metadata

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: sentence-transformers/msmarco-distilbert-base-v4
widget:
  - source_sentence: What services does Techchefz Digital offer for AI adoption?
    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.
      - >-
        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.                                                                                                                                            
         DATA INTELLIGENCE PLATFORMS we specialize in
        TensorFlow

        Databricks

        Tableau

        Pytorch

        OpenAI

        Pinecone"
      - >-
        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: Do you provide support 24/7?
    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&#39;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&#39;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.
      - >-
        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.
      - >-
        SERVICES WE PROVIDE

        Flexible engagement models tailored to your needs

        We specialize in comprehensive website audits that provide valuable
        insights and recommendations to enhance your online presence.

        Digital Strategy & Consulting

        Creating digital roadmap that transform your digital enterprise and
        produce a return on investment, basis our discovery framework,
        brainstorming sessions & current state analysis.


        Platform Selection

        Helping you select the optimal digital experience, commerce, cloud and
        marketing platform for your enterprise.


        Platform Builds

        Deploying next-gen scalable and agile enterprise digital platforms,
        along with multi-platform integrations.   

        Product Builds

        Help you ideate, strategize, and engineer your product with help of our
        enterprise frameworks

        Infrastructure

        Specialize in multi-cloud infrastructure helping you put forward the
        right cloud infrastructure and optimization strategy.


        Managed Services

        Operate and monitor your business-critical applications, data, and IT
        workloads, along with Application maintenance and operations.

        Team Augmentation

        Help 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:
      - >-
        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
      - >-
        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.
      - >-
        What makes your DevOps solutions stand out from the competition?

        Our DevOps solutions stand out due to our personalized approach,
        extensive expertise, and commitment to innovation. We focus on
        delivering measurable results, such as reduced deployment times,
        improved system reliability, and enhanced security, ensuring you get the
        maximum benefit from our services.
  - source_sentence: What kind of data do you leverage for AI solutions?
    sentences:
      - >-
        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.
      - >-
        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.
      - >-
        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 managed services does TechChefz provide ?
    sentences:
      - >2
          What we do

        Digital Strategy

        Creating digital frameworks that transform your digital enterprise and
        produce a return on investment.


        Platform Selection

        Helping you select the optimal digital experience, commerce, cloud and
        marketing platform for your enterprise.


        Platform Builds

        Deploying next-gen scalable and agile enterprise digital platforms,
        along with multi-platform integrations.


        Product Builds

        Help you ideate, strategize, and engineer your product with help of our
        enterprise frameworks 


        Team Augmentation

        Help you scale up and augment your existing team to solve your hiring
        challenges with our easy to deploy staff augmentation offerings .

        Managed Services

        Operate and monitor your business-critical applications, data, and IT
        workloads, along with Application maintenance and operations
      - >-
        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
      - >-
        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
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: 0.10666666666666667
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.49333333333333335
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5333333333333333
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6266666666666667
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.10666666666666667
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16444444444444445
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10666666666666667
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06266666666666666
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.10666666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.49333333333333335
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5333333333333333
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6266666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3696947495406473
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2864550264550264
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2993424751990436
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.10666666666666667
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4666666666666667
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5333333333333333
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6133333333333333
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.10666666666666667
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15555555555555556
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10666666666666667
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06133333333333333
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.10666666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4666666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5333333333333333
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6133333333333333
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3702942720383175
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.29092063492063486
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3047495006876888
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.14666666666666667
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4533333333333333
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.49333333333333335
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14666666666666667
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1511111111111111
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09866666666666667
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14666666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4533333333333333
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.49333333333333335
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.37318151343456746
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3006455026455026
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.31352550381063704
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.12
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4533333333333333
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.49333333333333335
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1511111111111111
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09866666666666667
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.12
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4533333333333333
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.49333333333333335
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.349467831727335
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.26956613756613756
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2814743968696581
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.16
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.38666666666666666
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.4666666666666667
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5466666666666666
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.16
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1288888888888889
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09333333333333335
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05466666666666666
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.16
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.38666666666666666
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4666666666666667
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5466666666666666
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.34485137335598726
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.28099999999999997
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.29532589563098727
            name: Cosine Map@100

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from sentence-transformers/msmarco-distilbert-base-v4. 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: sentence-transformers/msmarco-distilbert-base-v4
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
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Shashwat13333/msmarco-distilbert-base-v4")
# Run inference
sentences = [
    'What managed services does TechChefz provide ?',
    '  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',
    '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\nand achievied 200% Reduction in operational time & effort managing content & experience, 70% Reduction in Deployment Errors and Downtime, 2.5X Customer Engagement, Conversion & Retention',
]
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

Metric	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.1067	0.1067	0.1467	0.12	0.16
cosine_accuracy@3	0.4933	0.4667	0.4533	0.4533	0.3867
cosine_accuracy@5	0.5333	0.5333	0.4933	0.4933	0.4667
cosine_accuracy@10	0.6267	0.6133	0.6	0.6	0.5467
cosine_precision@1	0.1067	0.1067	0.1467	0.12	0.16
cosine_precision@3	0.1644	0.1556	0.1511	0.1511	0.1289
cosine_precision@5	0.1067	0.1067	0.0987	0.0987	0.0933
cosine_precision@10	0.0627	0.0613	0.06	0.06	0.0547
cosine_recall@1	0.1067	0.1067	0.1467	0.12	0.16
cosine_recall@3	0.4933	0.4667	0.4533	0.4533	0.3867
cosine_recall@5	0.5333	0.5333	0.4933	0.4933	0.4667
cosine_recall@10	0.6267	0.6133	0.6	0.6	0.5467
cosine_ndcg@10	0.3697	0.3703	0.3732	0.3495	0.3449
cosine_mrr@10	0.2865	0.2909	0.3006	0.2696	0.281
cosine_map@100	0.2993	0.3047	0.3135	0.2815	0.2953

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: 12.45 tokens
max: 18 tokens

min: 20 tokens
mean: 126.17 tokens
max: 378 tokens

	anchor	positive
type	string	string
details	min: 7 tokens mean: 12.45 tokens max: 18 tokens	min: 20 tokens mean: 126.17 tokens max: 378 tokens

Samples:

anchor	positive
`How can digital transformation enhance customer interactions across multiple channels?`	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...
`How does a CRM system improve customer retention?`	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.
`How can your recommendation engines improve our business?`	`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 with these parameters:

{
    "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/msmarco-distilbert-base-v4
push_to_hub_model_id: msmarco-distilbert-base-v4
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/msmarco-distilbert-base-v4
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: msmarco-distilbert-base-v4
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

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.2105	1	3.5757	-	-	-	-	-
0.8421	4	-	0.3563	0.3543	0.3378	0.3681	0.3077
1.2105	5	4.4031	-	-	-	-	-
1.8421	8	-	0.3652	0.3547	0.3574	0.3542	0.3579
2.4211	10	3.3423	-	-	-	-	-
2.8421	12	-	0.3783	0.3680	0.3558	0.3807	0.3408
3.6316	15	2.3695	-	-	-	-	-
3.8421	16	-	0.3697	0.3703	0.3732	0.3495	0.3449

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu124
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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

@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}
}