snowflake-arctic-legal-ft-1

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

This is a sentence-transformers model finetuned from 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 <!-- 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
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("vin00d/snowflake-arctic-legal-ft-1")
# Run inference
sentences = [
    "How does a fraudulent transfer relate to a debtor's intent in bankruptcy cases?",
    "A serious crime, usually punishable by at least one year in prison.\nFile\nTo place a paper in the official custody of the clerk of court to enter into the files or records\nof a case.\nFraudulent transfer\nA transfer of a debtor's property made with intent to defraud or for which the debtor\nreceives less than the transferred property's value.\nFresh start\nThe characterization of a debtor's status after bankruptcy, i.e., free of most debts. (Giving\ndebtors a fresh start is one purpose of the Bankruptcy Code.)\nG\nGrand jury\nA body of 16-23 citizens who listen to evidence of criminal allegations, which is presented by\nthe prosecutors, and determine whether there is probable cause to believe an individual",
    '-3-\nArgument:  A reason given in proof or rebuttal to persuade a judge or jury.\nAt Issue:  Whenever the parties to an action come to a point in the pleadings or argument which\nis affirmed on one side and denied on the other, the points  are said to be "at issue".\nAttachment:  The taking of property into legal custody by an enforcement officer (See specialty\nsection: Recovery of Chattel).\nAttestation:  The act of witnessing an instrument in writing at the request of the party making the\ninstrument and signing  it as a witness.\nAttorney of Record:  Attorney whose name appears in the court’s  records or files of a case.\nAward:  A decision of an Arbitrator, judge or jury.\n-B-',
]
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]

<!-- ### 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>

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Training Details

Training Dataset

Unnamed Dataset

• Size: 210 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 210 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 9 tokens</li><li>mean: 17.36 tokens</li><li>max: 33 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 122.9 tokens</li><li>max: 192 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>What is the purpose of the glossary of common legal terms provided in the context? </code>	<code>GLOSSARY ‐ COMMON LEGAL TERMS<br>NOTE:  The following definitions are not legal definitions.  Rather, these definitions are<br>intended to give you a general idea of the meanings of common legal words.  For <br>comprehensive Definitions of legal terms, you may wish to consult a legal dictionary<br> “Black’s Law Dictionary” is one such legal dictionary which is usually available at<br> most law libraries.<br>This glossary of common legal terms is also available on‐line at:<br>http://www.nycourts.gov/lawlibraries/glossary.shtml<br> <br>ADDITIONAL ON‐LINE RESOURCES:<br>http://www.nolo.com/glossary.cfm <br>Nolo’s on‐line legal dictionary.<br>http://www.law‐dictionary.org/<br>Free on‐line legal dictionary search engine.<br>http://www.law.cornell.edu/wex</code>
  <code>Where can one find a comprehensive legal dictionary for more detailed definitions of legal terms?</code>	<code>GLOSSARY ‐ COMMON LEGAL TERMS<br>NOTE:  The following definitions are not legal definitions.  Rather, these definitions are<br>intended to give you a general idea of the meanings of common legal words.  For <br>comprehensive Definitions of legal terms, you may wish to consult a legal dictionary<br> “Black’s Law Dictionary” is one such legal dictionary which is usually available at<br> most law libraries.<br>This glossary of common legal terms is also available on‐line at:<br>http://www.nycourts.gov/lawlibraries/glossary.shtml<br> <br>ADDITIONAL ON‐LINE RESOURCES:<br>http://www.nolo.com/glossary.cfm <br>Nolo’s on‐line legal dictionary.<br>http://www.law‐dictionary.org/<br>Free on‐line legal dictionary search engine.<br>http://www.law.cornell.edu/wex</code>
  <code>What organization maintains the legal dictionary and encyclopedia mentioned in the context? </code>	<code>Legal dictionary and encyclopedia maintained by the<br>Legal Information Institute at Cornell Law School.</code>

• Loss: <code>MatryoshkaLoss</code> with these parameters:

  {
      "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

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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

@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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