README.md

---
base_model: Snowflake/snowflake-arctic-embed-m
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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:800
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How have algorithms in hiring and credit decisions been shown to
    impact existing inequities, according to the context?
  sentences:
  - 'Shoshana Zuboff. The Age of Surveillance Capitalism: The Fight for a Human Future
    at the New Frontier of

    Power. Public Affairs. 2019.

    64. Angela Chen. Why the Future of Life Insurance May Depend on Your Online Presence.
    The Verge. Feb.

    7, 2019.

    https://www.theverge.com/2019/2/7/18211890/social-media-life-insurance-new-york-algorithms-big­

    data-discrimination-online-records

    68'
  - "SECTION TITLE­\nFOREWORD\nAmong the great challenges posed to democracy today\
    \ is the use of technology, data, and automated systems in \nways that threaten\
    \ the rights of the American public. Too often, these tools are used to limit\
    \ our opportunities and \nprevent our access to critical resources or services.\
    \ These problems are well documented. In America and around \nthe world, systems\
    \ supposed to help with patient care have proven unsafe, ineffective, or biased.\
    \ Algorithms used \nin hiring and credit decisions have been found to reflect\
    \ and reproduce existing unwanted inequities or embed \nnew harmful bias and discrimination.\
    \ Unchecked social media data collection has been used to threaten people’s"
  - "ways and to the greatest extent possible; where not possible, alternative privacy\
    \ by design safeguards should be \nused. Systems should not employ user experience\
    \ and design decisions that obfuscate user choice or burden \nusers with defaults\
    \ that are privacy invasive. Consent should only be used to justify collection\
    \ of data in cases \nwhere it can be appropriately and meaningfully given. Any\
    \ consent requests should be brief, be understandable \nin plain language, and\
    \ give you agency over data collection and the specific context of use; current\
    \ hard-to­\nunderstand notice-and-choice practices for broad uses of data should\
    \ be changed. Enhanced protections and"
- source_sentence: What factors should be considered when tailoring the extent of
    explanation provided by a system based on risk level?
  sentences:
  - 'ENDNOTES

    96. National Science Foundation. NSF Program on Fairness in Artificial Intelligence
    in Collaboration

    with Amazon (FAI). Accessed July 20, 2022.

    https://www.nsf.gov/pubs/2021/nsf21585/nsf21585.htm

    97. Kyle Wiggers. Automatic signature verification software threatens to disenfranchise
    U.S. voters.

    VentureBeat. Oct. 25, 2020.

    https://venturebeat.com/2020/10/25/automatic-signature-verification-software-threatens-to­

    disenfranchise-u-s-voters/

    98. Ballotpedia. Cure period for absentee and mail-in ballots. Article retrieved
    Apr 18, 2022.

    https://ballotpedia.org/Cure_period_for_absentee_and_mail-in_ballots

    99. Larry Buchanan and Alicia Parlapiano. Two of these Mail Ballot Signatures
    are by the Same Person.'
  - "data. “Sensitive domains” are those in which activities being conducted can cause\
    \ material harms, including signifi­\ncant adverse effects on human rights such\
    \ as autonomy and dignity, as well as civil liberties and civil rights. Domains\
    \ \nthat have historically been singled out as deserving of enhanced data protections\
    \ or where such enhanced protections \nare reasonably expected by the public include,\
    \ but are not limited to, health, family planning and care, employment, \neducation,\
    \ criminal justice, and personal finance. In the context of this framework, such\
    \ domains are considered \nsensitive whether or not the specifics of a system\
    \ context would necessitate coverage under existing law, and domains"
  - "transparent models should be used), rather than as an after-the-decision interpretation.\
    \ In other settings, the \nextent of explanation provided should be tailored to\
    \ the risk level. \nValid. The explanation provided by a system should accurately\
    \ reflect the factors and the influences that led \nto a particular decision,\
    \ and should be meaningful for the particular customization based on purpose,\
    \ target, \nand level of risk. While approximation and simplification may be necessary\
    \ for the system to succeed based on \nthe explanatory purpose and target of the\
    \ explanation, or to account for the risk of fraud or other concerns \nrelated\
    \ to revealing decision-making information, such simplifications should be done\
    \ in a scientifically"
- source_sentence: How do the five principles of the Blueprint for an AI Bill of Rights
    function as backstops against potential harms?
  sentences:
  - "programs; or, \nAccess to critical resources or services, such as healthcare,\
    \ financial services, safety, social services, \nnon-deceptive information about\
    \ goods and services, and government benefits. \nA list of examples of automated\
    \ systems for which these principles should be considered is provided in the \n\
    Appendix. The Technical Companion, which follows, offers supportive guidance for\
    \ any person or entity that \ncreates, deploys, or oversees automated systems.\
    \ \nConsidered together, the five principles and associated practices of the Blueprint\
    \ for an AI Bill of \nRights form an overlapping set of backstops against potential\
    \ harms. This purposefully overlapping"
  - "those laws beyond providing them as examples, where appropriate, of existing\
    \ protective measures. This \nframework instead shares a broad, forward-leaning\
    \ vision of recommended principles for automated system \ndevelopment and use\
    \ to inform private and public involvement with these systems where they have\
    \ the poten­\ntial to meaningfully impact rights, opportunities, or access. Additionally,\
    \ this framework does not analyze or \ntake a position on legislative and regulatory\
    \ proposals in municipal, state, and federal government, or those in \nother countries.\
    \ \nWe have seen modest progress in recent years, with some state and local governments\
    \ responding to these prob­"
  - "HUMAN ALTERNATIVES, \nCONSIDERATION, AND \nFALLBACK \nHOW THESE PRINCIPLES CAN\
    \ MOVE INTO PRACTICE\nReal-life examples of how these principles can become reality,\
    \ through laws, policies, and practical \ntechnical and sociotechnical approaches\
    \ to protecting rights, opportunities, and access. \nHealthcare “navigators” help\
    \ people find their way through online signup forms to choose \nand obtain healthcare.\
    \ A Navigator is “an individual or organization that's trained and able to help\
    \ \nconsumers, small businesses, and their employees as they look for health coverage\
    \ options through the \nMarketplace (a government web site), including completing\
    \ eligibility and enrollment forms.”106 For"
- source_sentence: What should be documented to justify the use of each data attribute
    and source in an automated system?
  sentences:
  - "hand and errors from data entry or other sources should be measured and limited.\
    \ Any data used as the target \nof a prediction process should receive particular\
    \ attention to the quality and validity of the predicted outcome \nor label to\
    \ ensure the goal of the automated system is appropriately identified and measured.\
    \ Additionally, \njustification should be documented for each data attribute and\
    \ source to explain why it is appropriate to use \nthat data to inform the results\
    \ of the automated system and why such use will not violate any applicable laws.\
    \ \nIn cases of high-dimensional and/or derived attributes, such justifications\
    \ can be provided as overall \ndescriptions of the attribute generation process\
    \ and appropriateness. \n19"
  - '13. National Artificial Intelligence Initiative Office. Agency Inventories of
    AI Use Cases. Accessed Sept. 8,

    2022. https://www.ai.gov/ai-use-case-inventories/

    14. National Highway Traffic Safety Administration. https://www.nhtsa.gov/

    15. See, e.g., Charles Pruitt. People Doing What They Do Best: The Professional
    Engineers and NHTSA. Public

    Administration Review. Vol. 39, No. 4. Jul.-Aug., 1979. https://www.jstor.org/stable/976213?seq=1

    16. The US Department of Transportation has publicly described the health and
    other benefits of these

    “traffic calming” measures. See, e.g.: U.S. Department of Transportation. Traffic
    Calming to Slow Vehicle'
  - "target measure; unobservable targets may result in the inappropriate use of proxies.\
    \ Meeting these \nstandards may require instituting mitigation procedures and\
    \ other protective measures to address \nalgorithmic discrimination, avoid meaningful\
    \ harm, and achieve equity goals. \nOngoing monitoring and mitigation. Automated\
    \ systems should be regularly monitored to assess algo­\nrithmic discrimination\
    \ that might arise from unforeseen interactions of the system with inequities\
    \ not \naccounted for during the pre-deployment testing, changes to the system\
    \ after deployment, or changes to the \ncontext of use or associated data. Monitoring\
    \ and disparity assessment should be performed by the entity"
- source_sentence: What are the implications of surveillance technologies on the rights
    and opportunities of underserved communities?
  sentences:
  - "manage risks associated with activities or business processes common across sectors,\
    \ such as the use of \nlarge language models (LLMs), cloud-based services, or\
    \ acquisition. \nThis document defines risks that are novel to or exacerbated by\
    \ the use of GAI. After introducing and \ndescribing these risks, the document\
    \ provides a set of suggested actions to help organizations govern, \nmap, measure,\
    \ and manage these risks. \n \n \n1 EO 14110 defines Generative AI as “the class\
    \ of AI models that emulate the structure and characteristics of input \ndata\
    \ in order to generate derived synthetic content. This can include images, videos,\
    \ audio, text, and other digital"
  - "rights, and community health, safety and welfare, as well ensuring better representation\
    \ of all voices, \nespecially those traditionally marginalized by technological\
    \ advances. Some panelists also raised the issue of \npower structures – providing\
    \ examples of how strong transparency requirements in smart city projects \nhelped\
    \ to reshape power and give more voice to those lacking the financial or political\
    \ power to effect change. \nIn discussion of technical and governance interventions\
    \ that that are needed to protect against the harms \nof these technologies, various\
    \ panelists emphasized the need for transparency, data collection, and \nflexible\
    \ and reactive policy development, analogous to how software is continuously updated\
    \ and deployed."
  - "limits its focus to both government and commercial use of surveillance technologies\
    \ when juxtaposed with \nreal-time or subsequent automated analysis and when such\
    \ systems have a potential for meaningful impact \non individuals’ or communities’\
    \ rights, opportunities, or access. \nUNDERSERVED COMMUNITIES: The term “underserved\
    \ communities” refers to communities that have \nbeen systematically denied a\
    \ full opportunity to participate in aspects of economic, social, and civic life,\
    \ as \nexemplified by the list in the preceding definition of “equity.” \n11"
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.805
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.925
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.965
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.97
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.805
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.30833333333333335
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.193
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09699999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.805
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.925
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.965
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.97
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8920929944400894
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8662916666666668
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8680077838827839
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.805
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.925
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.965
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.97
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.805
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.30833333333333335
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.193
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09699999999999999
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.805
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.925
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.965
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.97
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.8920929944400894
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.8662916666666668
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.8680077838827839
      name: Dot Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': False}) 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("checkthisout/finetuned_arctic")
# Run inference
sentences = [
    'What are the implications of surveillance technologies on the rights and opportunities of underserved communities?',
    'limits its focus to both government and commercial use of surveillance technologies when juxtaposed with \nreal-time or subsequent automated analysis and when such systems have a potential for meaningful impact \non individuals’ or communities’ rights, opportunities, or access. \nUNDERSERVED COMMUNITIES: The term “underserved communities” refers to communities that have \nbeen systematically denied a full opportunity to participate in aspects of economic, social, and civic life, as \nexemplified by the list in the preceding definition of “equity.” \n11',
    'manage risks associated with activities or business processes common across sectors, such as the use of \nlarge language models (LLMs), cloud-based services, or acquisition. \nThis document defines risks that are novel to or exacerbated by the use of GAI. After introducing and \ndescribing these risks, the document provides a set of suggested actions to help organizations govern, \nmap, measure, and manage these risks. \n \n \n1 EO 14110 defines Generative AI as “the class of AI models that emulate the structure and characteristics of input \ndata in order to generate derived synthetic content. This can include images, videos, audio, text, and other digital',
]
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.*
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## Evaluation

### Metrics

#### Information Retrieval

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.805     |
| cosine_accuracy@3   | 0.925     |
| cosine_accuracy@5   | 0.965     |
| cosine_accuracy@10  | 0.97      |
| cosine_precision@1  | 0.805     |
| cosine_precision@3  | 0.3083    |
| cosine_precision@5  | 0.193     |
| cosine_precision@10 | 0.097     |
| cosine_recall@1     | 0.805     |
| cosine_recall@3     | 0.925     |
| cosine_recall@5     | 0.965     |
| cosine_recall@10    | 0.97      |
| cosine_ndcg@10      | 0.8921    |
| cosine_mrr@10       | 0.8663    |
| **cosine_map@100**  | **0.868** |
| dot_accuracy@1      | 0.805     |
| dot_accuracy@3      | 0.925     |
| dot_accuracy@5      | 0.965     |
| dot_accuracy@10     | 0.97      |
| dot_precision@1     | 0.805     |
| dot_precision@3     | 0.3083    |
| dot_precision@5     | 0.193     |
| dot_precision@10    | 0.097     |
| dot_recall@1        | 0.805     |
| dot_recall@3        | 0.925     |
| dot_recall@5        | 0.965     |
| dot_recall@10       | 0.97      |
| dot_ndcg@10         | 0.8921    |
| dot_mrr@10          | 0.8663    |
| dot_map@100         | 0.868     |

<!--
## 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: 800 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 800 samples:
  |         | sentence_0                                                                        | sentence_1                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                              |
  | details | <ul><li>min: 11 tokens</li><li>mean: 20.1 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 127.42 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                              | sentence_1                                                                                                                                                                                              |
  |:------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What groups are involved in the processes that require cooperation and collaboration?</code>                      | <code>processes require the cooperation of and collaboration among industry, civil society, researchers, policymakers, <br>technologists, and the public. <br>14</code>                                 |
  | <code>Why is collaboration among different sectors important in these processes?</code>                                 | <code>processes require the cooperation of and collaboration among industry, civil society, researchers, policymakers, <br>technologists, and the public. <br>14</code>                                 |
  | <code>What did the panelists emphasize regarding the regulation of technology before it is built and instituted?</code> | <code>(before the technology is built and instituted). Various panelists also emphasized the importance of regulation <br>that includes limits to the type and cost of such technologies. <br>56</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`: steps
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: False
- `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`: False
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | cosine_map@100 |
|:-----:|:----:|:--------------:|
| 1.0   | 40   | 0.8449         |
| 1.25  | 50   | 0.8586         |
| 2.0   | 80   | 0.8693         |
| 2.5   | 100  | 0.8702         |
| 3.0   | 120  | 0.8703         |
| 3.75  | 150  | 0.8715         |
| 4.0   | 160  | 0.8659         |
| 5.0   | 200  | 0.8680         |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

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

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

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

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