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: What types of additional risks might future updates incorporate?
  sentences:
  - Inaccuracies in these labels can impact the “stability” or robustness of these
    benchmarks, which many GAI practitioners consider during the model selection process.
  - For example, when prompted to generate images of CEOs, doctors, lawyers, and judges,
    current text-to-image models underrepresent women and/or racial minorities , and
    people with disabilities .
  - Future updates may incorporate additional risks or provide further details on
    the risks identified below.
- source_sentence: What are some potential consequences of the abuse and misuse of
    AI systems by humans?
  sentences:
  - Even when trained on “clean” data, increasingly capable GAI models can synthesize
    or produce synthetic NCII and CSAM.
  - 3 the abuse, misuse, and unsafe repurposing by humans (adversarial or not ), and
    others result from interactions between a human and an AI system.
  - Energy and carbon emissions vary based on what is being done with the GAI model
    (i.e., pre -training, fine -tuning, inference), the modality of the content , hardware
    used, and type of task or application .
- source_sentence: What types of digital content can be included in GAI?
  sentences:
  - Errors in t hird-party GAI components can also have downstream impacts on accuracy
    and robustness .
  - In direct prompt injections, attackers might craft malicious prompts and input
    them directly to a GAI system , with a variety of downstream negative consequences
    to interconnected systems.
  - This can include images, videos, audio, text, and other digital content.” While
    not all GAI is derived from foundation models, for purposes of this document,
    GAI generally refers to generative foundation models .
- source_sentence: What are the implications of harmful bias and homogenization in
    relation to stereotypical content?
  sentences:
  - These risks provide a lens through which organizations can frame and execute risk
    management efforts.
  - 13 • Not every suggested action appl ies to every AI Actor14 or is relevant to
    every AI Actor Task .
  - The spread of denigrating or stereotypical content can also further exacerbate
    representational harms (see Harmful Bias and Homogenization below).
- source_sentence: What are the inventory exemptions defined in organizational policies
    for GAI systems embedded into application software?
  sentences:
  - Methods for creating smaller versions of train ed models, such as model distillation
    or compression, could reduce environmental impacts at inference time, but training
    and tuning such models may still contribute to their environmental impacts .
  - For example, predictive inferences made by GAI models based on PII or protected
    attributes c an contribute to adverse decisions , leading to representational
    or allocative harms to individuals or groups (see Harmful Bias and Homogenization
    below).
  - Information Security GV-1.6-002 Define any inventory exemptions in organizational
    policies for GAI systems embedded into application software .
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: 0.9
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.98
      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: 0.9
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3266666666666667
      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: 0.9
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.98
      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: 0.9563669441556807
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9417619047619047
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9417619047619047
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.9
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.98
      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: 0.9
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.3266666666666667
      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: 0.9
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.98
      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: 0.9563669441556807
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9417619047619047
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9417619047619047
      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 are the inventory exemptions defined in organizational policies for GAI systems embedded into application software?',
    'Information Security GV-1.6-002 Define any inventory exemptions in organizational policies for GAI systems embedded into application software .',
    'For example, predictive inferences made by GAI models based on PII or protected attributes c an contribute to adverse decisions , leading to representational or allocative harms to individuals or groups (see Harmful Bias and Homogenization below).',
]
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]
```

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### Direct Usage (Transformers)

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

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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   | 0.9        |
| cosine_accuracy@3   | 0.98       |
| cosine_accuracy@5   | 0.99       |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9        |
| cosine_precision@3  | 0.3267     |
| cosine_precision@5  | 0.198      |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9        |
| cosine_recall@3     | 0.98       |
| cosine_recall@5     | 0.99       |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9564     |
| cosine_mrr@10       | 0.9418     |
| **cosine_map@100**  | **0.9418** |
| dot_accuracy@1      | 0.9        |
| dot_accuracy@3      | 0.98       |
| dot_accuracy@5      | 0.99       |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.9        |
| dot_precision@3     | 0.3267     |
| dot_precision@5     | 0.198      |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.9        |
| dot_recall@3        | 0.98       |
| dot_recall@5        | 0.99       |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9564     |
| dot_mrr@10          | 0.9418     |
| dot_map@100         | 0.9418     |

<!--
## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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: 7 tokens</li><li>mean: 18.93 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 43.35 tokens</li><li>max: 165 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                             | sentence_1                                                                                                                                                                                            |
  |:-----------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What are indirect prompt injections and how can they exploit vulnerabilities?</code>                             | <code>Security researchers have already demonstrated how indirect prompt injections can exploit vulnerabilities by steal ing proprietary data or running malicious code remotely on a machine.</code> |
  | <code>What potential consequences can arise from exploiting vulnerabilities through indirect prompt injections?</code> | <code>Security researchers have already demonstrated how indirect prompt injections can exploit vulnerabilities by steal ing proprietary data or running malicious code remotely on a machine.</code> |
  | <code>What factors might organizations consider when tailoring their measurement of GAI risks?</code>                  | <code>Organizations may choose to tailor how they measure GAI risks based on these characteristics .</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
- `use_liger_kernel`: 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   | 0.9216         |
| 1.6667 | 50   | 0.9292         |
| 2.0    | 60   | 0.9361         |
| 3.0    | 90   | 0.9418         |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.1.1
- Transformers: 4.45.0
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citation

### BibTeX

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

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

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

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