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
andsentence_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
: stepsper_device_train_batch_size
: 20per_device_eval_batch_size
: 20num_train_epochs
: 5multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 20per_device_eval_batch_size
: 20per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_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}
}
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Base model
Snowflake/snowflake-arctic-embed-mEvaluation results
- Cosine Accuracy@1 on Unknownself-reported0.875
- Cosine Accuracy@3 on Unknownself-reported0.967
- Cosine Accuracy@5 on Unknownself-reported0.987
- Cosine Accuracy@10 on Unknownself-reported0.993
- Cosine Precision@1 on Unknownself-reported0.875
- Cosine Precision@3 on Unknownself-reported0.322
- Cosine Precision@5 on Unknownself-reported0.197
- Cosine Precision@10 on Unknownself-reported0.099
- Cosine Recall@1 on Unknownself-reported0.875
- Cosine Recall@3 on Unknownself-reported0.967