metadata

base_model: Snowflake/snowflake-arctic-embed-m
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:600
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Which entities are responsible for enforcing the requirements discussed in
      the context?
    sentences:
      - >-
        ALGORITHMIC DISCRIMINATION PROTECTIONS

        You should not face discrimination by algorithms and systems should be
        used and designed in
      - >-
        SECTION TITLE

        HUMAN ALTERNATIVES, CONSIDERATION, AND FALLBACK

        You should be able to opt out, where appropriate, and have access to a
        person who can quickly
      - >-
        requirements of the Federal agencies that enforce them. These principles
        are not intended to, and do not,
  - source_sentence: >-
      How is safety addressed in the development process according to the
      context?
    sentences:
      - |-
        TABLE OF CONTENTS
        FROM PRINCIPLES TO PRACTICE: A TECHNICAL COMPANION TO THE BLUEPRINT 
        FOR AN AI BILL OF RIGHTS 
         
        USING THIS TECHNICAL COMPANION
         
        SAFE AND EFFECTIVE SYSTEMS
      - |-
        stemming from unintended, yet foreseeable, uses or 
         
         
         
         
          
         
         
        SECTION TITLE
        BLUEPRINT FOR AN
        SAFE AND E 
        You should be protected from unsafe or 
        developed with consultation from diverse
      - >-
        tion or implemented under existing U.S. laws. For example, government
        surveillance, and data search and
  - source_sentence: >-
      How should the deployment of automated systems be aligned with the
      principles for protecting the American public?
    sentences:
      - >-
        public and private sector contexts; 

        Equal opportunities, including equitable access to education, housing,
        credit, employment, and other 

        programs; or,
      - >-
        use, and deployment of automated systems to protect the rights of the
        American public in the age of artificial
      - >-
        five principles that should guide the design, use, and deployment of
        automated systems to protect the American
  - source_sentence: >-
      Who should designers, developers, and deployers of automated systems seek
      permission from?
    sentences:
      - >-
        This important progress must not come at the price of civil rights or
        democratic values, foundational American
      - >-
        a blueprint for building and deploying automated systems that are
        aligned with democratic values and protect
      - >-
        context is collected. Designers, developers, and deployers of automated
        systems should seek your permission
  - source_sentence: >-
      What changes are suggested for notice-and-choice practices regarding broad
      uses of data?
    sentences:
      - >-
        mated systems, and researchers developing innovative guardrails.
        Advocates, researchers, and government
      - >-
        tial to meaningfully impact rights, opportunities, or access.
        Additionally, this framework does not analyze or
      - >-
        understand notice-and-choice practices for broad uses of data should be
        changed. Enhanced protections and
model-index:
  - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.9
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.99
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.99
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33000000000000007
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19799999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.99
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.99
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9601170111547646
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9464285714285714
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9464285714285714
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.9
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.99
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.99
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.9
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.33000000000000007
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.19799999999999998
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09999999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.9
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.99
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.99
            name: Dot Recall@5
          - type: dot_recall@10
            value: 1
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9601170111547646
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.9464285714285714
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.9464285714285714
            name: Dot Map@100

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("dstampfli/finetuned-snowflake-arctic-embed-m")
# Run inference
sentences = [
    'What changes are suggested for notice-and-choice practices regarding broad uses of data?',
    'understand notice-and-choice practices for broad uses of data should be changed. Enhanced protections and',
    'tial to meaningfully impact rights, opportunities, or access. Additionally, this framework does not analyze or',
]
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	0.99
cosine_accuracy@5	0.99
cosine_accuracy@10	1.0
cosine_precision@1	0.9
cosine_precision@3	0.33
cosine_precision@5	0.198
cosine_precision@10	0.1
cosine_recall@1	0.9
cosine_recall@3	0.99
cosine_recall@5	0.99
cosine_recall@10	1.0
cosine_ndcg@10	0.9601
cosine_mrr@10	0.9464
cosine_map@100	0.9464
dot_accuracy@1	0.9
dot_accuracy@3	0.99
dot_accuracy@5	0.99
dot_accuracy@10	1.0
dot_precision@1	0.9
dot_precision@3	0.33
dot_precision@5	0.198
dot_precision@10	0.1
dot_recall@1	0.9
dot_recall@3	0.99
dot_recall@5	0.99
dot_recall@10	1.0
dot_ndcg@10	0.9601
dot_mrr@10	0.9464
dot_map@100	0.9464

Training Details

Training Dataset

Unnamed Dataset

Size: 600 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 600 samples:
sentence_0 sentence_1
type string string
details
min: 9 tokens
mean: 17.15 tokens
max: 31 tokens

min: 7 tokens
mean: 23.93 tokens
max: 46 tokens

	sentence_0	sentence_1
type	string	string
details	min: 9 tokens mean: 17.15 tokens max: 31 tokens	min: 7 tokens mean: 23.93 tokens max: 46 tokens

Samples:

sentence_0	sentence_1
`What is the purpose of the AI Bill of Rights mentioned in the context?`	`BLUEPRINT FOR AN AI BILL OF RIGHTS MAKING AUTOMATED SYSTEMS WORK FOR THE AMERICAN PEOPLE OCTOBER 2022`
`When was the Blueprint for an AI Bill of Rights published?`	`BLUEPRINT FOR AN AI BILL OF RIGHTS MAKING AUTOMATED SYSTEMS WORK FOR THE AMERICAN PEOPLE OCTOBER 2022`
`What is the main purpose of the Blueprint for an AI Bill of Rights?`	`About this Document The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was`

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	30	0.9458
1.6667	50	0.9461
2.0	60	0.9461
3.0	90	0.9463
3.3333	100	0.9463
4.0	120	0.9464
5.0	150	0.9464

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1
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}
}