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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("northstaranlyticsma24/artic_ft_midterm")
# Run inference
sentences = [
    'What topics were discussed during the meetings related to the development of the Blueprint for an AI Bill of Rights?',
    'SECTION: LISTENING TO THE AMERICAN PEOPLE\nAPPENDIX\n• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these\nmeetings were specifically focused on providing ideas related to the development of the Blueprint for an AI\nBill of Rights while others provided useful general context on the positive use cases, potential harms, and/or\noversight possibilities for these technologies.',
    ' \nGAI systems can produce content that is inciting, radicalizing, or threatening, or that gloriﬁes violence, \nwith greater ease and scale than other technologies. LLMs have been reported to generate dangerous or \nviolent recommendations, and some models have generated actionable instructions for dangerous or \n \n \n9 Confabulations of falsehoods are most commonly a problem for text-based outputs; for audio, image, or video \ncontent, creative generation of non-factual content can be a desired behavior. 10 For example, legal confabulations have been shown to be pervasive in current state-of-the-art LLMs. See also, \ne.g.,  \n \n7 \nunethical behavior.',
]
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	0.7609
cosine_accuracy@3	0.8696
cosine_accuracy@5	0.913
cosine_accuracy@10	0.9783
cosine_precision@1	0.7609
cosine_precision@3	0.2899
cosine_precision@5	0.1826
cosine_precision@10	0.0978
cosine_recall@1	0.7609
cosine_recall@3	0.8696
cosine_recall@5	0.913
cosine_recall@10	0.9783
cosine_ndcg@10	0.8567
cosine_mrr@10	0.819
cosine_map@100	0.8204
dot_accuracy@1	0.7609
dot_accuracy@3	0.8696
dot_accuracy@5	0.913
dot_accuracy@10	0.9783
dot_precision@1	0.7609
dot_precision@3	0.2899
dot_precision@5	0.1826
dot_precision@10	0.0978
dot_recall@1	0.7609
dot_recall@3	0.8696
dot_recall@5	0.913
dot_recall@10	0.9783
dot_ndcg@10	0.8567
dot_mrr@10	0.819
dot_map@100	0.8204

Training Details

Training Dataset

Unnamed Dataset

Size: 363 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 363 samples:
sentence_0 sentence_1
type string string
details
min: 2 tokens
mean: 20.1 tokens
max: 36 tokens
min: 2 tokens
mean: 228.97 tokens
max: 512 tokens

Samples:

sentence_0	sentence_1
`What are the five principles outlined in the Blueprint for an AI Bill of Rights intended to protect against?`	SECTION: USING THIS TECHNICAL COMPANION[object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object]- [object Object]USING THIS TECHNICAL COMPANION[object Object]The Blueprint for an AI Bill of Rights is a set of five principles and associated practices to help guide the design, [object Object]use, and deployment of automated systems to protect the rights of the American public in the age of artificial [object Object]intelligence. This technical companion considers each principle in the Blueprint for an AI Bill of Rights and [object Object]provides examples and concrete steps for communities, industry, governments, and others to take in order to [object Object]build these protections into policy, practice, or the technological design process. Taken together, the technical protections and practices laid out in the Blueprint for an AI Bill of Rights can help [object Object]guard the American public against many of the potential and actual harms identified by researchers, technolo[object Object]gists, advocates, journalists, policymakers, and communities in the United States and around the world. This [object Object]technical companion is intended to be used as a reference by people across many circumstances – anyone [object Object]impacted by automated systems, and anyone developing, designing, deploying, evaluating, or making policy to [object Object]govern the use of an automated system. Each principle is accompanied by three supplemental sections: [object Object]1[object Object]2[object Object]WHY THIS PRINCIPLE IS IMPORTANT: [object Object]This section provides a brief summary of the problems that the principle seeks to address and protect against, including [object Object]illustrative examples. WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS: [object Object]• The expectations for automated systems are meant to serve as a blueprint for the development of additional technical[object Object]standards and practices that should be tailored for particular sectors and contexts. • This section outlines practical steps that can be implemented to realize the vision of the Blueprint for an AI Bill of Rights. The [object Object]expectations laid out often mirror existing practices for technology development, including pre-deployment testing, ongoing [object Object]monitoring, and governance structures for automated systems, but also go further to address unmet needs for change and offer [object Object]concrete directions for how those changes can be made. • Expectations about reporting are intended for the entity developing or using the automated system. The resulting reports can [object Object]be provided to the public, regulators, auditors, industry standards groups, or others engaged in independent review, and should [object Object]be made public as much as possible consistent with law, regulation, and policy, and noting that intellectual property, law [object Object]enforcement, or national security considerations may prevent public release. Where public reports are not possible, the [object Object]information should be provided to oversight bodies and privacy, civil liberties, or other ethics officers charged with safeguard [object Object]ing individuals’ rights. These reporting expectations are important for transparency, so the American people can have[object Object]confidence that their rights, opportunities, and access as well as their expectations about technologies are respected. 3[object Object]HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE: [object Object]This section provides real-life examples of how these guiding principles can become reality, through laws, policies, and practices. It describes practical technical and sociotechnical approaches to protecting rights, opportunities, and access. The examples provided are not critiques or endorsements, but rather are offered as illustrative cases to help [object Object]provide a concrete vision for actualizing the Blueprint for an AI Bill of Rights. Effectively implementing these [object Object]processes require the cooperation of and collaboration among industry, civil society, researchers, policymakers, [object Object]technologists, and the public.
`How does the technical companion suggest that automated systems should be monitored and reported on to ensure transparency and protect individual rights?`	SECTION: USING THIS TECHNICAL COMPANION[object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object] [object Object]- [object Object]USING THIS TECHNICAL COMPANION[object Object]The Blueprint for an AI Bill of Rights is a set of five principles and associated practices to help guide the design, [object Object]use, and deployment of automated systems to protect the rights of the American public in the age of artificial [object Object]intelligence. This technical companion considers each principle in the Blueprint for an AI Bill of Rights and [object Object]provides examples and concrete steps for communities, industry, governments, and others to take in order to [object Object]build these protections into policy, practice, or the technological design process. Taken together, the technical protections and practices laid out in the Blueprint for an AI Bill of Rights can help [object Object]guard the American public against many of the potential and actual harms identified by researchers, technolo[object Object]gists, advocates, journalists, policymakers, and communities in the United States and around the world. This [object Object]technical companion is intended to be used as a reference by people across many circumstances – anyone [object Object]impacted by automated systems, and anyone developing, designing, deploying, evaluating, or making policy to [object Object]govern the use of an automated system. Each principle is accompanied by three supplemental sections: [object Object]1[object Object]2[object Object]WHY THIS PRINCIPLE IS IMPORTANT: [object Object]This section provides a brief summary of the problems that the principle seeks to address and protect against, including [object Object]illustrative examples. WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS: [object Object]• The expectations for automated systems are meant to serve as a blueprint for the development of additional technical[object Object]standards and practices that should be tailored for particular sectors and contexts. • This section outlines practical steps that can be implemented to realize the vision of the Blueprint for an AI Bill of Rights. The [object Object]expectations laid out often mirror existing practices for technology development, including pre-deployment testing, ongoing [object Object]monitoring, and governance structures for automated systems, but also go further to address unmet needs for change and offer [object Object]concrete directions for how those changes can be made. • Expectations about reporting are intended for the entity developing or using the automated system. The resulting reports can [object Object]be provided to the public, regulators, auditors, industry standards groups, or others engaged in independent review, and should [object Object]be made public as much as possible consistent with law, regulation, and policy, and noting that intellectual property, law [object Object]enforcement, or national security considerations may prevent public release. Where public reports are not possible, the [object Object]information should be provided to oversight bodies and privacy, civil liberties, or other ethics officers charged with safeguard [object Object]ing individuals’ rights. These reporting expectations are important for transparency, so the American people can have[object Object]confidence that their rights, opportunities, and access as well as their expectations about technologies are respected. 3[object Object]HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE: [object Object]This section provides real-life examples of how these guiding principles can become reality, through laws, policies, and practices. It describes practical technical and sociotechnical approaches to protecting rights, opportunities, and access. The examples provided are not critiques or endorsements, but rather are offered as illustrative cases to help [object Object]provide a concrete vision for actualizing the Blueprint for an AI Bill of Rights. Effectively implementing these [object Object]processes require the cooperation of and collaboration among industry, civil society, researchers, policymakers, [object Object]technologists, and the public.
`What is the significance of the number 14 in the given context?`	`14[object Object]`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 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: 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	19	0.7434
2.0	38	0.7973
2.6316	50	0.8048
3.0	57	0.8048
4.0	76	0.8204
5.0	95	0.8204

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
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

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: 2 tokens mean: 20.1 tokens max: 36 tokens	min: 2 tokens mean: 228.97 tokens max: 512 tokens