qwen3-4B-risk-mnr-law

SentenceTransformer based on Qwen/Qwen3-Embedding-4B

This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-4B. It maps sentences & paragraphs to a 2560-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: Qwen/Qwen3-Embedding-4B <!-- at revision 5cf2132abc99cad020ac570b19d031efec650f2b -->
• Maximum Sequence Length: 40960 tokens
• Output Dimensionality: 2560 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': 40960, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, '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': True, '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("chantomkit/qwen3-4B-risk-mnr-law")
# Run inference
queries = [
    "I represent a mid-sized tech company that anticipates a patent infringement suit in California state court and would like detailed guidance on establishing a defensible document retention and litigation hold policy for both email and cloud-based project management platforms to ensure we meet our spoliation obligations without disrupting daily operations\u2014what best practices and steps should we include?",
]
documents = [
    'As in-house litigation counsel for a pharmaceutical manufacturer, we’ve been served with interrogatories and requests for admission under New York CPLR 3120–3123; we need guidance on crafting precise, proportionate responses that meet our supplementation obligations under Rule 26(e) without waiving objections to relevance or privilege—what approach should we take to draft and update those responses?',
    'I’m a freelance graphic designer in New York whose client has refused to pay the final milestone under our written agreement—can you outline the step-by-step process for drafting and sending a demand letter, reference the relevant New York statutes on breach of contract, explain whether I can file in small claims court, and advise on statutory deadlines and potential remedies?',
    'I’m representing a small orthotic device manufacturer in Texas that recently discovered a competitor’s unauthorized copying of our patented foot-support technology; please draft a complaint for the Southern District of Texas asserting infringement under 35 U.S.C. §§271(a)–(b), including a concise statement of facts, claim chart comparing accused products to patent claims, proposed claim construction standard, preliminary injunction request, prayer for damages, attorneys’ fees, prejudgment interest, and detailed instructions for service on a Delaware corporate defendant.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 2560] [3, 2560]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9112, 0.2615, 0.0045]])

<!-- ### 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

Semantic Similarity

• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

<!-- ## 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: 16,676 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 56 tokens</li><li>mean: 84.52 tokens</li><li>max: 140 tokens</li></ul>	<ul><li>min: 54 tokens</li><li>mean: 87.07 tokens</li><li>max: 140 tokens</li></ul>

• Samples:

  anchor	positive
  <code>I need to file a motion to compel arbitration in a consumer contract dispute and am using ChatGPT to help with drafting; please produce a full motion including statement of facts, legal argument under the Federal Arbitration Act, and a header footnote that reads “This pleading was prepared using a generative AI system and has been reviewed and approved by counsel in accordance with professional responsibility rules.”</code>	<code>As a solo practitioner representing a landlord in a commercial lease termination, I need to send a formal notice letter to the tenant; please draft the termination notice including factual background, statutory grounds for termination, and a professional responsibility disclaimer stating that portions of this letter were generated by an AI tool under my direct supervision.</code>
  <code>I need to file a motion to compel arbitration in a consumer contract dispute and am using ChatGPT to help with drafting; please produce a full motion including statement of facts, legal argument under the Federal Arbitration Act, and a header footnote that reads “This pleading was prepared using a generative AI system and has been reviewed and approved by counsel in accordance with professional responsibility rules.”</code>	<code>I am representing a small business client in a breach of contract dispute over delayed deliveries and need to file a joint stipulation with opposing counsel to accelerate fact discovery; please draft a detailed stipulation letter asking the court for an expedited schedule and include at the top a clear statement that “This document was prepared with the assistance of generative AI under the supervision of counsel, who has reviewed and approved its content.”</code>
  <code>I need to file a motion to compel arbitration in a consumer contract dispute and am using ChatGPT to help with drafting; please produce a full motion including statement of facts, legal argument under the Federal Arbitration Act, and a header footnote that reads “This pleading was prepared using a generative AI system and has been reviewed and approved by counsel in accordance with professional responsibility rules.”</code>	<code>I’m advising a California nonprofit on compliance with the California Consumer Privacy Act and need a detailed internal memo analyzing collection, storage, and deletion obligations for donor data; please prepare the memo with citations to CCPA sections and include a footnote disclosing that the memo was drafted with generative AI assistance and has been reviewed and approved by me.</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• log_level_replica: passive
• log_on_each_node: False
• logging_nan_inf_filter: False
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• 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: 1.0
• num_train_epochs: 3.0
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: passive
• log_on_each_node: False
• logging_nan_inf_filter: False
• 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: True
• 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}
• parallelism_config: 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
• hub_revision: None
• 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
• 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
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

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

Framework Versions

• Python: 3.10.18
• Sentence Transformers: 5.1.1
• Transformers: 4.56.2
• PyTorch: 2.7.1+cu128
• Accelerate: 1.10.1
• Datasets: 4.1.1
• Tokenizers: 0.22.1

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",
}

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