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:600
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How can high compute resource utilization in training GAI models
    affect ecosystems?
  sentences:
  - "should not be used in education, work, housing, or in other contexts where the\
    \ use of such surveillance \ntechnologies is likely to limit rights, opportunities,\
    \ or access. Whenever possible, you should have access to \nreporting that confirms\
    \ your data decisions have been respected and provides an assessment of the \n\
    potential impact of surveillance technologies on your rights, opportunities, or\
    \ access. \nNOTICE AND EXPLANATION"
  - "Legal Disclaimer \nThe Blueprint for an AI Bill of Rights: Making Automated Systems\
    \ Work for the American People is a white paper \npublished by the White House\
    \ Office of Science and Technology Policy. It is intended to support the \ndevelopment\
    \ of policies and practices that protect civil rights and promote democratic values\
    \ in the building, \ndeployment, and governance of automated systems. \nThe Blueprint\
    \ for an AI Bill of Rights is non-binding and does not constitute U.S. government\
    \ policy. It \ndoes not supersede, modify, or direct an interpretation of any\
    \ existing statute, regulation, policy, or \ninternational instrument. It does\
    \ not constitute binding guidance for the public or Federal agencies and"
  - "or stereotyping content . \n4. Data Privacy: Impacts due to l eakage and unauthorized\
    \ use, disclosure , or de -anonymization of \nbiometric, health, location , or\
    \ other  personally identifiable information  or sensitive data .7 \n5. Environmental\
    \ Impacts: Impacts due to high compute resource utilization in training or \n\
    operating GAI models, and related outcomes that may adversely impact ecosystems.\
    \  \n6. Harmful Bias or Homogenization: Amplification and exacerbation of  historical,\
    \  societal, and \nsystemic  biases ; performance disparities8 between  sub- groups\
    \ or languages , possibly due to \nnon- representative training data , that result\
    \ in discrimination, amplification of biases, or"
- source_sentence: What are the potential risks associated with human-AI configuration
    in GAI systems?
  sentences:
  - "establish approved GAI technology and service provider lists.  Value Chain and\
    \ Component \nIntegration  \nGV-6.1-0 08 Maintain records of changes to content\
    \ made by third parties to promote content \nprovenance, including sources, timestamps,\
    \ metadata . Information Integrity ; Value Chain \nand Component Integration;\
    \ Intellectual Property  \nGV-6.1-0 09 Update and integrate due diligence processes\
    \ for GAI acquisition and \nprocurement vendor assessments to include intellectual\
    \ property, data privacy, security, and other risks. For example, update p rocesses\
    \ \nto: Address solutions that \nmay rely on embedded GAI technologies; Address\
    \ ongoing monitoring , \nassessments, and alerting, dynamic risk assessments,\
    \ and real -time reporting"
  - "could lead to homogenized outputs, including by amplifying any homogenization\
    \ from the model used to \ngenerate the synthetic training data . \nTrustworthy\
    \ AI Characteristics:  Fair with Harmful Bias Managed, Valid and Reliable  \n\
    2.7. Human -AI Configuration  \nGAI system  use can involve varying risks of misconfigurations\
    \  and poor interactions  between a system \nand a human who is interacti ng with\
    \ it. Humans bring their  unique perspectives , experiences , or domain -\nspecific\
    \ expertise to interactions with AI systems  but may not have detailed knowledge\
    \ of AI systems and \nhow they work.  As a result, h uman experts may be unnecessarily\
    \ “averse ” to GAI systems , and thus \ndeprive themselves  or others  of GAI’s\
    \ beneficial uses ."
  - "requests image features that are inconsistent with the  stereotypes.  Harmful\
    \ b ias in GAI models , which \nmay stem from their training data , can also \
    \ cause representational  harm s or perpetuate  or exacerbate  \nbias based on\
    \ race, gender, disability, or other protected classes .  \nHarmful b ias in GAI\
    \ systems can also lead to harms via disparities between how a model performs\
    \ for \ndifferent subgroups or languages  (e.g., an LLM may perform  less well\
    \ for non- English languages  or \ncertain dialects ). Such disparities can contribute\
    \ to discriminatory decision -making or amplification of \nexisting societal biases.\
    \  In addition,  GAI systems may be inappropriately trusted to perform similarly"
- source_sentence: What types of content are considered harmful biases in the context
    of information security?
  sentences:
  - "MS-2.5-0 05 Verify GAI system training data and TEVV data provenance, and that\
    \ fine -tuning  \nor retrieval- augmented generation data is grounded.  Information\
    \ Integrity  \nMS-2.5-0 06 Regularly review security and safety guardrails, especially\
    \ if the GAI system is \nbeing operated in novel circumstances. This includes\
    \ reviewing reasons why the \nGAI system was initially assessed as being safe\
    \ to deploy.  Information Security ; Dangerous , \nViolent, or Hateful  Content\
    \  \nAI Actor Tasks:  Domain Experts, TEVV"
  - "to diminished transparency or accountability for downstream users.  While  this\
    \ is a risk for traditional AI \nsystems  and some other digital technologies\
    \ , the risk is exacerbated for GAI due to the scale of the \ntraining data, which\
    \ may be too large for humans to vet; the  difficulty of training foundation models,\
    \ \nwhich leads to extensive reuse of limited numbers of models; an d the extent\
    \ to which GAI may be \nintegrat ed into other  devices and services.  As GAI\
    \ systems often involve many distinct  third -party \ncomponents and data sources\
    \ , it may be difficult to attribute issues in a system’s behavior to any one of\
    \ \nthese sources.  \nErrors in t hird-party GAI components can also have downstream\
    \ impacts  on accuracy and robustness ."
  - "biases in the generated content.  Information Security ; Harmful Bias \nand Homogenization\
    \  \nMG-2.2-005 Engage in due diligence to analyze  GAI output for harmful content,\
    \ potential \nmisinformation , and CBRN -related or NCII content . CBRN Information\
    \ or Capabilities ; \nObscene, Degrading, and/or \nAbusive Content ; Harmful Bias\
    \ and \nHomogenization ; Dangerous , \nViolent, or Hateful Content"
- source_sentence: What is the focus of the paper by Padmakumar et al (2024) regarding
    language models and content diversity?
  sentences:
  - "Content  \nMS-2.12- 002 Document anticipated environmental impacts of model development,\
    \ \nmaintenance, and deployment in product design decisions.  Environmental  \n\
    MS-2.12- 003 Measure or estimate environmental impacts (e.g., energy and water\
    \ \nconsumption) for training, fine tuning, and deploying models: Verify tradeoffs\
    \ \nbetween resources used at inference time versus additional resources required\
    \ at training time.  Environmental  \nMS-2.12- 004 Verify effectiveness of carbon\
    \ capture or offset programs  for GAI training and \napplications , and address\
    \ green -washing concerns . Environmental  \nAI Actor Tasks:  AI Deployment, AI\
    \ Impact Assessment, Domain Experts, Operation and Monitoring, TEVV"
  - "opportunities, undermine their privac y, or pervasively track their activity—often\
    \ without their knowledge or \nconsent. \nThese outcomes are deeply harmful—but\
    \ they are not inevitable. Automated systems have brought about extraor-\ndinary\
    \ benefits, from technology that helps farmers grow food more efficiently and\
    \ computers that predict storm \npaths, to algorithms that can identify diseases\
    \ in patients. These tools now drive important decisions across \nsectors, while\
    \ data is helping to revolutionize global industries. Fueled by the power of American\
    \ innovation, \nthese tools hold the potential to redefine every part of our society\
    \ and make life better for everyone."
  - "Publishing, Paris . https://doi.org/10.1787/d1a8d965- en \nOpenAI  (2023) GPT-4\
    \ System Card . https://cdn.openai.com/papers/gpt -4-system -card.pdf  \nOpenAI\
    \  (2024) GPT-4 Technical Report. https://arxiv.org/pdf/2303.08774  \nPadmakumar,\
    \ V. et al. (2024) Does writing with language models reduce content diversity?\
    \  ICLR . \nhttps://arxiv.org/pdf/2309.05196  \nPark,  P.  et. al. (2024)  AI\
    \ deception: A survey of  examples, risks, and potential solutions. Patterns,\
    \ 5(5).  \narXiv . https://arxiv.org/pdf/2308.14752  \nPartnership on AI  (2023)\
    \ Building a Glossary for Synthetic Media Transparency Methods, Part 1: Indirect\
    \ \nDisclosure . https://partnershiponai.org/glossary -for-synthetic -media- transparency\
    \ -methods -part-1-\nindirect -disclosure/"
- source_sentence: What are the key components involved in ensuring data quality and
    ethical considerations in AI systems?
  sentences:
  - "(such as where significant negative impacts are imminent, severe harms are actually\
    \ occurring, or large -scale risks could occur); and broad GAI negative risks,\
    \ \nincluding: Immature safety or risk cultures related to AI and GAI design,\
    \ development and deployment, public information integrity risks, including impacts\
    \ on democratic processes, unknown long -term performance characteristics of GAI.\
    \  Information Integrity ; Dangerous , \nViolent, or Hateful Content ; CBRN \n\
    Information or Capabilities  \nGV-1.3-007 Devise a plan to halt development or\
    \ deployment of a GAI system that poses unacceptable negative risk.  CBRN Information\
    \ and Capability ; \nInformation Security ; Information \nIntegrity  \nAI Actor\
    \ Tasks: Governance and Oversight"
  - "30 MEASURE 2.2:  Evaluations involving human subjects meet applicable requirements\
    \ (including human subject protection) and are \nrepresentative of the relevant\
    \ population.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.2-001 Assess and\
    \ manage statistical biases related to GAI content provenance through \ntechniques\
    \ such as re -sampling, re -weighting, or adversarial training.  Information Integrity\
    \ ; Information \nSecurity ; Harmful Bias and \nHomogenization  \nMS-2.2-002 Document\
    \ how content provenance data  is tracked  and how that data interact s \nwith\
    \  privacy and security . Consider : Anonymiz ing data to protect the privacy\
    \ of \nhuman subjects; Leverag ing privacy output filters; Remov ing any personally"
  - "Data quality; Model architecture (e.g., convolutional neural network, transformers,\
    \ etc.); Optimizatio n objectives; Training algorithms; RLHF \napproaches; Fine\
    \ -tuning or retrieval- augmented generation approaches; \nEvaluation data; Ethical\
    \ considerations; Legal and regulatory requirements.  Information Integrity ;\
    \ Harmful Bias \nand Homogenization  \nAI Actor Tasks:  AI Deployment, AI Impact\
    \ Assessment, Domain Experts, End -Users, Operation and Monitoring, TEVV  \n \n\
    MEASURE 2.10:  Privacy risk of the AI system – as identified in the MAP function\
    \ – is examined and documented.  \nAction ID  Suggested Action  GAI Risks  \n\
    MS-2.10- 001 Conduct AI red -teaming to assess issues  such as: Outputting of\
    \ training data"
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: 0.8
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.99
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.99
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.33000000000000007
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19799999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.99
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.99
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9195108324425135
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8916666666666667
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8916666666666666
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.8
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.99
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.99
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 1.0
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.8
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.33000000000000007
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.19799999999999998
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09999999999999998
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.8
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.99
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.99
      name: Dot Recall@5
    - type: dot_recall@10
      value: 1.0
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.9195108324425135
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.8916666666666667
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.8916666666666666
      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("XicoC/midterm-finetuned-arctic")
# Run inference
sentences = [
    'What are the key components involved in ensuring data quality and ethical considerations in AI systems?',
    'Data quality; Model architecture (e.g., convolutional neural network, transformers, etc.); Optimizatio n objectives; Training algorithms; RLHF \napproaches; Fine -tuning or retrieval- augmented generation approaches; \nEvaluation data; Ethical considerations; Legal and regulatory requirements.  Information Integrity ; Harmful Bias \nand Homogenization  \nAI Actor Tasks:  AI Deployment, AI Impact Assessment, Domain Experts, End -Users, Operation and Monitoring, TEVV  \n \nMEASURE 2.10:  Privacy risk of the AI system – as identified in the MAP function – is examined and documented.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.10- 001 Conduct AI red -teaming to assess issues  such as: Outputting of training data',
    '30 MEASURE 2.2:  Evaluations involving human subjects meet applicable requirements (including human subject protection) and are \nrepresentative of the relevant population.  \nAction ID  Suggested Action  GAI Risks  \nMS-2.2-001 Assess and manage statistical biases related to GAI content provenance through \ntechniques such as re -sampling, re -weighting, or adversarial training.  Information Integrity ; Information \nSecurity ; Harmful Bias and \nHomogenization  \nMS-2.2-002 Document how content provenance data  is tracked  and how that data interact s \nwith  privacy and security . Consider : Anonymiz ing data to protect the privacy of \nhuman subjects; Leverag ing privacy output filters; Remov ing any personally',
]
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

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

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8        |
| cosine_accuracy@3   | 0.99       |
| cosine_accuracy@5   | 0.99       |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8        |
| cosine_precision@3  | 0.33       |
| cosine_precision@5  | 0.198      |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8        |
| cosine_recall@3     | 0.99       |
| cosine_recall@5     | 0.99       |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9195     |
| cosine_mrr@10       | 0.8917     |
| **cosine_map@100**  | **0.8917** |
| dot_accuracy@1      | 0.8        |
| dot_accuracy@3      | 0.99       |
| dot_accuracy@5      | 0.99       |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.8        |
| dot_precision@3     | 0.33       |
| dot_precision@5     | 0.198      |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.8        |
| dot_recall@3        | 0.99       |
| dot_recall@5        | 0.99       |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9195     |
| dot_mrr@10          | 0.8917     |
| dot_map@100         | 0.8917     |

<!--
## 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: 600 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 600 samples:
  |         | sentence_0                                                                         | sentence_1                                                                          |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              |
  | details | <ul><li>min: 13 tokens</li><li>mean: 21.67 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 132.86 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                         | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the title of the NIST publication related to Artificial Intelligence Risk Management?</code>         | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600 -1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br>  <br>This publication is available free of charge from:  <br>https://doi.org/10.6028/NIST.AI.600 -1</code>                                                                                                                                                                                                                                                                 |
  | <code>Where can the NIST AI 600 -1 publication be accessed for free?</code>                                        | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600 -1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br>  <br>This publication is available free of charge from:  <br>https://doi.org/10.6028/NIST.AI.600 -1</code>                                                                                                                                                                                                                                                                 |
  | <code>What is the title of the publication released by NIST in July 2024 regarding artificial intelligence?</code> | <code>NIST Trustworthy and Responsible AI  <br>NIST AI 600 -1 <br>Artificial Intelligence Risk Management <br>Framework: Generative Artificial <br>Intelligence Profile <br> <br>  <br>This publication is available free of charge from:  <br>https://doi.org/10.6028/NIST.AI.600 -1 <br> <br>July 2024  <br> <br> <br> <br> <br>U.S. Department of Commerce  <br>Gina M. Raimondo, Secretary <br>National Institute of Standards and Technology  <br>Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology</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    | 30   | 0.8722         |
| 1.6667 | 50   | 0.8817         |
| 2.0    | 60   | 0.8867         |
| 3.0    | 90   | 0.8867         |
| 3.3333 | 100  | 0.8917         |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.2
- 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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