README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:400
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: Why should manipulative and exploitative uses of AI be prohibited
    according to the context provided?
  sentences:
  - to operate without human intervention. The adaptiveness that an AI system could
    exhibit after deployment, refers to self-learning capabilities, allowing the system
    to change while in use. AI systems can be used on a stand-alone basis or as a component
    of a product, irrespective of whether the system is physically integrated into
    the product (embedded) or serves the functionality of the product without being
    integrated therein (non-embedded).
  - '(28)



    Aside from the many beneficial uses of AI, it can also be misused and provide
    novel and powerful tools for manipulative, exploitative and social control practices.
    Such practices are particularly harmful and abusive and should be prohibited because
    they contradict Union values of respect for human dignity, freedom, equality,
    democracy and the rule of law and fundamental rights enshrined in the Charter,
    including the right to non-discrimination, to data protection and to privacy and
    the rights of the child.













    (29)'
  - A Union legal framework laying down harmonised rules on AI is therefore needed
    to foster the development, use and uptake of AI in the internal market that at
    the same time meets a high level of protection of public interests, such as health
    and safety and the protection of fundamental rights, including democracy, the
    rule of law and environmental protection as recognised and protected by Union
    law. To achieve that objective, rules regulating the placing on the market, the
    putting into service and the use of certain AI systems should be laid down, thus
    ensuring the smooth functioning of the internal market and allowing those systems
    to benefit from the principle of free movement of goods and services. Those rules
    should be clear and robust
- source_sentence: What are the ethical principles mentioned in the context for developing
    voluntary best practices and standards?
  sentences:
  - encouraged to take into account, as appropriate, the ethical principles for the
    development of voluntary best practices and standards.
  - completed human activity that may be relevant for the purposes of the high-risk
    uses listed in an annex to this Regulation. Considering those characteristics,
    the AI system provides only an additional layer to a human activity with consequently
    lowered risk. That condition would, for example, apply to AI systems that are
    intended to improve the language used in previously drafted documents, for example
    in relation to professional tone, academic style of language or by aligning text
    to a certain brand messaging. The third condition should be that the AI system
    is intended to detect decision-making patterns or deviations from prior decision-making
    patterns. The risk would be lowered because the use of the AI system follows a previously
  - (17)
- source_sentence: How do climate change mitigation and adaptation relate to the conservation
    of biodiversity?
  sentences:
  - of the conditions referred to above should draw up documentation of the assessment
    before that system is placed on the market or put into service and should provide
    that documentation to national competent authorities upon request. Such a provider
    should be obliged to register the AI system in the EU database established under
    this Regulation. With a view to providing further guidance for the practical implementation
    of the conditions under which the AI systems listed in an annex to this Regulation
    are, on an exceptional basis, non-high-risk, the Commission should, after consulting
    the Board, provide guidelines specifying that practical implementation, completed
    by a comprehensive list of practical examples of use cases of AI systems that
  - the conservation and restoration of biodiversity and ecosystems and climate change
    mitigation and adaptation.
  - logistical point of view.
- source_sentence: How often should the risk-management system be reviewed and updated
    to maintain its effectiveness?
  sentences:
  - The risk-management system should consist of a continuous, iterative process that
    is planned and run throughout the entire lifecycle of a high-risk AI system. That
    process should be aimed at identifying and mitigating the relevant risks of AI
    systems on health, safety and fundamental rights. The risk-management system should
    be regularly reviewed and updated to ensure its continuing effectiveness, as well
    as justification and documentation of any significant decisions and actions taken
    subject to this Regulation. This process should ensure that the provider identifies
    risks or adverse impacts and implements mitigation measures for the known and
    reasonably foreseeable risks of AI systems to the health, safety and fundamental
    rights in light
  - solely on profiling them or on assessing their personality traits and characteristics
    should be prohibited. In any case, that prohibition does not refer to or touch
    upon risk analytics that are not based on the profiling of individuals or on the
    personality traits and characteristics of individuals, such as AI systems using
    risk analytics to assess the likelihood of financial fraud by undertakings on
    the basis of suspicious transactions or risk analytic tools to predict the likelihood
    of the localisation of narcotics or illicit goods by customs authorities, for
    example on the basis of known trafficking routes.
  - be clear and robust in protecting fundamental rights, supportive of new innovative
    solutions, enabling a European ecosystem of public and private actors creating
    AI systems in line with Union values and unlocking the potential of the digital
    transformation across all regions of the Union. By laying down those rules as
    well as measures in support of innovation with a particular focus on small and
    medium enterprises (SMEs), including startups, this Regulation supports the objective
    of promoting the European human-centric approach to AI and being a global leader
    in the development of secure, trustworthy and ethical AI as stated by the European
    Council (5), and it ensures the protection of ethical principles, as specifically
    requested by the
- source_sentence: How is the number 42 used in mathematical contexts?
  sentences:
  - (65)
  - (42)
  - to obtain prior authorisation. This could be, for example, a person involved in
    a crime, being unwilling, or unable due to an accident or a medical condition,
    to disclose their identity to law enforcement authorities.
pipeline_tag: sentence-similarity
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
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.875
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.875
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.875
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9484108127976215
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9305555555555555
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9305555555555557
      name: Cosine Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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': 1024, '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("arthikrangan/legal-ft-1")
# Run inference
sentences = [
    'How is the number 42 used in mathematical contexts?',
    '(42)',
    '(65)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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

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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   | 0.875      |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.875      |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.875      |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9484** |
| cosine_mrr@10       | 0.9306     |
| cosine_map@100      | 0.9306     |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 400 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 400 samples:
  |         | sentence_0                                                                         | sentence_1                                                                         |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 10 tokens</li><li>mean: 20.49 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 93.01 tokens</li><li>max: 186 tokens</li></ul> |
* Samples:
  | sentence_0                                                                   | sentence_1                                             |
  |:-----------------------------------------------------------------------------|:-------------------------------------------------------|
  | <code>What was requested by the European Parliament?</code>                  | <code>requested by the European Parliament (6).</code> |
  | <code>Who made the request to the European Parliament?</code>                | <code>requested by the European Parliament (6).</code> |
  | <code>What is the significance of the number 60 in the given context?</code> | <code>(60)</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`: 10
- `per_device_eval_batch_size`: 10
- `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`: 10
- `per_device_eval_batch_size`: 10
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| 1.0   | 40   | 0.9846         |
| 1.25  | 50   | 0.9923         |
| 2.0   | 80   | 0.9588         |
| 2.5   | 100  | 0.9692         |
| 3.0   | 120  | 0.9692         |
| 3.75  | 150  | 0.9539         |
| 4.0   | 160  | 0.9539         |
| 5.0   | 200  | 0.9588         |
| 6.0   | 240  | 0.9665         |
| 6.25  | 250  | 0.9588         |
| 7.0   | 280  | 0.9511         |
| 7.5   | 300  | 0.9511         |
| 8.0   | 320  | 0.9407         |
| 8.75  | 350  | 0.9484         |
| 9.0   | 360  | 0.9484         |
| 10.0  | 400  | 0.9484         |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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