sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:408 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:408
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What kinds of partnerships have you built?
  sentences:
  - TechChefz Digital has established its presence in two countries, showcasing its
    global reach and influence. The company’s headquarters is strategically located
    in Noida, India, serving as the central hub for its operations and leadership.
    In addition to the headquarters, TechChefz Digital has expanded its footprint
    with offices in Delaware, United States, allowing the company to cater to the
    North American market with ease and efficiency.
  - 'Accelerating Momentum: A year of strategic partnerships & Transformative Projects.
    In 2018, TechChefz continued to build on its strong foundation, expanding its
    global footprint and forging strategic partnerships. Our collaboration with digital
    agencies and system integrators propelled us into enterprise accounts, focusing
    on digital experience development. This year marked significant collaborations
    with leading automotive brands and financial institutions, enhancing our portfolio
    and establishing TechChefz as a trusted partner in the industry. '
  - 'Microservices Transformation Process

    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.


    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.


    Testing

    After each sprint we conduct thorough testing of the website to identify and fix
    any bugs or issues. Perform usability testing to ensure a positive user experience.

    Deployment

    After testing we deploy the website sprint by sprint, to a hosting environment,
    ensuring proper configuration for security and performance. Our expert DevOps
    team sets up any necessary domain and server configurations and ensure smooth
    running of website.'
- source_sentence: How can you help us grow our partnerships?
  sentences:
  - "Partner Experience (PX)\n From optimized collaboration tools to data-driven insights,\
    \ our solutions are designed to drive efficiency, transparency, and growth in\
    \ partner relationships. With a keen understanding of complexities of partner\
    \ ecosystems, we help enterprise brands unlock new opportunities, strengthen alliances,\
    \ and achieve shared success in today’s dynamic business environment."
  - 'Our Mission

    To decode data, and code new intelligence into products and automation, engineer,
    develop and deploy systems and applications that redefine experiences and realign
    business growth.'
  - 'Check out our latest news, announcements, and featured insights.


    Explore our latest insights and stay informed with our thought-provoking content.
    Dive in now for valuable perspectives.

    Our Featured Insights


    How UX and UI Work Together in Web Design


    Navigating the Post-Cookie Era: Strategies for Effective Targeting and Personalization


    Data-Driven Decision Making in Digital Advertising: Leveraging Analytics for Success


    SEO Unleashed: Navigating the Digital Landscape with Advanced Search Engine Optimization
    Tools


    Is manual testing replaced by automation Testing?'
- source_sentence: What kind of apps do you develop?
  sentences:
  - 'SERVICES WE PROVIDE

    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.'
  - 'Inception: Pioneering the Digital Frontier In our foundational year, TechChefz
    embarked on a journey of digital transformation, laying the groundwork for our
    future endeavors. We began working on Cab Accelerator Apps akin to Uber and Ola,
    deploying them across Europe, Africa, and Australia, marking our initial foray
    into global markets. Alongside, we successfully delivered technology trainings
    across USA & India.'
  - 'Career Opportunities

    We help Technology and Marketing Leaders succeed in their role, with our proven
    Digital Experience, Custom Development and Cloud transformation solutions. We
    have been helping traditional businesses transform and create dynamic, digital
    cultures through disruptive strategies and agile deployment of innovative solutions.


    2x Growth Y/Y


    30+ Technology Capabilities


    300+ Global Team Strength


    30+ Active Clients'
- source_sentence: What types of content can an Enterprise CMS manage?
  sentences:
  - 'Discover the right CMS for your Business Requirements

    Headless CMS

    They separate the backend content repository from the frontend presentation layer,
    allowing content to be delivered to any device or platform via APIs offering flexibility
    and scalability.



    Enterprise CMS

    ECMSs are more comprehensive systems designed to manage all types of content within
    an organization, including documents, images, videos, and other digital assets.'
  - '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.'
  - 'COMMERCE PLATFORMS

    Discover the strength of our partnership.

    Adobe Commerce Cloud

    A comprehensive e-commerce platform that allows businesses to create, manage,
    and optimize their online stores. Formerly known as Magento Commerce, Adobe Commerce
    Cloud provides a range of features and capabilities to help businesses create
    engaging online shopping experiences, manage their products and catalogs, process
    orders, and drive online sales.

    Magento

    An open-source e-commerce platform that allows businesses to create online stores
    and manage their digital operations. It was first released in 2008 and has since
    become one of the most popular e-commerce platforms in the world.

    Shopify

    Salesforce Commerce Cloud (SFCC)'
- source_sentence: How can your services help my company's workers feel better at
    work?
  sentences:
  - 'How do I figure out how much your services will cost?

    Determining the cost of our services is best achieved through a 15-30 minute discovery
    call, where we can understand your unique requirements. Following that, we will
    provide a transparent and detailed price within 24-48 hours tailored specifically
    to you'
  - "\nEmployee Experience (EX)\nWe are committed to revolutionizing the employee\
    \ experience for an enterprise brand or an evolving business through our tailored\
    \ digital solutions.\n Creating dynamic & intuitive platforms that empower employees,\
    \ enhance productivity, and foster a positive work environment\n Leveraging the\
    \ latest technologies to transform the way a brands engage with their workforce"
  - 'Our MarTech capabilities


    Analytics

    Analytics provide insights into customer behavior, campaign performance, and overall
    marketing effectiveness. By analyzing data, marketers can make informed decisions
    and optimize strategies for better results.


    Customer 360-degree View

    360 degree view of the customer integrates data from various touchpoints and channels
    to create a comprehensive profile. This helps marketers understand customers better.


    Real-time Segmentation

    This allows marketers to divide their audience into groups based on specific criteria.
    This enables personalized targeting and messaging, leading to higher engagement
    and conversions.'
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: 0.00980392156862745
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.06862745098039216
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4117647058823529
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5392156862745098
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.00980392156862745
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.02287581699346405
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08235294117647057
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05392156862745096
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.00980392156862745
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.06862745098039216
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4117647058823529
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5392156862745098
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2168475328522258
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11956504824151877
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.13310009311144466
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.09803921568627451
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4215686274509804
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0326797385620915
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08431372549019606
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04999999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.09803921568627451
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4215686274509804
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2064871760192189
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.11587690631808277
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1300339495420755
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.00980392156862745
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.0784313725490196
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.43137254901960786
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5196078431372549
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.00980392156862745
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0261437908496732
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08627450980392154
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05196078431372548
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.00980392156862745
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0784313725490196
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.43137254901960786
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5196078431372549
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.21536756324649986
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.1222494553376906
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.1338810033156541
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.00980392156862745
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.029411764705882353
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3627450980392157
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5196078431372549
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.00980392156862745
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.00980392156862745
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07254901960784314
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05196078431372548
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.00980392156862745
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.029411764705882353
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3627450980392157
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5196078431372549
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20400841250437815
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.10977279800809209
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.11979963733631876
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.0
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.0196078431372549
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.30392156862745096
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4117647058823529
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.0
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0065359477124183
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.060784313725490195
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04117647058823529
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.0196078431372549
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.30392156862745096
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4117647058823529
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.16222632731102943
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.0868308434484905
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.09855704024939387
      name: Cosine Map@100
---

# BGE base Financial Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 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("akashmaggon/bge-base-financial-matryoshkafinetuning-tcz-webiste")
# Run inference
sentences = [
    "How can your services help my company's workers feel better at work?",
    '\nEmployee Experience (EX)\nWe are committed to revolutionizing the employee experience for an enterprise brand or an evolving business through our tailored digital solutions.\n Creating dynamic & intuitive platforms that empower employees, enhance productivity, and foster a positive work environment\n Leveraging the latest technologies to transform the way a brands engage with their workforce',
    'How do I figure out how much your services will cost?\nDetermining the cost of our services is best achieved through a 15-30 minute discovery call, where we can understand your unique requirements. Following that, we will provide a transparent and detailed price within 24-48 hours tailored specifically to you',
]
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]
```

<!--
### 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.*
-->

## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](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   | 0.0098     | 0.0        | 0.0098     | 0.0098    | 0.0        |
| cosine_accuracy@3   | 0.0686     | 0.098      | 0.0784     | 0.0294    | 0.0196     |
| cosine_accuracy@5   | 0.4118     | 0.4216     | 0.4314     | 0.3627    | 0.3039     |
| cosine_accuracy@10  | 0.5392     | 0.5        | 0.5196     | 0.5196    | 0.4118     |
| cosine_precision@1  | 0.0098     | 0.0        | 0.0098     | 0.0098    | 0.0        |
| cosine_precision@3  | 0.0229     | 0.0327     | 0.0261     | 0.0098    | 0.0065     |
| cosine_precision@5  | 0.0824     | 0.0843     | 0.0863     | 0.0725    | 0.0608     |
| cosine_precision@10 | 0.0539     | 0.05       | 0.052      | 0.052     | 0.0412     |
| cosine_recall@1     | 0.0098     | 0.0        | 0.0098     | 0.0098    | 0.0        |
| cosine_recall@3     | 0.0686     | 0.098      | 0.0784     | 0.0294    | 0.0196     |
| cosine_recall@5     | 0.4118     | 0.4216     | 0.4314     | 0.3627    | 0.3039     |
| cosine_recall@10    | 0.5392     | 0.5        | 0.5196     | 0.5196    | 0.4118     |
| **cosine_ndcg@10**  | **0.2168** | **0.2065** | **0.2154** | **0.204** | **0.1622** |
| cosine_mrr@10       | 0.1196     | 0.1159     | 0.1222     | 0.1098    | 0.0868     |
| cosine_map@100      | 0.1331     | 0.13       | 0.1339     | 0.1198    | 0.0986     |

<!--
## 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: 408 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 408 samples:
  |         | anchor                                                                            | positive                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 8 tokens</li><li>mean: 12.71 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 94.18 tokens</li><li>max: 270 tokens</li></ul> |
* Samples:
  | anchor                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:-----------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What's it like working at Techchefz?</code>          | <code>Join one of the most resourceful tech teams<br><br>Discover your future with us. Explore opportunities, values, and culture. Join a dynamic and innovative team at Techchefz.<br><br>LIFE AT TECHCHEFZ<br>Make an Impact from Day One.<br><br>We believe in the power of collaboration to create, innovate, and develop groundbreaking solutions. Our teams work closely with clients and partners to co-create solutions that drive innovation and business growth.<br>Your new journey awaits!</code> |
  | <code>How can I contact TechChefz if I'm in the US?</code> | <code>TechChefz Digital has established its presence in two countries, showcasing its global reach and influence. The company’s headquarters is strategically located in Noida, India, serving as the central hub for its operations and leadership. In addition to the headquarters, TechChefz Digital has expanded its footprint with offices in Delaware, United States, allowing the company to cater to the North American market with ease and efficiency.</code>                                       |
  | <code>What results can I expect from your services?</code> | <code>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.</code>                                                                                                                                                                                                                              |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 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`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `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`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `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

</details>

### Training Logs
| Epoch | Step  | 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** | **0** | **0.2168**             | **0.2065**             | **0.2154**             | **0.204**              | **0.1622**            |

* 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+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->