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---
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
- type: cosine_accuracy@3
value: 0.8024193548387096
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8991935483870968
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9596774193548387
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4596774193548387
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2674731182795699
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17983870967741938
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0959677419354839
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4596774193548387
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8024193548387096
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8991935483870968
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9596774193548387
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7184810942825108
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6395305299539169
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6408821665935496
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.46774193548387094
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7983870967741935
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8951612903225806
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9596774193548387
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46774193548387094
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2661290322580645
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17903225806451614
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0959677419354839
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.46774193548387094
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7983870967741935
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8951612903225806
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9596774193548387
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7213571757198337
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6433467741935482
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6448406697096213
name: Cosine Map@100
- 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
- type: cosine_accuracy@3
value: 0.7983870967741935
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9112903225806451
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9637096774193549
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4596774193548387
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2661290322580645
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18225806451612905
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0963709677419355
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4596774193548387
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7983870967741935
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9112903225806451
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9637096774193549
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7207090934241043
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6410682283666154
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6422448191163128
name: Cosine Map@100
- 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
- type: cosine_accuracy@3
value: 0.7580645161290323
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8830645161290323
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9475806451612904
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4314516129032258
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25268817204301075
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17661290322580647
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09475806451612905
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4314516129032258
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7580645161290323
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8830645161290323
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9475806451612904
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6948316840385708
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6124535970302099
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6145615813099632
name: Cosine Map@100
- 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
- type: cosine_accuracy@3
value: 0.7459677419354839
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8709677419354839
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9516129032258065
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4032258064516129
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24865591397849462
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17419354838709677
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09516129032258065
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4032258064516129
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7459677419354839
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8709677419354839
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9516129032258065
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6800470209866719
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5919978878648234
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5935355054811555
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("anishareddyalla/bge-base-aws-case-studies")
# Run inference
sentences = [
'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.',
"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?",
"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?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4597 |
| cosine_accuracy@3 | 0.8024 |
| cosine_accuracy@5 | 0.8992 |
| cosine_accuracy@10 | 0.9597 |
| cosine_precision@1 | 0.4597 |
| cosine_precision@3 | 0.2675 |
| cosine_precision@5 | 0.1798 |
| cosine_precision@10 | 0.096 |
| cosine_recall@1 | 0.4597 |
| cosine_recall@3 | 0.8024 |
| cosine_recall@5 | 0.8992 |
| cosine_recall@10 | 0.9597 |
| cosine_ndcg@10 | 0.7185 |
| cosine_mrr@10 | 0.6395 |
| **cosine_map@100** | **0.6409** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4677 |
| cosine_accuracy@3 | 0.7984 |
| cosine_accuracy@5 | 0.8952 |
| cosine_accuracy@10 | 0.9597 |
| cosine_precision@1 | 0.4677 |
| cosine_precision@3 | 0.2661 |
| cosine_precision@5 | 0.179 |
| cosine_precision@10 | 0.096 |
| cosine_recall@1 | 0.4677 |
| cosine_recall@3 | 0.7984 |
| cosine_recall@5 | 0.8952 |
| cosine_recall@10 | 0.9597 |
| cosine_ndcg@10 | 0.7214 |
| cosine_mrr@10 | 0.6433 |
| **cosine_map@100** | **0.6448** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4597 |
| cosine_accuracy@3 | 0.7984 |
| cosine_accuracy@5 | 0.9113 |
| cosine_accuracy@10 | 0.9637 |
| cosine_precision@1 | 0.4597 |
| cosine_precision@3 | 0.2661 |
| cosine_precision@5 | 0.1823 |
| cosine_precision@10 | 0.0964 |
| cosine_recall@1 | 0.4597 |
| cosine_recall@3 | 0.7984 |
| cosine_recall@5 | 0.9113 |
| cosine_recall@10 | 0.9637 |
| cosine_ndcg@10 | 0.7207 |
| cosine_mrr@10 | 0.6411 |
| **cosine_map@100** | **0.6422** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4315 |
| cosine_accuracy@3 | 0.7581 |
| cosine_accuracy@5 | 0.8831 |
| cosine_accuracy@10 | 0.9476 |
| cosine_precision@1 | 0.4315 |
| cosine_precision@3 | 0.2527 |
| cosine_precision@5 | 0.1766 |
| cosine_precision@10 | 0.0948 |
| cosine_recall@1 | 0.4315 |
| cosine_recall@3 | 0.7581 |
| cosine_recall@5 | 0.8831 |
| cosine_recall@10 | 0.9476 |
| cosine_ndcg@10 | 0.6948 |
| cosine_mrr@10 | 0.6125 |
| **cosine_map@100** | **0.6146** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4032 |
| cosine_accuracy@3 | 0.746 |
| cosine_accuracy@5 | 0.871 |
| cosine_accuracy@10 | 0.9516 |
| cosine_precision@1 | 0.4032 |
| cosine_precision@3 | 0.2487 |
| cosine_precision@5 | 0.1742 |
| cosine_precision@10 | 0.0952 |
| cosine_recall@1 | 0.4032 |
| cosine_recall@3 | 0.746 |
| cosine_recall@5 | 0.871 |
| cosine_recall@10 | 0.9516 |
| cosine_ndcg@10 | 0.68 |
| cosine_mrr@10 | 0.592 |
| **cosine_map@100** | **0.5935** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,231 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 434.98 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.46 tokens</li><li>max: 65 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>”.</code> | <code>What specific event or topic is being discussed in the given information?</code> |
| <code>On AWS, Rackspace solved a major industry challenge with a solution that saved time, cut costs, and reduced complexity for its customers and itself. “When things go wrong, customers expect Rackspace to step in and act swiftly to solve their problem,” says Prewitt. “Using AWS Systems Manager, we can do that much more quickly. ” Português Rackspace needed a solution that could run both on premises and on the cloud. “We wanted one tool to use across the full suite of solutions that Rackspace manages,” says Gignac. AWS Systems Manager met that requirement and offered programmability. “That’s a key differentiator of AWS: we can use AWS Systems Manager to run shell scripts on individual VMs and do advanced orchestration,” Gignac continues. .</code> | <code>How did Rackspace use AWS Systems Manager to solve major industry challenges and improve their ability to quickly address customer issues?</code> |
| <code>Français Shortly after the onset of the pandemic in early 2020, Valant began offering a telehealth solution to provide virtual capabilities to practices and their patients. The solution was based on a digital communications platform that lacked a multi-user experience and many other requested features. “The platform we used offered peer-to-peer video only, and we needed group capabilities, chat, screen and file sharing, and a whiteboard,” says James Jay, chief technology officer at Valant Medical Solutions. “In behavioral health, it’s common to have parents, spouses, or other guests attend sessions, and we saw a significant demand from practices for multi-user functionality, as well as other features critical to engaging effectively with patients. We also had strong demand to integrate co-payment collection into telehealth check-in workflows in advance of sessions. ” 2023 Amazon Simple Email Service Español by using voice, video, messaging, and automated reminders Valant Medical Solutions, Inc. provides electronic health record software to behavioral health providers and practices. To add enhanced telehealth capabilities and improve patient communication, the company turned to Amazon Web Services to add capabilities in voice, video, messaging, and email through AWS Communication Developer Services to build a new telehealth solution for more than 2,500 behavioral health practices. AWS Communication Developer Services (CDS) are cloud-based APIs and SDKs that help builders add communication capabilities into their apps or websites with minimal coding. 日本語 Valant Medical Solutions, Inc. designs and develops web-based electronic health record (EHR) software to help behavioral health providers and practices streamline administration tasks and improve patient outcomes. More than 20,000 behavioral health professionals in group and solo private practices across the United States use the Valant platform to treat individuals seeking behavioral healthcare. The Valant IO system has extensive capabilities to enable providers to deliver value-based care through measurement-based assessment and ongoing outcome assessments. 5% Get Started 한국어 Overview | Opportunity | Solution | Outcome | AWS Services Used With the new Valant solution, practices can better engage their patients and communicate with them more frequently through automated reminders for appointments, insurance, no-show follow up, and more. Each practice has the option to deliver all communications via SMS, voice, and emails. Additionally, Valant has grown its overall business by 21 percent and increased add-on revenue by more than 100 percent. business growth Valant Uses AWS Communication Developer Services to Help Behavioral Health Practices Drive Better Patient Engagement Opportunity | Looking to Add More Features to a Telehealth Solution AWS Services Used Amazon Chime SDK As a result of key features built over the last 12 months, Valant has increased its overall business by more than 20 percent. The new telehealth and patient communications features are a big driver of the success. “Because of our new telehealth and automated reminders, which offer more robust features such as group meetings, our clients have seen a revenue increase,” says Jay. “We’ve had an incredible adoption of these new tools, which is also helping us grow our market share and customer satisfaction.</code> | <code>How has the implementation of the new telehealth solution with enhanced communication capabilities through AWS Communication Developer Services impacted Valant Medical Solutions, specifically in terms of business growth and revenue generation?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| **0.9143** | **4** | **-** | **0.6055** | **0.6308** | **0.646** | **0.5623** | **0.6339** |
| 1.8286 | 8 | - | 0.6255 | 0.6505 | 0.6517 | 0.5791 | 0.6558 |
| 2.2857 | 10 | 2.0293 | - | - | - | - | - |
| 2.9714 | 13 | - | 0.6096 | 0.6472 | 0.6471 | 0.5935 | 0.6490 |
| 3.8857 | 17 | - | 0.6125 | 0.6410 | 0.6468 | 0.6020 | 0.6422 |
| 4.5714 | 20 | 0.5008 | - | - | - | - | - |
| 4.8 | 21 | - | 0.6156 | 0.6351 | 0.6409 | 0.6014 | 0.6391 |
| 5.9429 | 26 | - | 0.6143 | 0.6350 | 0.6367 | 0.6015 | 0.6406 |
| 6.8571 | 30 | 0.2964 | 0.6167 | 0.6371 | 0.6390 | 0.5981 | 0.6387 |
| 8.0 | 35 | - | 0.6138 | 0.6364 | 0.6391 | 0.5986 | 0.6392 |
| 8.9143 | 39 | - | 0.6173 | 0.6378 | 0.6389 | 0.6021 | 0.6394 |
| 9.1429 | 40 | 0.2382 | 0.6161 | 0.6376 | 0.6391 | 0.5982 | 0.6398 |
| **0.9143** | **4** | **-** | **0.6273** | **0.6535** | **0.6608** | **0.5949** | **0.66** |
| 1.8286 | 8 | - | 0.6177 | 0.6439 | 0.6515 | 0.6074 | 0.6508 |
| 2.2857 | 10 | 0.554 | - | - | - | - | - |
| 2.9714 | 13 | - | 0.6070 | 0.6300 | 0.6339 | 0.5923 | 0.6366 |
| 3.8857 | 17 | - | 0.6071 | 0.6332 | 0.6362 | 0.5976 | 0.6362 |
| 4.5714 | 20 | 0.2694 | - | - | - | - | - |
| 4.8 | 21 | - | 0.6124 | 0.6397 | 0.6455 | 0.5988 | 0.6404 |
| 5.9429 | 26 | - | 0.6155 | 0.6411 | 0.6446 | 0.6007 | 0.6429 |
| 6.8571 | 30 | 0.1746 | 0.6167 | 0.6429 | 0.6467 | 0.5942 | 0.6424 |
| 8.0 | 35 | - | 0.6166 | 0.6398 | 0.6462 | 0.5928 | 0.6429 |
| 8.9143 | 39 | - | 0.6108 | 0.6426 | 0.6448 | 0.5943 | 0.6432 |
| 9.1429 | 40 | 0.1419 | 0.6146 | 0.6422 | 0.6448 | 0.5935 | 0.6409 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Accelerate: 0.32.1
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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