰月，以第一个月为休假日)。3、允许周末上移下错，与法定节假日形成连休。",
    "这个直接去我的QQ中心不就好了么那里可以查到 我的好友单向好友好友恢复、 以及好友申请 啊可以是你加别人的 或 别人加你的都可以查得到QQ空间里 这个没注意 要有的话也会在你进空间的时候会提示你的QQ 空间里 上面消息 就可以看见了！望采纳！谢谢这个直接去我的QQ中心不就好了么那里可以查到 我的好友单向好友好友恢复、 以及好友申请 啊可以是你加别人的 或 别人加你的都可以查得到",
]
input_texts = queries + documents

tokenizer = AutoTokenizer.from_pretrained('Lenovo-Zhihui/Zhihui_LLM_Embedding', trust_remote_code=True)
model = AutoModel.from_pretrained('Lenovo-Zhihui/Zhihui_LLM_Embedding', trust_remote_code=True)

max_length = 512

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())

Usage (Sentence-Transformers)

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Lenovo-Zhihui/Zhihui_LLM_Embedding", trust_remote_code=True)
model.max_seq_length = 512
# 数据来源DuRetrieval https://huggingface.co./datasets/C-MTEB/DuRetrieval
queries = [
    "国家法定节假日共多少天",
    "如何查看好友申请",
]
documents = [
    "一年国家法定节假日为11天。根据公布的国家法定节假日调整方案，调整的主要内容包括：元旦放假1天不变；春节放假3天，放假时间为农历正月初一、初二、初三；“五一”国际劳动节1天不变；“十一”国庆节放假3天；清明节、端午节、中秋节增设为国家法定节假日，各放假1天(农历节日如遇闰月，以第一个月为休假日)。3、允许周末上移下错，与法定节假日形成连休。",
    "这个直接去我的QQ中心不就好了么那里可以查到 我的好友单向好友好友恢复、 以及好友申请 啊可以是你加别人的 或 别人加你的都可以查得到QQ空间里 这个没注意 要有的话也会在你进空间的时候会提示你的QQ 空间里 上面消息 就可以看见了！望采纳！谢谢这个直接去我的QQ中心不就好了么那里可以查到 我的好友单向好友好友恢复、 以及好友申请 啊可以是你加别人的 或 别人加你的都可以查得到",
]

query_embeddings = model.encode(queries, prompt_name="query", normalize_embeddings=True)
document_embeddings = model.encode(documents, normalize_embeddings=True)

scores = (query_embeddings @ document_embeddings.T)
print(scores.tolist())

Reproduce our results（C-MTEB）:

Check out scripts/eval_mteb.py to reproduce evaluation results on C-MTEB benchmark.

Model	T2Retrieval	MMarcoRetrieval	DuRetrieval	CovidRetrieval	CmedqaRetrieval	EcomRetrieval	MedicalRetrieval	VideoRetrieval	Avg
Zhihui_LLM_Embedding	88.30	84.77	91.34	84.39	48.69	71.96	65.19	79.31	76.74
zpoint_large_embedding_zh	83.81	82.38	89.23	89.14	47.16	70.74	68.14	80.26	76.36
gte-Qwen2-7B-instruct	87.73	85.16	87.44	83.65	48.69	71.15	65.59	78.84	76.03
360Zhinao-search	87.12	83.32	87.57	85.02	46.73	68.9	63.69	78.09	75.06
AGE_Hybrid	86.88	80.65	89.28	83.66	47.26	69.28	65.94	76.79	74.97