---
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: Which entities are responsible for enforcing the requirements discussed
    in the context?
  sentences:
  - 'ALGORITHMIC DISCRIMINATION PROTECTIONS

    You should not face discrimination by algorithms and systems should be used and
    designed in'
  - 'SECTION TITLE

    HUMAN ALTERNATIVES, CONSIDERATION, AND FALLBACK

    You should be able to opt out, where appropriate, and have access to a person
    who can quickly'
  - requirements of the Federal agencies that enforce them. These principles are not
    intended to, and do not,
- source_sentence: How is safety addressed in the development process according to
    the context?
  sentences:
  - "TABLE OF CONTENTS\nFROM PRINCIPLES TO PRACTICE: A TECHNICAL COMPANION TO THE\
    \ BLUEPRINT \nFOR AN AI BILL OF RIGHTS \n \nUSING THIS TECHNICAL COMPANION\n \n\
    SAFE AND EFFECTIVE SYSTEMS"
  - "stemming from unintended, yet foreseeable, uses or \n \n \n \n \n  \n \n \nSECTION\
    \ TITLE\nBLUEPRINT FOR AN\nSAFE AND E \nYou should be protected from unsafe or\
    \ \ndeveloped with consultation from diverse"
  - tion or implemented under existing U.S. laws. For example, government surveillance,
    and data search and
- source_sentence: How should the deployment of automated systems be aligned with
    the principles for protecting the American public?
  sentences:
  - "public and private sector contexts; \nEqual opportunities, including equitable\
    \ access to education, housing, credit, employment, and other \nprograms; or,"
  - use, and deployment of automated systems to protect the rights of the American
    public in the age of artificial
  - five principles that should guide the design, use, and deployment of automated
    systems to protect the American
- source_sentence: Who should designers, developers, and deployers of automated systems
    seek permission from?
  sentences:
  - This important progress must not come at the price of civil rights or democratic
    values, foundational American
  - a blueprint for building and deploying automated systems that are aligned with
    democratic values and protect
  - context is collected. Designers, developers, and deployers of automated systems
    should seek your permission
- source_sentence: What changes are suggested for notice-and-choice practices regarding
    broad uses of data?
  sentences:
  - mated systems, and researchers developing innovative guardrails. Advocates, researchers,
    and government
  - tial to meaningfully impact rights, opportunities, or access. Additionally, this
    framework does not analyze or
  - understand notice-and-choice practices for broad uses of data should be changed.
    Enhanced protections and
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: 0.99
      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: 0.9
      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: 0.9
      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: 0.9601170111547646
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9464285714285714
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9464285714285714
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.9
      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: 0.9
      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: 0.9
      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: 0.9601170111547646
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.9464285714285714
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.9464285714285714
      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("dstampfli/finetuned-snowflake-arctic-embed-m")
# Run inference
sentences = [
    'What changes are suggested for notice-and-choice practices regarding broad uses of data?',
    'understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and',
    'tial to meaningfully impact rights, opportunities, or access. Additionally, this framework does not analyze or',
]
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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</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   | 0.99       |
| cosine_accuracy@5   | 0.99       |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9        |
| cosine_precision@3  | 0.33       |
| cosine_precision@5  | 0.198      |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9        |
| cosine_recall@3     | 0.99       |
| cosine_recall@5     | 0.99       |
| cosine_recall@10    | 1.0        |
| cosine_ndcg@10      | 0.9601     |
| cosine_mrr@10       | 0.9464     |
| **cosine_map@100**  | **0.9464** |
| dot_accuracy@1      | 0.9        |
| dot_accuracy@3      | 0.99       |
| dot_accuracy@5      | 0.99       |
| dot_accuracy@10     | 1.0        |
| dot_precision@1     | 0.9        |
| dot_precision@3     | 0.33       |
| dot_precision@5     | 0.198      |
| dot_precision@10    | 0.1        |
| dot_recall@1        | 0.9        |
| dot_recall@3        | 0.99       |
| dot_recall@5        | 0.99       |
| dot_recall@10       | 1.0        |
| dot_ndcg@10         | 0.9601     |
| dot_mrr@10          | 0.9464     |
| dot_map@100         | 0.9464     |

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### Recommendations

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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: 9 tokens</li><li>mean: 17.15 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 23.93 tokens</li><li>max: 46 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 BILL 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 BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> |
  | <code>What is the main purpose of the Blueprint for an AI Bill of Rights?</code>    | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was</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   | 0.9458         |
| 1.6667 | 50   | 0.9461         |
| 2.0    | 60   | 0.9461         |
| 3.0    | 90   | 0.9463         |
| 3.3333 | 100  | 0.9463         |
| 4.0    | 120  | 0.9464         |
| 5.0    | 150  | 0.9464         |


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

## Citation

### BibTeX

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

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

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

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