---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:150
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: Do you provide support 24/7?
sentences:
- 'How can we get started with your DevOps solutions?
Getting started is easy. Contact us through our website. We''ll schedule a consultation
to discuss your needs, evaluate your current infrastructure, and propose a customized
DevOps solution designed to achieve your goals.'
- 'This is our Portfolio
Introducing the world of Housing Finance& Banking Firm.
Corporate Website with 10 regional languages in India with analytics and user
personalization and Dashboard for Regional Managers, Sales Agents, etc. to manage
the Builder Requests, approve/deny Properties, manage visits and appointments,
manage leads, etc.
Introducing the world of Global Automotive Brand.We have implemented a Multi Locale
Multilingual Omnichannel platform for Royal Enfield. The platform supports public
websites, customer portals, internal portals, business applications for over 35+
different locations all over the world.
Developed Digital Platform for Students, Guardians, Teachers, Tutors, with AI/ML
in collaboration with Successive Technologies Inc, USA. Cloud, Dev-Sec-Ops &
Data Governance
Managing cloud provisioning and modernization alongside automated infrastructure,
event-driven microservices, containerization, DevOps, cybersecurity, and 24x7
monitoring support ensures efficient, secure, and responsive IT operations.'
- 'We are a New breed of innovative digital transformation agency, redefining storytelling
for an always-on world.
With roots dating back to 2017, we started as a pocket size team of enthusiasts
with a goal of helping traditional businesses transform and create dynamic, digital
cultures through disruptive strategies and agile deployment of innovative solutions.'
- source_sentence: What services do you offer for AI adoption?
sentences:
- 'In what ways can machine learning optimize our operations?
Machine learning algorithms can analyze operational data to identify inefficiencies,
predict maintenance needs, optimize supply chains, and automate repetitive tasks,
significantly improving operational efficiency and reducing costs.'
- "At Techchefz Digital, we specialize in guiding companies through the complexities\
\ of adopting and integrating Artificial Intelligence and Machine Learning technologies.\
\ Our consultancy services are designed to enhance your operational efficiency\
\ and decision-making capabilities across all sectors. With a global network of\
\ AI/ML experts and a commitment to excellence, we are your partners in transforming\
\ innovative possibilities into real-world achievements. \
\ \
\ \n DATA INTELLIGENCE PLATFORMS we\
\ specialize in\nTensorFlow\nDatabricks\nTableau\nPytorch\nOpenAI\nPinecone\""
- "SERVICES WE PROVIDE\nFlexible engagement models tailored to your needs\nWe specialize\
\ in comprehensive website audits that provide valuable insights and recommendations\
\ to enhance your online presence.\nDigital Strategy & Consulting\nCreating digital\
\ roadmap that transform your digital enterprise and produce a return on investment,\
\ basis our discovery framework, brainstorming sessions & current state analysis.\n\
\nPlatform Selection\nHelping you select the optimal digital experience, commerce,\
\ cloud and marketing platform for your enterprise.\n\nPlatform Builds\nDeploying\
\ next-gen scalable and agile enterprise digital platforms, along with multi-platform\
\ integrations. \nProduct Builds\nHelp you ideate, strategize, and engineer\
\ your product with help of our enterprise frameworks\nInfrastructure\nSpecialize\
\ in multi-cloud infrastructure helping you put forward the right cloud infrastructure\
\ and optimization strategy.\n\nManaged Services\nOperate and monitor your business-critical\
\ applications, data, and IT workloads, along with Application maintenance and\
\ operations.\nTeam Augmentation\nHelp you scale up and augment your existing\
\ team to solve your hiring challenges with our easy to deploy staff augmentation\
\ offerings.\""
- source_sentence: What challenges did the company face in its early days?
sentences:
- 'How do we do Custom Development ?
We follow below process to develop custom web or mobile Application on Agile Methodology,
breaking requirements in pieces and developing and shipping them with considering
utmost quality:
Requirements Analysis
We begin by understanding the client's needs and objectives for the website.
Identify key features, functionality, and any specific design preferences.
Project Planning
Then create a detailed project plan outlining the scope, timeline, and milestones.
Define the technology stack and development tools suitable for the project.
User Experience Design
Then comes the stage of Developing wireframes or prototypes to visualize the website's
structure and layout. We create a custom design that aligns with the brand identity
and user experience goals.
Development
After getting Sign-off on Design from Client, we break the requirements into Sprints
on Agile Methodology, and start developing them.'
- 'After a transformative scuba dive in the Maldives, Mayank Maggon made a pivotal
decision to depart from the corporate ladder in December 2016. Fueled by a clear
vision to revolutionize the digital landscape, Mayank set out to leverage the
best technology ingredients, crafting custom applications and digital ecosystems
tailored to clients'' specific needs, limitations, and budgets.
However, this solo journey was not without its challenges. Mayank had to initiate
the revenue engine by offering corporate trainings and conducting online batches
for tech training across the USA. He also undertook small projects and subcontracted
modules of larger projects for clients in the US, UK, and India. It was only after
this initial groundwork that Mayank was able to hire a group of interns, whom
he meticulously trained and groomed to prepare them for handling Enterprise Level
Applications. This journey reflects Mayank''s resilience, determination, and entrepreneurial
spirit in building TechChefz Digital from the ground up.
With a passion for innovation and a relentless drive for excellence, Mayank has
steered TechChefz Digital through strategic partnerships, groundbreaking projects,
and exponential growth. His leadership has been instrumental in shaping TechChefz
Digital into a leading force in the digital transformation arena, inspiring a
culture of innovation and excellence that continues to propel the company forward.'
- 'Our Solutions
Strategy & Digital Transformation
Innovate via digital transformation, modernize tech, craft product strategies,
enhance customer experiences, optimize data analytics, transition to cloud for
growth and efficiency
Product Engineering & Custom Development
Providing product development, enterprise web and mobile development, microservices
integrations, quality engineering, and application support services to drive innovation
and enhance operational efficiency.'
- source_sentence: What kind of data do you leverage for AI solutions?
sentences:
- 'In the Introducing the world of Global Insurance Firm, we crafted Effective Solutions
for Complex Problems and delieverd a comprehensive Website Development, Production
Support & Managed Services, we optimized customer journeys, integrate analytics,
CRM, ERP, and third-party applications, and implement cutting-edge technologies
for enhanced performance and efficiency
and achievied 200% Reduction in operational time & effort managing content & experience,
70% Reduction in Deployment Errors and Downtime, 2.5X Customer Engagement, Conversion
& Retention'
- 'Why do we need Microservices ?
Instead of building a monolithic application where all functionalities are tightly
integrated, microservices break down the system into modular and loosely coupled
services.
Scalability
Flexibility and Agility
Resilience and Fault Isolation
Technology Diversity
Continuous Delivery'
- Our AI/ML services pave the way for transformative change across industries, embodying
a client-focused approach that integrates seamlessly with human-centric innovation.
Our collaborative teams are dedicated to fostering growth, leveraging data, and
harnessing the predictive power of artificial intelligence to forge the next wave
of software excellence. We don't just deliver AI; we deliver the future.
- source_sentence: What do you guys do for digital strategy?
sentences:
- " What we do\n\nDigital Strategy\nCreating digital frameworks that transform\
\ your digital enterprise and produce a return on investment.\n\nPlatform Selection\n\
Helping you select the optimal digital experience, commerce, cloud and marketing\
\ platform for your enterprise.\n\nPlatform Builds\nDeploying next-gen scalable\
\ and agile enterprise digital platforms, along with multi-platform integrations.\n\
\nProduct Builds\nHelp you ideate, strategize, and engineer your product with\
\ help of our enterprise frameworks \n\nTeam Augmentation\nHelp you scale up and\
\ augment your existing team to solve your hiring challenges with our easy to\
\ deploy staff augmentation offerings .\nManaged Services\nOperate and monitor\
\ your business-critical applications, data, and IT workloads, along with Application\
\ maintenance and operations\n"
- "Introducing the world of\nGlobal Hospitality Firm\n\nIn this project, We focused\
\ on strategizing CX, diverse platform dev, travel booking, indemnity journeys,\
\ digital community, and managed services enhance travel experience and operational\
\ efficiency. \nStrategizing & defining the Customer Experience across business\
\ units and respective products / services,\nPlatform Development and Integrations\
\ across different tech stacks - Drupal, Magento, MERN, Microservices, Canvas\
\ LMS, OKTA SSO, AWS based Cloud Infrastructure, Build Automation\nTravel Packages\
\ Booking Platform with payments, subscriptions, real time booking, etc\nIndemnity\
\ & Self-Service Journeys\n\nAnd we achieved, 100% Improvement in Marketing Content,\
\ Real Time Prices & Inventories delivery. 80% Increase in Customer Retention,175%\
\ Increase in Partner & Vendor Operational Efficiency"
- 'Introducing the world of General Insurance Firm
In this project, we implemented Digital Solution and Implementation with Headless
Drupal as the CMS, and lightweight React JS (Next JS SSR on Node JS) with the
following features:
PWA & AMP based Web Pages
Page Speed Optimization
Reusable and scalable React JS / Next JS Templates and Components
Headless Drupal CMS with Content & Experience management, approval workflows,
etc for seamless collaboration between the business and marketing teams
Minimalistic Buy and Renewal Journeys for various products, with API integrations
and adherence to data compliances
We achieved 250% Reduction in Operational Time and Effort in managing the Content
& Experience for Buy & renew Journeys,220% Reduction in Customer Drops during
buy and renewal journeys, 300% Reduction in bounce rate on policy landing and
campaign pages'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
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: 0.18666666666666668
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5866666666666667
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18666666666666668
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19555555555555554
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13599999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07999999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18666666666666668
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5866666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.68
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48942651032647805
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.38962962962962955
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.398026376123124
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.24
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5733333333333334
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6533333333333333
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.24
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1911111111111111
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13066666666666663
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07999999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.24
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5733333333333334
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6533333333333333
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4991793077336057
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4047195767195766
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4124023465759078
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.21333333333333335
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5466666666666666
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6266666666666667
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7466666666666667
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.21333333333333335
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1822222222222222
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12533333333333332
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07466666666666665
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.21333333333333335
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5466666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6266666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7466666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4717065825983648
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.38359259259259254
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.39417579048787715
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.21333333333333335
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.52
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5733333333333334
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7066666666666667
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.21333333333333335
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11466666666666667
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07066666666666666
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.21333333333333335
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5733333333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7066666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.44415760022208445
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.36086772486772484
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.37364447853598953
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.14666666666666667
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5066666666666667
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6133333333333333
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14666666666666667
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10133333333333334
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06133333333333333
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14666666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5066666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6133333333333333
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3595031317594935
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.27981481481481474
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.29776557642203677
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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **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("Shashwat13333/bge-base-en-v1.5_v1")
# Run inference
sentences = [
'What do you guys do for digital strategy?',
' What we do\n\nDigital Strategy\nCreating digital frameworks that transform your digital enterprise and produce a return on investment.\n\nPlatform Selection\nHelping you select the optimal digital experience, commerce, cloud and marketing platform for your enterprise.\n\nPlatform Builds\nDeploying next-gen scalable and agile enterprise digital platforms, along with multi-platform integrations.\n\nProduct Builds\nHelp you ideate, strategize, and engineer your product with help of our enterprise frameworks \n\nTeam Augmentation\nHelp you scale up and augment your existing team to solve your hiring challenges with our easy to deploy staff augmentation offerings .\nManaged Services\nOperate and monitor your business-critical applications, data, and IT workloads, along with Application maintenance and operations\n',
'Introducing the world of General Insurance Firm\nIn this project, we implemented Digital Solution and Implementation with Headless Drupal as the CMS, and lightweight React JS (Next JS SSR on Node JS) with the following features:\nPWA & AMP based Web Pages\nPage Speed Optimization\nReusable and scalable React JS / Next JS Templates and Components\nHeadless Drupal CMS with Content & Experience management, approval workflows, etc for seamless collaboration between the business and marketing teams\nMinimalistic Buy and Renewal Journeys for various products, with API integrations and adherence to data compliances\n\nWe achieved 250% Reduction in Operational Time and Effort in managing the Content & Experience for Buy & renew Journeys,220% Reduction in Customer Drops during buy and renewal journeys, 300% Reduction in bounce rate on policy landing and campaign pages',
]
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]
```
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.1867 | 0.24 | 0.2133 | 0.2133 | 0.1467 |
| cosine_accuracy@3 | 0.5867 | 0.5733 | 0.5467 | 0.52 | 0.4 |
| cosine_accuracy@5 | 0.68 | 0.6533 | 0.6267 | 0.5733 | 0.5067 |
| cosine_accuracy@10 | 0.8 | 0.8 | 0.7467 | 0.7067 | 0.6133 |
| cosine_precision@1 | 0.1867 | 0.24 | 0.2133 | 0.2133 | 0.1467 |
| cosine_precision@3 | 0.1956 | 0.1911 | 0.1822 | 0.1733 | 0.1333 |
| cosine_precision@5 | 0.136 | 0.1307 | 0.1253 | 0.1147 | 0.1013 |
| cosine_precision@10 | 0.08 | 0.08 | 0.0747 | 0.0707 | 0.0613 |
| cosine_recall@1 | 0.1867 | 0.24 | 0.2133 | 0.2133 | 0.1467 |
| cosine_recall@3 | 0.5867 | 0.5733 | 0.5467 | 0.52 | 0.4 |
| cosine_recall@5 | 0.68 | 0.6533 | 0.6267 | 0.5733 | 0.5067 |
| cosine_recall@10 | 0.8 | 0.8 | 0.7467 | 0.7067 | 0.6133 |
| **cosine_ndcg@10** | **0.4894** | **0.4992** | **0.4717** | **0.4442** | **0.3595** |
| cosine_mrr@10 | 0.3896 | 0.4047 | 0.3836 | 0.3609 | 0.2798 |
| cosine_map@100 | 0.398 | 0.4124 | 0.3942 | 0.3736 | 0.2978 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 150 training samples
* Columns: anchor and positive
* Approximate statistics based on the first 150 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 7 tokens
- mean: 12.15 tokens
- max: 20 tokens
| - min: 20 tokens
- mean: 126.17 tokens
- max: 378 tokens
|
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Is it hard to move old systems to the cloud? | We offer custom software development, digital marketing strategies, and tailored solutions to drive tangible results for your business. Our expert team combines technical prowess with industry insights to propel your business forward in the digital landscape.
"Engage, analyze & target your customers
Digital transformation enables you to interact with customers across multiple channels, providing personalized experiences. This could include social media engagement, interactive websites, and mobile apps." "Empower your employees & partners
The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges." "Optimize & automate your operations
The push for digital transformation has led many companies to embrace cloud solutions. However, the migration and integration of legacy systems into the cloud often present challenges." "Transform your products
The push for digi... |
| What benefits does marketing automation offer for time management? | Our MarTech capabilities
Personalization
Involves tailoring marketing messages and experiences to individual customers. It enhances customer engagement, loyalty, and ultimately, conversion rates.
Marketing Automation
Marketing automation streamlines repetitive tasks such as email marketing, lead nurturing, and social media posting. It improves efficiency, saves time, and ensures timely communication with customers.
Customer Relationship Management
CRM systems help manage interactions with current and potential customers. They store customer data, track interactions, and facilitate communication, improving customer retention. |
| do you track customer behavior? | How can your recommendation engines improve our business?
Our recommendation engines are designed to analyze customer behavior and preferences to deliver personalized suggestions, enhancing user experience, increasing sales, and boosting customer retention. |
* Loss: [MatryoshkaLoss](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
- `gradient_accumulation_steps`: 4
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `push_to_hub`: True
- `hub_model_id`: Shashwat13333/bge-base-en-v1.5_v1
- `push_to_hub_model_id`: bge-base-en-v1.5_v1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: Shashwat13333/bge-base-en-v1.5_v1
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: bge-base-en-v1.5_v1
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional