Instructor Large

🚀 hkunlp/instructor-large

我们推出了 Instructor👨‍🏫，这是一个经过指令微调的文本嵌入模型，它能够在无需任何微调的情况下，通过简单地提供任务指令，生成适用于任何任务（如分类、检索、聚类、文本评估等）和领域（如科学、金融等）的文本嵌入。Instructor👨‍在70个不同的嵌入任务中达到了最优性能（MTEB排行榜）！

该模型可以通过我们定制的 sentence-transformer 库轻松使用。更多详细信息，请查看我们的论文和项目页面！

**************************** 更新内容 ****************************

• 12月28日：我们发布了一个新的检查点，该检查点使用了难负样本进行训练，性能更优。
• 12月21日：我们发布了论文、代码、检查点和项目页面！欢迎查看！

🚀 快速开始

📦 安装指南

pip install InstructorEmbedding

💻 使用示例

基础用法

计算自定义嵌入

from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-large')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)

高级用法

计算自定义文本的嵌入

如果你想为特定句子计算自定义嵌入，可以按照统一模板编写指令：

                          Represent the domain text_type for task_objective:

• domain 是可选的，它指定了文本的领域，例如科学、金融、医学等。
• text_type 是必需的，它指定了编码单元，例如句子、文档、段落等。
• task_objective 是可选的，它指定了嵌入的目标，例如检索文档、对句子进行分类等。

计算句子相似度

你可以进一步使用该模型，通过自定义嵌入来计算两组句子之间的相似度。

from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], 
               ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
               ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)

信息检索

你还可以使用自定义嵌入进行信息检索。

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
query  = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
          ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
          ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
query_embeddings = model.encode(query)
corpus_embeddings = model.encode(corpus)
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
retrieved_doc_id = np.argmax(similarities)
print(retrieved_doc_id)

聚类

使用自定义嵌入对文本进行分组聚类。

import sklearn.cluster
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
             ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
             ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
             ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
             ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher,  Vector States of Charmonium']]
embeddings = model.encode(sentences)
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
clustering_model.fit(embeddings)
cluster_assignment = clustering_model.labels_
print(cluster_assignment)

📚 详细文档

该模型在多个数据集和任务上进行了评估，以下是部分评估结果：

📄 许可证

本项目采用 apache-2.0 许可证。