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("sentence_transformers_model_id")
# Run inference
sentences = [
'What are the inventory exemptions defined in organizational policies for GAI systems embedded into application software?',
'Information Security GV-1.6-002 Define any inventory exemptions in organizational policies for GAI systems embedded into application software .',
'For example, predictive inferences made by GAI models based on PII or protected attributes c an contribute to adverse decisions , leading to representational or allocative harms to individuals or groups (see Harmful Bias and Homogenization below).',
]
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 | 0.9 |
cosine_accuracy@3 | 0.98 |
cosine_accuracy@5 | 0.99 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.9 |
cosine_precision@3 | 0.3267 |
cosine_precision@5 | 0.198 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.9 |
cosine_recall@3 | 0.98 |
cosine_recall@5 | 0.99 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.9564 |
cosine_mrr@10 | 0.9418 |
cosine_map@100 | 0.9418 |
dot_accuracy@1 | 0.9 |
dot_accuracy@3 | 0.98 |
dot_accuracy@5 | 0.99 |
dot_accuracy@10 | 1.0 |
dot_precision@1 | 0.9 |
dot_precision@3 | 0.3267 |
dot_precision@5 | 0.198 |
dot_precision@10 | 0.1 |
dot_recall@1 | 0.9 |
dot_recall@3 | 0.98 |
dot_recall@5 | 0.99 |
dot_recall@10 | 1.0 |
dot_ndcg@10 | 0.9564 |
dot_mrr@10 | 0.9418 |
dot_map@100 | 0.9418 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 600 training samples
- Columns:
sentence_0
andsentence_1
- Approximate statistics based on the first 600 samples:
sentence_0 sentence_1 type string string details - min: 7 tokens
- mean: 18.93 tokens
- max: 33 tokens
- min: 4 tokens
- mean: 43.35 tokens
- max: 165 tokens
- Samples:
sentence_0 sentence_1 What are indirect prompt injections and how can they exploit vulnerabilities?
Security researchers have already demonstrated how indirect prompt injections can exploit vulnerabilities by steal ing proprietary data or running malicious code remotely on a machine.
What potential consequences can arise from exploiting vulnerabilities through indirect prompt injections?
Security researchers have already demonstrated how indirect prompt injections can exploit vulnerabilities by steal ing proprietary data or running malicious code remotely on a machine.
What factors might organizations consider when tailoring their measurement of GAI risks?
Organizations may choose to tailor how they measure GAI risks based on these characteristics .
- 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
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | cosine_map@100 |
---|---|---|
1.0 | 30 | 0.9216 |
1.6667 | 50 | 0.9292 |
2.0 | 60 | 0.9361 |
3.0 | 90 | 0.9418 |
Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.1.1
- Transformers: 4.45.0
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0
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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Model tree for Galatea007/finetuned_arctic
Base model
Snowflake/snowflake-arctic-embed-mEvaluation results
- Cosine Accuracy@1 on Unknownself-reported0.900
- Cosine Accuracy@3 on Unknownself-reported0.980
- Cosine Accuracy@5 on Unknownself-reported0.990
- Cosine Accuracy@10 on Unknownself-reported1.000
- Cosine Precision@1 on Unknownself-reported0.900
- Cosine Precision@3 on Unknownself-reported0.327
- Cosine Precision@5 on Unknownself-reported0.198
- Cosine Precision@10 on Unknownself-reported0.100
- Cosine Recall@1 on Unknownself-reported0.900
- Cosine Recall@3 on Unknownself-reported0.980