jet-taekyo/snowflake_finetuned_semantic

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

This is a sentence-transformers model finetuned from 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

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': 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:

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("jet-taekyo/snowflake_finetuned_semantic")
# Run inference
sentences = [
    'What must lenders provide to consumers who are denied credit under the Fair Credit Reporting Act?',
    'that consumers who are denied credit receive "adverse action" notices. Anyone who relies on the information in a \ncredit report to deny a consumer credit must, under the Fair Credit Reporting Act, provide an "adverse action" \nnotice to the consumer, which includes "notice of the reasons a creditor took adverse action on the application \nor on an existing credit account."90 In addition, under the risk-based pricing rule,91 lenders must either inform \nborrowers of their credit score, or else tell consumers when "they are getting worse terms because of \ninformation in their credit report." The CFPB has also asserted that "[t]he law gives every applicant the right to \na specific explanation if their application for credit was denied, and that right is not diminished simply because \na company uses a complex algorithm that it doesn\'t understand."92 Such explanations illustrate a shared value \nthat certain decisions need to be explained.',
    'measures to prevent, flag, or take other action in response to outputs that \nreproduce particular training data (e.g., plagiarized, trademarked, patented, \nlicensed content or trade secret material). \nIntellectual Property; CBRN \nInformation or Capabilities',
]
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]

Evaluation

Metrics

Information Retrieval

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.875
cosine_accuracy@3	0.9671
cosine_accuracy@5	0.9868
cosine_accuracy@10	0.9934
cosine_precision@1	0.875
cosine_precision@3	0.3224
cosine_precision@5	0.1974
cosine_precision@10	0.0993
cosine_recall@1	0.875
cosine_recall@3	0.9671
cosine_recall@5	0.9868
cosine_recall@10	0.9934
cosine_ndcg@10	0.9421
cosine_mrr@10	0.9249
cosine_map@100	0.9255
dot_accuracy@1	0.875
dot_accuracy@3	0.9671
dot_accuracy@5	0.9868
dot_accuracy@10	0.9934
dot_precision@1	0.875
dot_precision@3	0.3224
dot_precision@5	0.1974
dot_precision@10	0.0993
dot_recall@1	0.875
dot_recall@3	0.9671
dot_recall@5	0.9868
dot_recall@10	0.9934
dot_ndcg@10	0.9421
dot_mrr@10	0.9249
dot_map@100	0.9255

Information Retrieval

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8906
cosine_accuracy@3	0.9688
cosine_accuracy@5	0.9688
cosine_accuracy@10	0.9766
cosine_precision@1	0.8906
cosine_precision@3	0.3229
cosine_precision@5	0.1938
cosine_precision@10	0.0977
cosine_recall@1	0.8906
cosine_recall@3	0.9688
cosine_recall@5	0.9688
cosine_recall@10	0.9766
cosine_ndcg@10	0.9391
cosine_mrr@10	0.9266
cosine_map@100	0.9282
dot_accuracy@1	0.8906
dot_accuracy@3	0.9688
dot_accuracy@5	0.9688
dot_accuracy@10	0.9766
dot_precision@1	0.8906
dot_precision@3	0.3229
dot_precision@5	0.1938
dot_precision@10	0.0977
dot_recall@1	0.8906
dot_recall@3	0.9688
dot_recall@5	0.9688
dot_recall@10	0.9766
dot_ndcg@10	0.9391
dot_mrr@10	0.9266
dot_map@100	0.9282

Training Details

Training Dataset

Unnamed Dataset

• Size: 714 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 714 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 7 tokens mean: 17.69 tokens max: 32 tokens	min: 2 tokens mean: 175.22 tokens max: 512 tokens

• Samples:

  sentence_0	sentence_1
  What are the limitations of current pre-deployment testing approaches for GAI applications?	49 early lifecycle TEVV approaches are developed and matured for GAI, organizations may use recommended “pre-deployment testing” practices to measure performance, capabilities, limits, risks, and impacts. This section describes risk measurement and estimation as part of pre-deployment TEVV, and examines the state of play for pre-deployment testing methodologies. Limitations of Current Pre-deployment Test Approaches Currently available pre-deployment TEVV processes used for GAI applications may be inadequate, non- systematically applied, or fail to reflect or mismatched to deployment contexts. For example, the anecdotal testing of GAI system capabilities through video games or standardized tests designed for humans (e.g., intelligence tests, professional licensing exams) does not guarantee GAI system validity or reliability in those domains.
  How do organizations measure performance and risks during pre-deployment testing of GAI systems?	49 early lifecycle TEVV approaches are developed and matured for GAI, organizations may use recommended “pre-deployment testing” practices to measure performance, capabilities, limits, risks, and impacts. This section describes risk measurement and estimation as part of pre-deployment TEVV, and examines the state of play for pre-deployment testing methodologies. Limitations of Current Pre-deployment Test Approaches Currently available pre-deployment TEVV processes used for GAI applications may be inadequate, non- systematically applied, or fail to reflect or mismatched to deployment contexts. For example, the anecdotal testing of GAI system capabilities through video games or standardized tests designed for humans (e.g., intelligence tests, professional licensing exams) does not guarantee GAI system validity or reliability in those domains.
  What are the key aspects of the broad application scope mentioned in the context?	broad application scope, fine-tuning, and varieties of data sources (e.g., grounding, retrieval-augmented generation). Data Privacy; Intellectual Property

• Loss: MatryoshkaLoss 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: 20
• per_device_eval_batch_size: 20
• num_train_epochs: 5
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• 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

Training Logs

Epoch	Step	cosine_map@100
1.0	36	0.9145
1.3889	50	0.9256
2.0	72	0.9246
2.7778	100	0.9282
3.0	108	0.9245
4.0	144	0.9244
4.1667	150	0.9244
5.0	180	0.9255
1.0	31	0.9282

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 3.1.0
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.0
• Tokenizers: 0.19.1

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",
}

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}
}