|
--- |
|
language: |
|
- en |
|
license: apache-2.0 |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:3825 |
|
- loss:TripletLoss |
|
base_model: BAAI/bge-large-en-v1.5 |
|
widget: |
|
- source_sentence: Adobe Workfront is the leader in Collaborative Work Management |
|
and Project and Portfolio Management, and has been recognized by Gartner, Forrester, |
|
IDC, Constellation Research, and more for its current capabilities and future |
|
vision. Relying on instant messages, post-it notes, and meetings to keep everyone |
|
in the loop create information silos. Adobe Workfront tracks threaded conversations, |
|
status updates, and feedback in one place so communication is kept in context |
|
of work, and there’s no confusion or wasted time. Project team members can collaborate |
|
on documents, timesheets and time usage, attach notes, comments, calendars, and |
|
record meeting discussions in custom forms in projects, tasks, issues, etc. Integration |
|
with collaboration tools like Teams and Slack are also natively available. In |
|
addition, Workfront Proof provides the ability for internal and external users |
|
to collaborate on documents, videos, websites and over 150 file types. |
|
sentences: |
|
- Is there a pre-built report that provides a visual representation of all the marketing |
|
program interactions that impacted the leads linked to a specific account and |
|
opportunity? |
|
- Is real-time collaboration offered? Does your platform include features such as |
|
real-time persistent chat, shared whiteboards, team meetings with audio, video, |
|
and screen sharing, as well as VoIP and telephony integration? |
|
- This is supported with the schema defined as part of the Experience Data Model |
|
(XDM). Changes to a schema, data set and data ingestion process can be carried |
|
out without breaking or invalidating past ingestions.https://experienceleague.adobe.com/docs/experience-platform/xdm/schema/composition.html#evolution |
|
- source_sentence: Real-Time CDP enables you to create robust, centralized profiles |
|
containing customer attributes and timestamped events each customer interaction |
|
across systems integrated with Adobe Real-Time CDP. The format and structure of |
|
this data is provided by Experience Data Model (XDM) schemas, with each schema |
|
being based upon an XDM class and containing fields that are compatible with that |
|
class.Schemas can be created for multiple use cases, referencing the same class |
|
but containing fields specific to their use. When a schema is enabled for Profile, |
|
it becomes part of a union schema. In other words, union schemas are composed |
|
of multiple schemas that share the same class and have been enabled for Profile. |
|
The union schema enables you to see an amalgamation of all of the fields contained |
|
within schemas sharing the same class. Real-Time CDP uses the union schema to |
|
create a holistic view of each individual customer.For more information on union |
|
schemas, visit https://experienceleague.adobe.com/docs/experience-platform/profile/union-schemas/union-schema.html?lang=en |
|
sentences: |
|
- In what ways does your platform's inventory facilitate the development of a data |
|
inventory for regulatory compliance? |
|
- The tool should have the capability to completely automate campaign outputs, encompassing |
|
everything from segment refresh to the generation of the output file. |
|
- You can manage campaigns across multiple channels in Adobe Marketo Engage, including |
|
email, social, paid media, SMS, in-app messaging and web. If there is a channel |
|
that Marketo Engage doesn't natively support (such as Direct Mail), you can leverage |
|
one of our pre-built integrations with partner technologies in our best-in-class |
|
partner ecosystem. This means that as your marketing strategy grows and you extend |
|
your presence across other channels, you won't need to worry about finding and |
|
integrating a point tool and making sure the data is getting passed correctly.Marketo |
|
Engage is designed to be a multi-channel marketing platform at its core. The platform |
|
offers numerous capabilities that make it easy to configure multi-channel campaigns |
|
and align communication to match customer preferences for channel. Due to the |
|
design and layout of the system, Marketo Engage consistently scores higher on |
|
ease-of-use and time-to-value by analysts and customers alike compared to other |
|
platforms.Because you're able to listen and engage across multiple channels using |
|
Marketo Engage, that means you're also able to report on which channels and campaigns |
|
are performing the best during different stages of the customer lifecycle. |
|
- source_sentence: Adobe's Secure Product Lifecycle (SPLC) was designed from the ground |
|
up and integrated into multiple stages of the product lifecycle to help keeping |
|
customer information safe and secure. It is comprised of a rigorous set of several |
|
hundred specific security activities spanning software development practices, |
|
processes and tools. Adobe SPLC controls include, depending on the specific Adobe |
|
product or service, some or all of the following recommended practices, processes |
|
and tools:- Security training and certification for product teams- Product health, |
|
risk, and threat landscape analysis- Secure coding guidelines, rules and analysis- |
|
Service roadmaps, security tools, and testing methods that guide the security |
|
team to help address the Open Web Application Security Project (OWASP) Top 10 |
|
most critical web application security flaws and CWE/SANS Top 25 most dangerous |
|
software errors- Security architecture reviews and penetration testing- Source |
|
code reviews to help eliminate known flaws that could lead to vulnerabilities- |
|
User-generated content validation- Static and dynamic code analysis- Application |
|
and network scanning- Readiness reviews, response plans, and release of developer |
|
education materials. |
|
sentences: |
|
- "Rephrased question: \n\nCommitment to Good Coding Practices \n\na) Astro and\ |
|
\ third parties should commit to making reasonable efforts to adhere to good coding\ |
|
\ practices. \n\nb) Compliance with recognized industry standards, such as those\ |
|
\ established by The Open Web Application Security Project (OWASP) Foundation,\ |
|
\ is recommended. \n\nc) Astro and third parties should agree to adhere to a defined\ |
|
\ set of secure coding guidelines that specify the expected formatting, structure,\ |
|
\ and commenting of code. \n\nd) Thorough commenting is required for all security-relevant\ |
|
\ code. \n\ne) Guidance on how to avoid common security vulnerabilities must be\ |
|
\ included. \n\nf) Prior to being considered ready for unit testing, all code\ |
|
\ must undergo review by at least one additional Developer to ensure it meets\ |
|
\ the security requirements and coding guidelines. \n\ng) Developers must provide\ |
|
\ and follow a security test plan that outlines the approach for testing and confirming\ |
|
\ compliance with each security requirement. \n\nh) Developers will execute the\ |
|
\ security test plan and, if necessary for audit purposes, present the test results\ |
|
\ to Astro. \n\ni) Developers agree to deliver secure configuration guidelines\ |
|
\ that comprehensively explain all security-related configuration options and\ |
|
\ their implications for the overall security of the software." |
|
- The Service Provider will deliver maintenance services that encompass both manual |
|
and automated patch management, as well as the application of patches that have |
|
received approval from the Agency. |
|
- While offering a decoupled architecture, with ContextHub, composition middleware, |
|
and front-end management capabilities, Adobe Experience Manager comes as a fully |
|
integrated solution that customers can use end-to-end or via leveraging portions |
|
of the platform. For example, only as a ContextHub (headless CMS) or only as composition |
|
middleware.As part of Adobe Experience Cloud, Adobe Experience Manager offers |
|
on always-up-to-date solution that isn't limited for extensibility, to fit the |
|
solution into the enterprise solution landscape. With the move to a cloud-native |
|
platform architecture, Adobe has preserved the extensibility for which the Experience |
|
Manager platform is known and respected.Delivering globally scalable experiences |
|
to a distributed workforce requires an architecture that natively includes the |
|
cloud-based edge technologies. With our CMS platform, edge computing is an integral |
|
part of the architecture. Keep in mind, this is not the same as leveraging CDNs |
|
(everybody can do that). This means that Adobe Experience Manager has been re-architected |
|
to run time-sensitive workloads seamlessly on edge-based cloud platforms.Adobe |
|
Experience Manager got its name from providing approachable in-context editing |
|
capabilities. Adobe keeps investing to bring in-context editing to any surface, |
|
and was the first to market with decoupled, in-context editing in Single Page |
|
Applications with a lightweight SDK. With experimentation and personalization |
|
by default, Adobe provides new ways for brands and enterprises to have a minimal |
|
overhead to continuously optimize experiences.Deep integration with the Adobe |
|
Creative and Adobe Document Clouds allows for access to industry-leading content |
|
creation capabilities and asset / document services like e-signatures. |
|
- source_sentence: Functionality is provided as a native feature and is included in |
|
the base price of the commerce system Native support for customer-specific price |
|
books / catalogs, restrictions can be made by customer segment. Customer segments |
|
can also be assigned to specific websites, allowing for different segmentation |
|
strategies across multiple site. Segments can be applied to visitors, registered |
|
customers, or both, allowing for broad or specific targeting. |
|
sentences: |
|
- Adobe Experience Platform supports a number of identifiers which can be broadly |
|
classified in three categories-An identity such as a login ID, ECID, or loyalty |
|
ID is referred to as a known identity.PII such as email address and phone number, |
|
serves to directly identify a customer. As a result, PII is used to match a customer’s |
|
multiple identities across systems.Unknown or anonymous identities single out |
|
a device without identifying the actual person using it. This category includes |
|
information such as a visitor’s IP address and cookie ID. In addition, please |
|
see the above response DM-5 to understand how ID's are stitched together using |
|
the identity service.Reference Material:https://experienceleague.adobe.com/docs/experience-platform/identity/home.html?lang=en |
|
- In what ways does your company collect enhancement feedback from customers and |
|
engage them in determining the priority of future releases? Additionally, how |
|
does your company assess its effectiveness in meeting customer needs? |
|
- Capability to categorize the catalog and limit visibility of sections based on |
|
country, region, brand, business line, customer, segment, and role. |
|
- source_sentence: 'Adobe Experience Platform helps you create a Real-time Customer |
|
Profile for each customer record where you can see a holistic view of each individual |
|
customer by combining data from multiple sources, channels, including online, |
|
offline, CRM, and third party. Profile allows you to consolidate your customer |
|
data into a unified view offering an actionable, timestamped account of every |
|
customer interaction. Further, each data source or channel might work on different |
|
customer identity and will share multiple identities with the Platform. Identity |
|
Service helps you to gain a better view of your customer and their behavior by |
|
bridging identities across devices and systems, allowing you to deliver impactful, |
|
personal digital experiences in real time. The Platform creates an identity graph, |
|
a map of relationships between different identity namespaces, providing you with |
|
a visual representation of how your customer interacts with your brand across |
|
different channels. The data captured in the datsets is secure and cannot be accessed |
|
outside of the Real time Customer Profile and segmentation. << Customer name >> |
|
users which elligible to access the data as per access control, can only access |
|
the data.Reference material: Identity Service - https://experienceleague.adobe.com/docs/experience-platform/identity/namespaces.html?lang=enAccess |
|
Control - https://experienceleague.adobe.com/docs/experience-platform/access-control/home.html?lang=en' |
|
sentences: |
|
- How is security handled in relation to a single customer view when we grant access |
|
to various business units? Is user data explicitly linked to the division that |
|
supplied the source data, or to the profile that has been identified as comprising |
|
data from that division? |
|
- Is it possible to prioritize tasks within the application? |
|
- Clients may capture new project or other work requests through any number of request |
|
queues that the client can configure. Adobe Workfront provides a help desk area |
|
of the application where request queues can be configured for the purpose of capturing, |
|
routing, and managing various requests. Client can configure request forms through |
|
the UI and forms can include both native and custom fields. Routing rules and |
|
approval processes can be designated for each specific request queue. Project |
|
requests may also require a business case to be built for the requested project. |
|
Adobe Workfront allows clients to build business cases for projects and these |
|
business cases can be used to evaluate the merits of a project. Information captured |
|
in business cases can include (but is not limited to) project goals/objectives, |
|
planned costs (expenses and resource related), high-level resources estimates, |
|
alignment scorecard, potential risks, and any custom data fields the client chooses |
|
to add. |
|
pipeline_tag: sentence-similarity |
|
library_name: sentence-transformers |
|
--- |
|
|
|
# BGE large model |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 1024 dimensions |
|
- **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': 1024, '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("bge-large-triplet-v1.5") |
|
# Run inference |
|
sentences = [ |
|
'Adobe Experience Platform helps you create a Real-time Customer Profile for each customer record where you can see a holistic view of each individual customer by combining data from multiple sources, channels, including online, offline, CRM, and third party. Profile allows you to consolidate your customer data into a unified view offering an actionable, timestamped account of every customer interaction. Further, each data source or channel might work on different customer identity and will share multiple identities with the Platform. Identity Service helps you to gain a better view of your customer and their behavior by bridging identities across devices and systems, allowing you to deliver impactful, personal digital experiences in real time. The Platform creates an identity graph, a map of relationships between different identity namespaces, providing you with a visual representation of how your customer interacts with your brand across different channels. The data captured in the datsets is secure and cannot be accessed outside of the Real time Customer Profile and segmentation. << Customer name >> users which elligible to access the data as per access control, can only access the data.Reference material: Identity Service - https://experienceleague.adobe.com/docs/experience-platform/identity/namespaces.html?lang=enAccess Control - https://experienceleague.adobe.com/docs/experience-platform/access-control/home.html?lang=en', |
|
'How is security handled in relation to a single customer view when we grant access to various business units? Is user data explicitly linked to the division that supplied the source data, or to the profile that has been identified as comprising data from that division?', |
|
'Clients may capture new project or other work requests through any number of request queues that the client can configure. Adobe Workfront provides a help desk area of the application where request queues can be configured for the purpose of capturing, routing, and managing various requests. Client can configure request forms through the UI and forms can include both native and custom fields. Routing rules and approval processes can be designated for each specific request queue. Project requests may also require a business case to be built for the requested project. Adobe Workfront allows clients to build business cases for projects and these business cases can be used to evaluate the merits of a project. Information captured in business cases can include (but is not limited to) project goals/objectives, planned costs (expenses and resource related), high-level resources estimates, alignment scorecard, potential risks, and any custom data fields the client chooses to add.', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 1024] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
* Size: 3,825 training samples |
|
* Columns: <code>positive</code>, <code>anchor</code>, and <code>negative</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | negative | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| type | string | string | string | |
|
| details | <ul><li>min: 7 tokens</li><li>mean: 146.93 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 23.8 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 141.82 tokens</li><li>max: 512 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | negative | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Adobe Commerce being an Open-source platform nurtures community of users to contribute learn and connect to our platform. There are over 400K+ developers and community members worldwide with Adobe Commerce development experience, and over 8,000 Certified Adobe Commerce Developers, who can support projects and implementations. This global community is truly dedicated to the growth of our platform and success of our customers. In addition, you can easily grow and scale your team because Adobe Commerce talent is easy to find. For more details, please see below: - https://business.adobe.com/in/products/magento/community.html#</code> | <code>Can you provide an overview of the Adobe Commerce Developer Community?</code> | <code>Streaming ingestion for Adobe Experience Platform provides users a method to send data from client and server-side devices to Experience Platform in real time. Streaming ingestion plays a key role in building real-time customer profiles by enabling <<Customer name >> to deliver Profile data into the Data Lake with as little latency as possible. The stream connector for Adobe Experience Platform is based on Apache Kafka Connect. This library can be used to stream JSON events from Kafka topics in <<Customer name >> data centre directly to Experience Platform in real time. The stream connector is a sink (one-way) connector, delivering data from Kafka topics to a registered endpoint on Experience Platform. The connector supports the following features:1. Authenticated collection of data2. Batching messages to reduce network calls and increase throughputFull documentation here: https://experienceleague.adobe.com/docs/experience-platform/ingestion/streaming/kafka.html?lang=en</code> | |
|
| <code>Adobe Commerce has extensive experience in the B2C environment. Our platform supports B2C business models out of the box and provides a range of features and capabilities to enhance the B2C customer experience. With Adobe Commerce, businesses can create personalized commerce journeys, boost conversion and sales with AI-powered merchandising tools, and provide a seamless and intuitive shopping experience for their customers.</code> | <code>Can you explain your experience working in the B2C sector?</code> | <code>Adobe’s vision is to empower companies to unify end-to-end customer experiences from creation to commerce, driving loyalty and business growth. Our company values — Create the future, Own the outcome, Raise the bar, and Be genuine — represent who we are, how we show up in the world, and how we’ll define our future success.</code> | |
|
| <code>Adobe Professional Services takes a phased approach in implementation. In the first phase which we call a “Design and plan” phase we define business requirements, features, and KPIs. We run a series of workshops in first 4-5 days to gather requirements and then design a best-in-class architecture considering your goals and capabilities, Integrations and customizations. Key out comes of Design and plan phase : Defined Success Criteria and KPIs Business Requirements Data migration strategy Feature Matrix Technical Architecture - scale to future needs Catalog setup, customizations, and integrations. Detailed Roadmap We believe architecting the overall solution and key system integrations aligned to your business long term strategy is crucial to ensuring a successful commerce platform implementation.</code> | <code>Please provide an overview of the workshop focusing on functionality, design, and architecture.</code> | <code>Streaming segmentation on Adobe Experience Platform allows customers to do segmentation in near real-time while focusing on data richness. With streaming segmentation, segment qualification now happens as streaming data lands into Platform, alleviating the need to schedule and run segmentation jobs. This essentially ensures that the right customers are targeted in near real-time and they are added/removed from a digital marketing activity across various channels including Advertising ecosystems such as DSP, Social, Search etc</code> | |
|
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
|
```json |
|
{ |
|
"distance_metric": "TripletDistanceMetric.EUCLIDEAN", |
|
"triplet_margin": 5 |
|
} |
|
``` |
|
|
|
### Evaluation Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
* Size: 956 evaluation samples |
|
* Columns: <code>positive</code>, <code>anchor</code>, and <code>negative</code> |
|
* Approximate statistics based on the first 956 samples: |
|
| | positive | anchor | negative | |
|
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
| type | string | string | string | |
|
| details | <ul><li>min: 8 tokens</li><li>mean: 139.34 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.97 tokens</li><li>max: 234 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 137.66 tokens</li><li>max: 512 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | negative | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code><<Customer Name>> can import, edit, manage, as well as manually create profiles in Adobe Campaign.Using your data, your marketers can also use the powerful, user-friendly segmentation and targeting features to create highly targeted, differentiated segments through the easy-to-use, point and click interface. Segmentation can be based on an unlimited number of conditions utilizing the underlying marketing data, including historical customer transactions, demographics and marketing history.Once you have created your segments, the criteria logic used to create the lists can be saved as a Pre-Defined Filter. These filters are then available to reuse and select from a library of filters, eliminating the need to recreate the logic each time. You can then modify these pre-set filters and these filters will be applied dynamically during execution.</code> | <code>The tool should have the capability to generate data profiles internally.</code> | <code>There is no limit to the number of concurrent users (with different users types) that Adobe solutions can support. We also provide scalable environment leveraging our flexible architecture.</code> | |
|
| <code>Adobe does have anti-malware and anti-virus solutions installed on all workstations, as well as all Windows-based production servers. Adobe does not install anti-malware/anti-virus on Linux-based servers. Adobe has advanced security tools for Linux. Included in this toolset is file hash checking, centralized process monitoring, critical file monitoring, forced host hardening, and OS Query for real-time security investigations.</code> | <code>The solution should include support for malware scanning.</code> | <code>Yes, Adobe Customer Journey Analytics has Retention rates view and cohort tables that show the percentage of users that return after their initial engagement within the desired date range. Presently, calculated metrics and participation attribution settings can be used to calculate the time between events for particular users. Please see: https://experienceleague.adobe.com/docs/analytics-platform/using/guided-analysis/retention/retention-rates.html?lang=enPlease see here for information on Cohort Analysis: https://experienceleague.adobe.com/docs/analytics-platform/using/cja-workspace/visualizations/cohort-table/cohort-analysis.html?lang=en</code> | |
|
| <code>The user interface is customizable at the user level and allows the authorized admin users to customize it to meet business requirements. The platform provides a central web console configuration manager that allows administrators to configure the solution seamlessly. OSGi is a fundamental element in the technology stack of Adobe Experience Manager. It is used to control the composite bundles of AEM and their configuration. More details: https://experienceleague.adobe.com/docs/experience-manager-cloud-service/content/implementing/deploying/configuring-osgi.html?lang=en</code> | <code>Can the user interface be customized for individual users or groups? If so, what aspects can be customized?</code> | <code>Yes, in data centers, DDoS mitigation contracts are in place with telecommunications providers to leverage DDoS "scrubbers" should they be necessary. In Public cloud provider locations, we leverage provided methodologies including auto expansion of capacity and DDoS mitigation where possible. Synthetic monitoring solutions including NewRelic, run synthetic transactions against our infrastructure to monitor application performance. When latency is detected, our 24x7x65 operations center is alerted and escalates with operational teams as necessary. For further information, please see the Infrastructure & Virtualization Security section in 3. CSA CAIQ v3.1 Adobe Experience Platform 2020 within the accompanying security pack</code> | |
|
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
|
```json |
|
{ |
|
"distance_metric": "TripletDistanceMetric.EUCLIDEAN", |
|
"triplet_margin": 5 |
|
} |
|
``` |
|
|
|
### Framework Versions |
|
- Python: 3.11.11 |
|
- Sentence Transformers: 3.4.1 |
|
- Transformers: 4.48.3 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.3.0 |
|
- Datasets: 3.3.2 |
|
- Tokenizers: 0.21.0 |
|
|
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## Citation |
|
|
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### 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", |
|
} |
|
``` |
|
|
|
#### TripletLoss |
|
```bibtex |
|
@misc{hermans2017defense, |
|
title={In Defense of the Triplet Loss for Person Re-Identification}, |
|
author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, |
|
year={2017}, |
|
eprint={1703.07737}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
|
``` |
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