sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:568 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Snowflake/snowflake-arctic-embed-m base_model:finetune:Snowflake/snowflake-arctic-embed-m model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:568
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What measures did the device manufacturer take to protect individuals
    from unwanted tracking?
  sentences:
  - "Tailored to the target of the explanation. Explanations should be targeted to\
    \ specific audiences and clearly state that audience. An explanation provided\
    \ to the subject of a decision might differ from one provided to an advocate,\
    \ or to a domain expert or decision maker. Tailoring should be assessed (e.g.,\
    \ via user experience research). \n43\n      NOTICE & \nEXPLANATION \nWHAT SHOULD\
    \ BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are\
    \ meant to serve as a blueprint for the development of additional \ntechnical\
    \ standards and practices that are tailored for particular sectors and contexts.\
    \ \nTailored to the level of risk. An assessment should be done to determine the\
    \ level of risk of the auto -"
  - '7

    • A device originally developed to help people track and find lost items has been
    used as a tool by stalkers to trackvictims’ locations in violation of their privacy
    and safet y. The device manufacturer took steps after release to

    protect people from unwanted tracking by alerting people on their phones when
    a device is found to be movingwith them over time and also by having the device
    make an occasional noise, but not all phones are ableto receive the notification
    and the devices remain a safety concern due to their misuse.

    8'
  - '-

    sonable expectations in a given context and with a focus on ensuring broad accessibility
    and protecting the public from especially harm

    -

    ful impacts. In some cases, a human or other alternative may be re -

    quired by law. You should have access to timely human consider -

    ation and remedy by a fallback and escalation process if an automat -

    ed system fails, it produces an error, or you would like to appeal or contest
    its impacts on you. Human consideration and fallback should be accessible, equitable,
    effective, maintained, accompanied by appropriate operator training, and should
    not impose an unrea

    -'
- source_sentence: Why is ongoing monitoring and mitigation important for automated
    systems after deployment?
  sentences:
  - "-\ntest its impacts on you \nProportionate. The availability of human consideration\
    \ and fallback, along with associated training and \nsafeguards against human\
    \ bias, should be proportionate to the potential of the automated system to meaning\
    \ -\nfully impact rights, opportunities, or access. Automated systems that have\
    \ greater control over outcomes, provide input to high-stakes decisions, relate\
    \ to sensitive domains, or otherwise have a greater potential to meaningfully\
    \ impact rights, opportunities, or access should have greater availability (e.g.,\
    \ staffing) and over\n-\nsight of human consideration and fallback mechanisms.\
    \ \nAccessible. Mechanisms for human consideration and fallback, whether in-person,\
    \ on paper, by phone, or"
  - "algorithmic 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 accounted for during the pre-deployment testing,\
    \ changes to the system after deployment, or changes to the context of use or\
    \ associated data. Monitoring and disparity assessment should be performed by\
    \ the entity deploying or using the automated system to examine whether the system\
    \ has led to algorithmic discrimina\n-"
  - "The expectations for automated systems are meant to serve as a blueprint for\
    \ the development of additional \ntechnical standards and practices that are tailored\
    \ for particular sectors and contexts. \nOngoing monitoring. Automated systems\
    \ should have ongoing monitoring procedures, including recalibra -\ntion procedures,\
    \ in place to ensure that their performance does not fall below an acceptable\
    \ level over time, \nbased on changing real-world conditions or deployment contexts,\
    \ post-deployment modification, or unexpect -"
- source_sentence: What should be included in the measurement of the impact of risks
    associated with automated systems?
  sentences:
  - "104 \n48\n      HUMAN ALTERNATIVES, \nCONSIDERATION, AND \nFALLBACK \nWHAT SHOULD\
    \ BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are\
    \ meant to serve as a blueprint for the development of additional \ntechnical\
    \ standards and practices that are tailored for particular sectors and contexts.\
    \ \nAn automated system should provide demonstrably effective mechanisms to opt\
    \ out in favor of a human alterna -\ntive, where appropriate, as well as timely\
    \ human consideration and remedy by a fallback system, with additional \nhuman\
    \ oversight and safeguards for systems used in sensitive domains, and with training\
    \ and assessment for any human-based portions of the system to ensure effectiveness."
  - collection and use is legal and consistent with the expectations of the people
    whose data is collected. User experience research should be conducted to confirm
    that people understand what data is being collected about them and how it will
    be used, and that this collection matches their expectations and desires.
  - "-\nsurement of the impact of risks should be included and balanced such that\
    \ high impact risks receive attention and mitigation proportionate with those\
    \ impacts. Automated systems with the intended purpose of violating the safety\
    \ of others should not be developed or used; systems with such safety violations\
    \ as identified unin\n-\ntended consequences should not be used until the risk\
    \ can be mitigated. Ongoing risk mitigation may necessi -\ntate rollback or significant\
    \ modification to a launched automated system. \n18\n      \n \n \n \n \n  SAFE\
    \ AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe\
    \ expectations for automated systems are meant to serve as a blueprint for the\
    \ development of additional"
- source_sentence: What measures should be taken to avoid "mission creep" when identifying
    goals for data collection?
  sentences:
  - 'narrow identified goals, to avoid "mission creep."  Anticipated data collection
    should be determined to be strictly necessary to the identified goals and should
    be minimized as much as possible. Data collected based on these identified goals
    and for a specific context should not be used in a different context without assessing
    for new privacy risks and implementing appropriate mitigation measures, which
    may include express consent. Clear timelines for data retention should be established,
    with data deleted as soon as possible in accordance with legal or policy-based
    limitations. Determined data retention timelines should be documented and justi

    -

    fied.'
  - with more and more companies tracking the behavior of the American public, building
    individual profiles based on this data, and using this granular-level information
    as input into automated systems that further track, profile, and impact the American
    public. Government agencies, particularly law enforcement agencies, also use and
    help develop a variety of technologies that enhance and expand surveillance capabilities,
    which similarly collect data used as input into other automated systems that directly
    impact people’s lives. Federal law has not grown to address the expanding scale
    of private data collection, or of the ability of governments at all levels to
    access that data and leverage the means of private collection.
  - "additional technical standards and practices that should be tailored for particular\
    \ sectors and contexts. While \nexisting laws informed the development of the\
    \ Blueprint for an AI Bill of Rights, this framework does not detail those laws\
    \ beyond providing them as examples, where appropriate, of existing protective\
    \ measures. This framework instead shares a broad, forward-leaning vision of recommended\
    \ principles for automated system development and use to inform private and public\
    \ involvement with these systems where they have the poten-tial to meaningfully\
    \ impact rights, opportunities, or access. Additionall y, this framework does\
    \ not analyze or"
- source_sentence: What types of data are considered sensitive according to the context
    provided?
  sentences:
  - "Provide the public with mechanisms for appropriate and meaningful consent, access,\
    \ and \ncontrol over their data \nUse-specific consent. Consent practices should\
    \ not allow for abusive surveillance practices. Where data \ncollectors or automated\
    \ systems seek consent, they should seek it for specific, narrow use contexts,\
    \ for specif -\nic time durations, and for use by specific entities. Consent should\
    \ not extend if any of these conditions change; consent should be re-acquired\
    \ before using data if the use case changes, a time limit elapses, or data is\
    \ trans\n-"
  - and home, work, or school environmental data); or have the reasonable potential
    to be used in ways that are likely to expose individuals to meaningful harm, such
    as a loss of privacy or financial harm due to identity theft. Data and metadata
    generated by or about those who are not yet legal adults is also sensitive, even
    if not related to a sensitive domain. Such data includes, but is not limited to,
    numerical, text, image, audio, or video data. “Sensitive domains” are those in
    which activities being conducted can cause material harms, including signifi
  - "that 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\n       \n \n  SAFE\
    \ AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe\
    \ expectations for automated systems are meant to serve as a blueprint for the\
    \ development of additional \ntechnical standards and practices that are tailored\
    \ for particular sectors and contexts. \nDerived data sources tracked and reviewed\
    \ carefully. Data that is derived from other data through"
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: 0.7677725118483413
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8862559241706162
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9241706161137441
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.981042654028436
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.7677725118483413
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29541864139020535
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1848341232227488
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0981042654028436
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7677725118483413
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8862559241706162
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9241706161137441
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.981042654028436
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8716745978729181
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8371304445948993
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.838229587684564
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.7677725118483413
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.8862559241706162
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.9241706161137441
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.981042654028436
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.7677725118483413
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.29541864139020535
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.1848341232227488
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.0981042654028436
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.7677725118483413
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.8862559241706162
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.9241706161137441
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.981042654028436
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.8716745978729181
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.8371304445948993
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.838229587684564
      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("sentence_transformers_model_id")
# Run inference
sentences = [
    'What types of data are considered sensitive according to the context provided?',
    'and home, work, or school environmental data); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful harm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about those who are not yet legal adults is also sensitive, even if not related to a sensitive domain. Such data includes, but is not limited to, numerical, text, image, audio, or video data. “Sensitive domains” are those in which activities being conducted can cause material harms, including signifi',
    'Provide the public with mechanisms for appropriate and meaningful consent, access, and \ncontrol over their data \nUse-specific consent. Consent practices should not allow for abusive surveillance practices. Where data \ncollectors or automated systems seek consent, they should seek it for specific, narrow use contexts, for specif -\nic time durations, and for use by specific entities. Consent should not extend if any of these conditions change; consent should be re-acquired before using data if the use case changes, a time limit elapses, or data is trans\n-',
]
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   | 0.7678     |
| cosine_accuracy@3   | 0.8863     |
| cosine_accuracy@5   | 0.9242     |
| cosine_accuracy@10  | 0.981      |
| cosine_precision@1  | 0.7678     |
| cosine_precision@3  | 0.2954     |
| cosine_precision@5  | 0.1848     |
| cosine_precision@10 | 0.0981     |
| cosine_recall@1     | 0.7678     |
| cosine_recall@3     | 0.8863     |
| cosine_recall@5     | 0.9242     |
| cosine_recall@10    | 0.981      |
| cosine_ndcg@10      | 0.8717     |
| cosine_mrr@10       | 0.8371     |
| **cosine_map@100**  | **0.8382** |
| dot_accuracy@1      | 0.7678     |
| dot_accuracy@3      | 0.8863     |
| dot_accuracy@5      | 0.9242     |
| dot_accuracy@10     | 0.981      |
| dot_precision@1     | 0.7678     |
| dot_precision@3     | 0.2954     |
| dot_precision@5     | 0.1848     |
| dot_precision@10    | 0.0981     |
| dot_recall@1        | 0.7678     |
| dot_recall@3        | 0.8863     |
| dot_recall@5        | 0.9242     |
| dot_recall@10       | 0.981      |
| dot_ndcg@10         | 0.8717     |
| dot_mrr@10          | 0.8371     |
| dot_map@100         | 0.8382     |

<!--
## 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: 568 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 568 samples:
  |         | sentence_0                                                                         | sentence_1                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 11 tokens</li><li>mean: 19.09 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 118.73 tokens</li><li>max: 160 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                       | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the purpose of the AI Bill of Rights mentioned in the context?</code>                                                              | <code>BLUEPRINT FOR AN <br>AI B ILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  | <code>When was the Blueprint for an AI Bill of Rights published?</code>                                                                          | <code>BLUEPRINT FOR AN <br>AI B ILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  | <code>What is the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy?</code> | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced  the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP)  was established by the National Science 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`: 10
- `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`: 10
- `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    | 29   | 0.7800         |
| 1.7241 | 50   | 0.8242         |
| 2.0    | 58   | 0.8382         |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+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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