Model: anishareddyalla/bge-base-aws-case-studies
Author: anishareddyalla
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:2231, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: base_model: BAAI/bge-base-en-v1.5
language: en
library_name: sentence-transformers
license: apache-2.0
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:2231, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: Brian Pugh Chief Information Officer, Comscore Français Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance. Learn more » 2023 Español Then, Comscore can set up its own privacy controls, including a mutually agreed upon join key that gives collaborators the ability to match data tables and perform analyses using a double-blind method. This method means that all parties can protect sensitive data, such as cookies, first-party IDs, and IP addresses, and run queries on combined data to gain richer, more comprehensive insights. “Instead of ingesting all that information and doing the analysis behind our firewall, we can join those things in AWS Clean Rooms and get back what we need,” says Brian Pugh, chief information officer at Comscore. Additionally, Comscore can organize its analytics by demographics or other categories so that it can identify trends in how groups of people interact with certain media. Comscore can also connect AWS Clean Rooms with Amazon QuickSight—a solution that provides unified business intelligence at hyperscale—so that it can visualize its data in one place using interactive, customizable dashboards. 日本語 About Comscore Get Started 한국어 Organizations of all sizes across all industries are transforming their businesses and delivering on their missions every day using AWS. Contact our experts and start your own AWS journey today. Industry Challenge AWS Clean Rooms helps customers and their partners more easily and securely collaborate and analyze their collective datasets—without sharing or copying one another’s underlying data. AWS Services Used 中文 (繁體) Bahasa Indonesia AWS Clean Rooms. . . helps Comscore to provide the best possible measurement and support to our data partners to trust that the data that they’re providing is safe and protected. ” Ρусский عربي Analytics and insights provider Comscore provides a wide range of data-driven solutions that support planning, transacting, and measuring media across channels. It serves media companies and advertisers, promoting transparency and trust within the industry. Benefits of Using AWS 中文 (简体) Comscore turned to Amazon Web Services (AWS) and chose AWS Clean Rooms to uphold privacy-enhanced collaborations with its partners. AWS Clean Rooms helps Comscore’s customers and partners to securely match, analyze, and collaborate on their combined datasets with ease and without sharing or revealing underlying data. Using this solution, Comscore can invite up to five collaborators into an AWS Clean Room and pull pre-encrypted data into a configured data table from Amazon Simple Storage Service (Amazon S3), an object storage service built to retrieve any amount of data from anywhere. Media ratings company Comscore can provide richer insights to advertisers while maintaining data privacy by securely collaborating on its data with third parties using AWS Clean Rooms. Amazon QuickSight powers data-driven organizations with unified business intelligence (BI) at hyperscale.
sentences: How does Comscore use AWS Clean Rooms to protect sensitive data while collaborating with third parties?, How did AWS help CEHC in building a cost-effective alternate production/DR environment in a fraction of the time compared to a traditional brick-and-mortar production build?, How does AWS aim to democratize access to generative AI applications for all builders through services like Amazon Bedrock?, source_sentence: We convert the HTML pages on this site into smaller overlapping chunks (to retain some context continuity between chunks) of information and then convert these chunks into embeddings using the gpt-j-6b model and store the embeddings in OpenSearch Service. We implement the RAG functionality inside an AWS Lambda function with Amazon API Gateway to handle routing all requests to the Lambda. We implement a chatbot application in Streamlit which invokes the function via the API Gateway and the function does a similarity search in the OpenSearch Service index for the embeddings of user question. The matching documents (chunks) are added to the prompt as context by the Lambda function and then the function uses the flan-t5-xxl model deployed as a SageMaker endpoint to generate an answer to the user question. All the code for this post is available in the GitHub repo. The following figure represents the high-level architecture of the proposed solution. Figure 1: Architecture Step-by-step explanation: The User provides a question via the Streamlit web application. The Streamlit application invokes the API Gateway endpoint REST API. The API Gateway invokes the Lambda function. The function invokes the SageMaker endpoint to convert user question into embeddings. The function invokes invokes an OpenSearch Service API to find similar documents to the user question. The function creates a “prompt” with the user query and the “similar documents” as context and asks the SageMaker endpoint to generate a response. The response is provided from the function to the API Gateway. The API Gateway provides the response to the Streamlit application. The User is able to view the response on the Streamlit application, As illustrated in the architecture diagram, we use the following AWS services: SageMaker and Amazon SageMaker JumpStart for hosting the two LLMs. OpenSearch Service for storing the embeddings of the enterprise knowledge corpus and doing similarity search with user questions. Lambda for implementing the RAG functionality and exposing it as a REST endpoint via the API Gateway. Amazon SageMaker Processing jobs for large scale data ingestion into OpenSearch. Amazon SageMaker Studio for hosting the Streamlit application. AWS Identity and Access Management roles and policies for access management.
sentences: How can model producers and application builders effectively fine-tune generative foundation models to be aligned with human preferences and perform specific tasks accurately?, How do retailers lose out on revenue due to issues with search functionality on their websites?, How is the RAG functionality implemented within the AWS architecture described for handling user questions and providing responses via the Streamlit application?, source_sentence: Although Amazon EKS provided management capabilities, it was immediately apparent that we were managing infrastructure that wasn’t specifically tailored for inference. Forethought had to manage model inference on Amazon EKS ourselves, which was a burden on engineering efficiency. For example, in order to share expensive GPU resources between multiple models, we were responsible for allocating rigid memory fractions to models that were specified during deployment. We wanted to address the following key problems with our existing infrastructure: High cost – To ensure that each model had enough resources, we would be very conservative in how many models to fit per instance. This resulted in much higher costs for model hosting than necessary. Low reliability – Despite being conservative in our memory allocation, not all models have the same requirements, and occasionally some models would throw out of memory (OOM) errors. Inefficient management – We had to manage different deployment manifests for each type of model (such as classifiers, embeddings, and autocomplete), which was time-consuming and error-prone. We also had to maintain the logic to determine the memory allocation for different model types. Ultimately, we needed an inference platform to take on the heavy lifting of managing our models at runtime to improve the cost, reliability, and the management of serving our models. SageMaker MMEs allowed us to address these needs. Through its smart and dynamic model loading and unloading, and its scaling capabilities, SageMaker MMEs provided a significantly less expensive and more reliable solution for hosting our models. We are now able to fit many more models per instance and don’t have to worry about OOM errors because SageMaker MMEs handle loading and unloading models dynamically. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. The following diagram illustrates our legacy architecture. To begin our migration to SageMaker MMEs, we identified the best use cases for MMEs and which of our models would benefit the most from this change. MMEs are best used for the following: Models that are expected to have low latency but can withstand a cold start time (when it’s first loaded in) Models that are called often and consistently Models that need partial GPU resources Models that share common requirements and inference logic We identified our embeddings models and autocomplete language models as the best candidates for our migration. To organize these models under MMEs, we would create one MME per model type, or task, one for our embeddings models, and another for autocomplete language models. We already had an API layer on top of our models for model management and inference. Our task at hand was to rework how this API was deploying and handling inference on models under the hood with SageMaker, with minimal changes to how clients and product teams interacted with the API. We also needed to package our models and custom inference logic to be compatible with NVIDIA Triton Inference Server using SageMaker MMEs.
sentences: How did the company address the issues of high cost, low reliability, and inefficient management in managing model inference on Amazon EKS, and what solution did they implement to improve the cost, reliability, and management of serving their models?, How can Aurora be configured to interface with Comprehend for analyzing text data?, How has the implementation of chatbots and voice bots powered by Amazon Lex improved the customer and agent experiences at WaFd Bank's contact center solution?, source_sentence: In our current approach, we store these files in Amazon S3. Although these stored files aren’t accessible from the browser in our version of the code, you can modify the code to play previously generated audio files by fetching them from Amazon S3 (instead of regenerating the audio for the text again using Amazon Polly). We have more code examples for accessing Amazon Polly with Python in the AWS Code Library. Create the solution The entire solution is available from our Github repo. To create this solution in your account, follow the instructions in the README. md file. The solution includes an AWS CloudFormation template to provision your resources. Cleanup To clean up the resources created in this demo, perform the following steps: Delete the S3 buckets created to store the CloudFormation template (Bucket A), the source code (Bucket B) and the website ( pth-cf-text-highlighter-website-[Suffix] ). Delete the CloudFormation stack pth-cf. Delete the S3 bucket containing the speech files ( pth-speech-[Suffix] ). This bucket was created by the CloudFormation template to store the audio and speech marks files generated by Amazon Polly. Summary In this post, we showed an example of a solution that can highlight text as it’s being spoken using Amazon Polly. It was developed using the Amazon Polly speech marks feature, which provides us markers for the place each word or sentence begins in an audio file. The solution is available as a CloudFormation template. It can be deployed as is to any web application that performs text-to-speech conversion. This would be useful for adding visual capabilities to audio in books, avatars with lip-sync capabilities (using viseme speech marks), websites, and blogs, and for aiding people with hearing impairments. It can be extended to perform additional tasks besides highlighting text. For example, the browser can show images, play music, and perform other animations on the front end while the text is being spoken. This capability can be useful for creating dynamic audio books, educational content, and richer text-to-speech applications. We welcome you to try out this solution and learn more about the relevant AWS services from the following links.
sentences: How has the TRRF platform improved patient care for individuals with Angelman Syndrome, according to Megan Cross of the Foundation for Angelman Syndrome (FAST)?, How does Amazon SageMaker Ground Truth Plus help users prepare high-quality training datasets for generative AI applications, specifically in terms of removing the heavy lifting associated with data labeling applications and managing the labeling workforce?, How can the solution of highlighting text as it's being spoken using Amazon Polly be extended to perform additional tasks, and what are some examples of these tasks?, source_sentence: CU Coventry’s bachelor of science in cloud computing course officially began in September 2020 and has already seen success from the program’s industry-driven framework. Overview Validate technical skills and cloud expertise to grow your career and business. Learn more » Get Started on AWS services using AWS Academy Learner Labs Build your cloud skills at your own pace, on your own time, and completely for free. Looking ahead, Coventry University Group plans to expand bachelor of science degree in cloud computing courses to its campuses in London and Wroclaw. “The ability to have hands-on experience with AWS services—the same ones that companies use in the real world—is invaluable,” said Tomasz, a student of the Cloud Computing Course. “Once we join the workforce, we can apply our skill sets and hit the ground running. ” Türkçe English Students successfully engaging in the program graduate with in-demand skills for careers in the cloud, including valuable experience with AWS services through AWS Academy Learner Labs. AWS Academy provides higher education institutions with ready-to-teach cloud computing curriculum to prepare students for AWS Certifications, which validate technical skills and cloud expertise for in-demand cloud jobs. “The most important thing is for the modules to reflect what the industry needs. We want students to add value to the global workforce,” says Flood. Taking advantage of AWS Education Programs, CU Coventry’s BSc degree in cloud computing innovates on AWS to track the IT industry’s rapid pace. AWS Certification Deutsch Coventry University Group is based in the United Kingdom with more than 30,000 students and more than 200 undergraduate and postgraduate degrees across its schools, faculties, and campuses. Tiếng Việt AWS Training and Certification Italiano ไทย Outcome | Looking to the Future of Coventry University Group’s Cloud Computing Program Learn more » Increases employability Coventry University Group used AWS Education Programs to create a comprehensive and flexible degree to help students meet growing IT industry cloud skills demand. Both the 3-year bachelor of science degree in cloud computing and its accelerated version were developed in collaboration with AWS. These programs were designed by working backwards from the cloud skills employers are currently seeking in the UK and across the global labor market. “The approach gave us insights into what skill gaps were lacking in the industry. From there, we designed the courses, with the AWS team providing helpful inputs,” says Flood. “For example, the AWS team pointed out that there was an industry need for serverless computing skills, and we integrated that into our curriculum. ” Português.
sentences: How did Read use Amazon Web Services (AWS) and NVIDIA Riva to improve the performance of its transcription tool while keeping costs low?, How does RUSH University System for Health use HECAP and Amazon HealthLake to address healthcare disparities and improve patient outcomes for residents of Chicago's West Side?, How does CU Coventry's Bachelor of Science in Cloud Computing program incorporate AWS services and industry-driven insights to prepare students for in-demand cloud jobs?
model-index: name: BGE base Financial Matryoshka
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.4596774193548387
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.8024193548387096
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.8991935483870968
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9596774193548387
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.4596774193548387
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.2674731182795699
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.17983870967741938
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0959677419354839
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.4596774193548387
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.8024193548387096
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.8991935483870968
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9596774193548387
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.7184810942825108
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.6395305299539169
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.6408821665935496
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@1
value: 0.46774193548387094
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verified: no, type: cosine_accuracy@3
value: 0.7983870967741935
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verified: no, type: cosine_accuracy@5
value: 0.8951612903225806
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verified: no, type: cosine_accuracy@10
value: 0.9596774193548387
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value: 0.2661290322580645
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.17903225806451614
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verified: no, type: cosine_precision@10
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verified: no, type: cosine_recall@3
value: 0.7983870967741935
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.8951612903225806
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verified: no, type: cosine_recall@10
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verified: no, type: cosine_ndcg@10
value: 0.7213571757198337
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.6433467741935482
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.6448406697096213
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.4596774193548387
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
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verified: no, type: cosine_accuracy@10
value: 0.9637096774193549
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verified: no, type: cosine_precision@5
value: 0.18225806451612905
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verified: no, type: cosine_precision@10
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verified: no, type: cosine_recall@3
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verified: no, type: cosine_recall@5
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verified: no, type: cosine_recall@10
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verified: no, type: cosine_ndcg@10
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name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.6410682283666154
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.6422448191163128
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.4314516129032258
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.7580645161290323
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.8830645161290323
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9475806451612904
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.4314516129032258
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value: 0.17661290322580647
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verified: no, type: cosine_recall@10
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verified: no, type: cosine_ndcg@10
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name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.6124535970302099
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.6145615813099632
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 64
type: dim_64
metrics: type: cosine_accuracy@1
value: 0.4032258064516129
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.7459677419354839
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
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name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
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name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.4032258064516129
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.24865591397849462
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.17419354838709677
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09516129032258065
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.4032258064516129
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.7459677419354839
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.8709677419354839
name: Cosine Recall@5
verified: no, type: cosine_recall@10
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name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
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name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.5919978878648234
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.5935355054811555
name: Cosine Map@100
verified: no
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Created: 2024-07-18 18:52:49