hkunlp / instructor-large

我们介绍 Instructor ?‍?，这是一个经过指导微调的文本嵌入模型，可以生成针对任何任务（如分类、检索、聚类、文本评估等）和领域（如科学、金融等）定制的文本嵌入，只需提供任务指导，无需进行任何微调。Instructor?‍ 实现了 70 个多样化嵌入任务的最佳结果（ MTEB leaderboard ）!该模型与我们定制的 sentence-transformer 库配合使用非常方便。更多详细信息，请查看 our paper 和 project page ！

**************************** 更新 ****************************

• 12/28: 我们发布了一个新的 checkpoint ，使用困难负例进行训练，性能更佳。
• 12/21: 我们发布了我们的 paper 、 code 、 checkpoint 和 project page ！去看看吧！

快速开始

安装

pip install InstructorEmbedding

计算自定义嵌入

然后，您可以使用以下方式计算特定于领域和任务的嵌入：

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)

使用案例

为自定义文本计算嵌入

如果您想为特定的句子计算自定义嵌入，您可以按照统一的模板编写指示：

表示任务目标的 text_type 的 domain_text_type：

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

计算句子相似度

您还可以使用该模型计算两组句子之间的相似度，使用自定义嵌入。

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

您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)