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 organizations tailor their measurement of GAI risks based
    on specific characteristics?
  sentences:
  - "3 \nthe abuse, misuse, and unsafe repurposing by humans (adversarial or not),\
    \ and others result \nfrom interactions between a human and an AI system.  \n\
    • \nTime scale: GAI risks may materialize abruptly or across extended periods.\
    \ Examples include \nimmediate (and/or prolonged) emotional harm and potential\
    \ risks to physical safety due to the \ndistribution of harmful deepfake images,\
    \ or the long-term effect of disinformation on societal \ntrust in public institutions."
  - "12 \nCSAM. Even when trained on “clean” data, increasingly capable GAI models\
    \ can synthesize or produce \nsynthetic NCII and CSAM. Websites, mobile apps,\
    \ and custom-built models that generate synthetic NCII \nhave moved from niche\
    \ internet forums to mainstream, automated, and scaled online businesses.  \n\
    Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe, Privacy\
    \ Enhanced \n2.12. \nValue Chain and Component Integration"
  - "case context. \nOrganizations may choose to tailor how they measure GAI risks\
    \ based on these characteristics. They may \nadditionally wish to allocate risk\
    \ management resources relative to the severity and likelihood of \nnegative impacts,\
    \ including where and how these risks manifest, and their direct and material\
    \ impacts \nharms in the context of GAI use. Mitigations for model or system level\
    \ risks may differ from mitigations \nfor use-case or ecosystem level risks."
- source_sentence: What methods are suggested for measuring the reliability of content
    authentication techniques in the context of content provenance?
  sentences:
  - "updates. \nInformation Integrity; Data Privacy \nMG-3.2-003 \nDocument sources\
    \ and types of training data and their origins, potential biases \npresent in\
    \ the data related to the GAI application and its content provenance, \narchitecture,\
    \ training process of the pre-trained model including information on \nhyperparameters,\
    \ training duration, and any fine-tuning or retrieval-augmented \ngeneration processes\
    \ applied. \nInformation Integrity; Harmful Bias \nand Homogenization; Intellectual\
    \ \nProperty"
  - "Security \nMS-2.7-005 \nMeasure reliability of content authentication methods,\
    \ such as watermarking, \ncryptographic signatures, digital fingerprints, as well\
    \ as access controls, \nconformity assessment, and model integrity verification,\
    \ which can help support \nthe effective implementation of content provenance techniques.\
    \ Evaluate the \nrate of false positives and false negatives in content provenance,\
    \ as well as true \npositives and true negatives for verification. \nInformation\
    \ Integrity \nMS-2.7-006"
  - "GV-1.6-003 \nIn addition to general model, governance, and risk information,\
    \ consider the \nfollowing items in GAI system inventory entries: Data provenance\
    \ information \n(e.g., source, signatures, versioning, watermarks); Known issues\
    \ reported from \ninternal bug tracking or external information sharing resources\
    \ (e.g., AI incident \ndatabase, AVID, CVE, NVD, or OECD AI incident monitor);\
    \ Human oversight roles \nand responsibilities; Special rights and considerations\
    \ for intellectual property,"
- source_sentence: What are the suggested actions an organization can take to manage
    GAI risks?
  sentences:
  - "Information Integrity; Dangerous, \nViolent, or Hateful Content; CBRN \nInformation\
    \ or Capabilities \nGV-1.3-007 Devise a plan to halt development or deployment\
    \ of a GAI system that poses \nunacceptable negative risk. \nCBRN Information\
    \ and Capability; \nInformation Security; Information \nIntegrity \nAI Actor Tasks:\
    \ Governance and Oversight \n \nGOVERN 1.4: The risk management process and its\
    \ outcomes are established through transparent policies, procedures, and other"
  - "match the statistical properties of real-world data without disclosing personally\
    \ \nidentifiable information or contributing to homogenization. \nData Privacy;\
    \ Intellectual Property; \nInformation Integrity; \nConfabulation; Harmful Bias\
    \ and \nHomogenization \nAI Actor Tasks: AI Deployment, AI Impact Assessment,\
    \ Governance and Oversight, Operation and Monitoring \n \nMANAGE 2.3: Procedures\
    \ are followed to respond to and recover from a previously unknown risk when it\
    \ is identified. \nAction ID"
  - "• \nSuggested Action: Steps an organization or AI actor can take to manage GAI\
    \ risks.  \n• \nGAI Risks: Tags linking suggested actions with relevant GAI risks.\
    \  \n• \nAI Actor Tasks: Pertinent AI Actor Tasks for each subcategory. Not every\
    \ AI Actor Task listed will \napply to every suggested action in the subcategory\
    \ (i.e., some apply to AI development and \nothers apply to AI deployment).  \n\
    The tables below begin with the AI RMF subcategory, shaded in blue, followed by\
    \ suggested actions."
- source_sentence: How can harmful bias and homogenization be addressed in the context
    of human-AI configuration?
  sentences:
  - "on GAI, apply general fairness metrics (e.g., demographic parity, equalized odds,\
    \ \nequal opportunity, statistical hypothesis tests), to the pipeline or business\
    \ \noutcome where appropriate; Custom, context-specific metrics developed in \n\
    collaboration with domain experts and affected communities; Measurements of \n\
    the prevalence of denigration in generated content in deployment (e.g., sub-\n\
    sampling a fraction of traffic and manually annotating denigrating content). \n\
    Harmful Bias and Homogenization;"
  - "MP-5.1-001 Apply TEVV practices for content provenance (e.g., probing a system's\
    \ synthetic \ndata generation capabilities for potential misuse or vulnerabilities.\
    \ \nInformation Integrity; Information \nSecurity \nMP-5.1-002 \nIdentify potential\
    \ content provenance harms of GAI, such as misinformation or \ndisinformation,\
    \ deepfakes, including NCII, or tampered content. Enumerate and \nrank risks based\
    \ on their likelihood and potential impact, and determine how well"
  - "MS-1.3-002 \nEngage in internal and external evaluations, GAI red-teaming, impact\
    \ \nassessments, or other structured human feedback exercises in consultation\
    \ \nwith representative AI Actors with expertise and familiarity in the context\
    \ of \nuse, and/or who are representative of the populations associated with the\
    \ \ncontext of use. \nHuman-AI Configuration; Harmful \nBias and Homogenization;\
    \ CBRN \nInformation or Capabilities \nMS-1.3-003"
- source_sentence: How can structured human feedback exercises, such as GAI red-teaming,
    contribute to GAI risk measurement and management?
  sentences:
  - "rank risks based on their likelihood and potential impact, and determine how\
    \ well \nprovenance solutions address specific risks and/or harms. \nInformation\
    \ Integrity; Dangerous, \nViolent, or Hateful Content; \nObscene, Degrading, and/or\
    \ \nAbusive Content \nMP-5.1-003 \nConsider disclosing use of GAI to end users\
    \ in relevant contexts, while considering \nthe objective of disclosure, the context\
    \ of use, the likelihood and magnitude of the"
  - "15 \nGV-1.3-004 Obtain input from stakeholder communities to identify unacceptable\
    \ use, in \naccordance with activities in the AI RMF Map function. \nCBRN Information\
    \ or Capabilities; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias\
    \ \nand Homogenization; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005\
    \ \nMaintain an updated hierarchy of identified and expected GAI risks connected\
    \ to \ncontexts of GAI model advancement and use, potentially including specialized\
    \ risk"
  - "AI-generated content, for example by employing techniques like chaos \nengineering\
    \ and seeking stakeholder feedback. \nInformation Integrity \nMS-1.1-008 \nDefine\
    \ use cases, contexts of use, capabilities, and negative impacts where \nstructured\
    \ human feedback exercises, e.g., GAI red-teaming, would be most \nbeneficial for\
    \ GAI risk measurement and management based on the context of \nuse. \nHarmful\
    \ Bias and \nHomogenization; CBRN \nInformation or Capabilities \nMS-1.1-009"
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: 0.85
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.96
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.98
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.85
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.31999999999999995
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19599999999999995
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.85
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.96
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.98
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9342942871848772
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9124166666666668
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9124166666666668
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.85
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.96
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.98
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 1.0
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.85
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.31999999999999995
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.19599999999999995
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09999999999999998
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.85
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.96
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.98
      name: Dot Recall@5
    - type: dot_recall@10
      value: 1.0
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.9342942871848772
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9124166666666668
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9124166666666668
      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("Cheselle/finetuned-arctic-sentence")
# Run inference
sentences = [
    'How can structured human feedback exercises, such as GAI red-teaming, contribute to GAI risk measurement and management?',
    'AI-generated content, for example by employing techniques like chaos \nengineering and seeking stakeholder feedback. \nInformation Integrity \nMS-1.1-008 \nDefine use cases, contexts of use, capabilities, and negative impacts where \nstructured human feedback exercises, e.g., GAI red-teaming, would be most \nbeneficial for GAI risk measurement and management based on the context of \nuse. \nHarmful Bias and \nHomogenization; CBRN \nInformation or Capabilities \nMS-1.1-009',
    '15 \nGV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use, in \naccordance with activities in the AI RMF Map function. \nCBRN Information or Capabilities; \nObscene, Degrading, and/or \nAbusive Content; Harmful Bias \nand Homogenization; Dangerous, \nViolent, or Hateful Content \nGV-1.3-005 \nMaintain an updated hierarchy of identified and expected GAI risks connected to \ncontexts of GAI model advancement and use, potentially including specialized risk',
]
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   | 0.85       |
| cosine_accuracy@3   | 0.96       |
| cosine_accuracy@5   | 0.98       |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.85       |
| cosine_precision@3  | 0.32       |
| cosine_precision@5  | 0.196      |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.85       |
| cosine_recall@3     | 0.96       |
| cosine_recall@5     | 0.98       |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9343     |
| cosine_mrr@10       | 0.9124     |
| **cosine_map@100**  | **0.9124** |
| dot_accuracy@1      | 0.85       |
| dot_accuracy@3      | 0.96       |
| dot_accuracy@5      | 0.98       |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.85       |
| dot_precision@3     | 0.32       |
| dot_precision@5     | 0.196      |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.85       |
| dot_recall@3        | 0.96       |
| dot_recall@5        | 0.98       |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9343     |
| dot_mrr@10          | 0.9124     |
| dot_map@100         | 0.9124     |

<!--
## 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: 11 tokens</li><li>mean: 21.05 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 91.74 tokens</li><li>max: 335 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                    | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:--------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the title of the publication related to Artificial Intelligence Risk Management by NIST?</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> <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> <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 AI 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> <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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 3
- `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    | 38   | 0.9033         |
| 1.3158 | 50   | 0.9067         |
| 2.0    | 76   | 0.9124         |


### 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: 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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