This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-xs. It maps sentences & paragraphs to a 384-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-xs
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 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': 384, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("jimmydzj2006/snowflake-arctic-embed-xs_finetuned_aipolicy")
# Run inference
sentences = [
    'How can human subjects revoke their consent according to the suggested action?',
    'Action ID: MS-2.2-003\nSuggested Action: Provide human subjects with options to withdraw participation or revoke their\nconsent for present or future use of their data in GAI applications.\nGAI Risks: Data Privacy; Human-AI\nConfiguration; Information\nIntegrity',
    'Disinformation and misinformation – both of which may be facilitated by GAI – may erode public trust in true or valid evidence and information, with downstream effects. For example, a synthetic image of a Pentagon blast went viral and briefly caused a drop in the stock market. Generative AI models can also assist malicious actors in creating compelling imagery and propaganda to support disinformation campaigns, which may not be photorealistic, but could enable these campaigns to gain more reach and engagement on social media platforms. Additionally, generative AI models can assist malicious actors in creating fraudulent content intended to impersonate others.\n\n Trustworthy AI Characteristics: Accountable and Transparent, Safe, Valid and Reliable, Interpretable and Explainable\n\n 2.9. Information Security\n\n Information security for computer systems and data is a mature field with widely accepted and standardized practices for offensive and defensive cyber capabilities. GAI-based systems present two primary information security risks: GAI could potentially discover or enable new cybersecurity risks by lowering the barriers for or easing automated exercise of offensive capabilities; simultaneously, it expands the available attack surface, as GAI itself is vulnerable to attacks like prompt injection or data poisoning. \n\n Offensive cyber capabilities advanced by GAI systems may augment cybersecurity attacks such as hacking, malware, and phishing. Reports have indicated that LLMs are already able to discover some vulnerabilities in systems (hardware, software, data) and write code to exploit them. Sophisticated threat actors might further these risks by developing GAI-powered security co-pilots for use in several parts of the attack chain, including informing attackers on how to proactively evade threat detection and escalate privileges after gaining system access.\n\n Information security for GAI models and systems also includes maintaining availability of the GAI system and the integrity and (when applicable) the confidentiality of the GAI code, training data, and model weights. To identify and secure potential attack points in AI systems or specific components of the AI',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 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	0.6484
cosine_accuracy@3	0.7363
cosine_accuracy@5	0.7802
cosine_accuracy@10	0.8571
cosine_precision@1	0.6484
cosine_precision@3	0.2454
cosine_precision@5	0.156
cosine_precision@10	0.0857
cosine_recall@1	0.6484
cosine_recall@3	0.7363
cosine_recall@5	0.7802
cosine_recall@10	0.8571
cosine_ndcg@10	0.7434
cosine_mrr@10	0.7083
cosine_map@100	0.7158
dot_accuracy@1	0.6484
dot_accuracy@3	0.7363
dot_accuracy@5	0.7802
dot_accuracy@10	0.8571
dot_precision@1	0.6484
dot_precision@3	0.2454
dot_precision@5	0.156
dot_precision@10	0.0857
dot_recall@1	0.6484
dot_recall@3	0.7363
dot_recall@5	0.7802
dot_recall@10	0.8571
dot_ndcg@10	0.7434
dot_mrr@10	0.7083
dot_map@100	0.7158

Training Details

Training Dataset

Unnamed Dataset

Size: 2,730 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 8 tokens
mean: 15.71 tokens
max: 33 tokens
min: 19 tokens
mean: 183.25 tokens
max: 467 tokens

Samples:

sentence_0	sentence_1
`What is the Action ID associated with the suggested action?`	`Action ID: MS-2.12-004[object Object]Suggested Action: Verify effectiveness of carbon capture or offset programs for GAI training and[object Object]applications, and address green-washing concerns.[object Object]GAI Risks: Environmental`
`What is the suggested action regarding carbon capture or offset programs?`	`Action ID: MS-2.12-004[object Object]Suggested Action: Verify effectiveness of carbon capture or offset programs for GAI training and[object Object]applications, and address green-washing concerns.[object Object]GAI Risks: Environmental`
`What specific concerns should be addressed in relation to carbon capture programs?`	`Action ID: MS-2.12-004[object Object]Suggested Action: Verify effectiveness of carbon capture or offset programs for GAI training and[object Object]applications, and address green-washing concerns.[object Object]GAI Risks: Environmental`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 284, 5 256, 6 128, 7 64, 8 32 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 10
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: 16
per_device_eval_batch_size: 16
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: 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	Training Loss	cosine_map@100
0.2924	50	-	0.5949
0.5848	100	-	0.6455
0.8772	150	-	0.6680
1.0	171	-	0.6721
1.1696	200	-	0.6811
1.4620	250	-	0.6850
1.7544	300	-	0.6959
2.0	342	-	0.7021
2.0468	350	-	0.7008
2.3392	400	-	0.7043
2.6316	450	-	0.7017
2.9240	500	5.9671	0.7018
3.0	513	-	0.7039
3.2164	550	-	0.7014
3.5088	600	-	0.7039
3.8012	650	-	0.7022
4.0	684	-	0.7058
4.0936	700	-	0.7039
4.3860	750	-	0.7061
4.6784	800	-	0.7030
4.9708	850	-	0.7073
5.0	855	-	0.7073
5.2632	900	-	0.7071
5.5556	950	-	0.7095
5.8480	1000	3.5897	0.7103
6.0	1026	-	0.7080
6.1404	1050	-	0.7075
6.4327	1100	-	0.7089
6.7251	1150	-	0.7087
7.0	1197	-	0.7102
7.0175	1200	-	0.7101
7.3099	1250	-	0.7134
7.6023	1300	-	0.7130
7.8947	1350	-	0.7133
8.0	1368	-	0.7142
8.1871	1400	-	0.7125
8.4795	1450	-	0.7163
8.7719	1500	3.0206	0.7124
9.0	1539	-	0.7144
9.0643	1550	-	0.7158
9.3567	1600	-	0.7159
9.6491	1650	-	0.7158
9.9415	1700	-	0.7158
10.0	1710	-	0.7158

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.2.0
Transformers: 4.44.1
PyTorch: 2.4.0
Accelerate: 0.34.2
Datasets: 3.0.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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

bibtex

@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}
}

	sentence_0	sentence_1
type	string	string
details	min: 8 tokens mean: 15.71 tokens max: 33 tokens	min: 19 tokens mean: 183.25 tokens max: 467 tokens