metadata
base_model: Alibaba-NLP/gte-large-en-v1.5
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:500
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
narrow identified goals, to avoid "mission creep." Anticipated data
collection should be determined to be
strictly necessary to the identified goals and should be minimized as much
as possible. Data collected based on
these identified goals and for a specific context should not be used in a
different context without assessing for
new privacy risks and implementing appropriate mitigation measures, which
may include express consent.
sentences:
- >-
What measures should be taken if data collected for specific identified
goals is to be used in a different context?
- >-
What measures should be taken to ensure the privacy of sensitive data
and limit access to it?
- >-
What special requirements are mentioned in the white paper regarding
national security and defense activities in relation to trustworthy
artificial intelligence?
- source_sentence: >-
•
Karen Levy, Assistant Professor, Department of Information Science,
Cornell University
•
Natasha Duarte, Project Director, Upturn
•
Elana Zeide, Assistant Professor, University of Nebraska College of Law
•
Fabian Rogers, Constituent Advocate, Office of NY State Senator Jabari
Brisport and Community
Advocate and Floor Captain, Atlantic Plaza Towers Tenants Association
The individual panelists described the ways in which AI systems and other
technologies are increasingly being
sentences:
- >-
What are some of the challenges posed to democracy by the use of
technology and automated systems, as mentioned in the foreword?
- >-
What principles has the U.S. Intelligence Community developed to guide
personnel in the ethical use of AI?
- >-
What roles do the panelists hold in relation to the discussion on AI
systems and technology?
- source_sentence: |-
impacts disfavoring people based on their race, color, ethnicity,
sex
(including
pregnancy,
childbirth,
and
related
medical
conditions,
gender
identity,
intersex
status,
and
sexual
orientation), religion, age, national origin, disability, veteran status,
sentences:
- >-
What does the term "HUMAN ALTERNATIVES" refer to in the context
provided?
- What types of discrimination are mentioned in the context?
- >-
What are the expectations for automated systems in relation to public
protection from surveillance?
- source_sentence: >-
establish and maintain the capabilities that will allow individuals to use
their own automated systems to help
them make consent, access, and control decisions in a complex data
ecosystem. Capabilities include machine
readable data, standardized data formats, metadata or tags for expressing
data processing permissions and
preferences and data provenance and lineage, context of use and
access-specific tags, and training models for
assessing privacy risk.
sentences:
- >-
What measures should be taken to ensure that independent evaluations of
algorithmic discrimination are conducted while balancing individual
privacy and data access needs?
- >-
What capabilities are necessary for individuals to effectively manage
consent and control decisions in a complex data ecosystem?
- >-
What are some examples of classifications that are protected by law
against discrimination?
- source_sentence: >-
SAFE AND EFFECTIVE
SYSTEMS
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint
for the development of additional
technical standards and practices that are tailored for particular sectors
and contexts.
Derived data sources tracked and reviewed carefully. Data that is derived
from other data through
sentences:
- >-
What is the purpose of the expectations set for automated systems in
relation to technical standards and practices?
- >-
What factors influence the appropriate application of the principles
outlined in the white paper regarding automated systems?
- >-
What actions can a court take if a federal agency fails to comply with
the Privacy Act regarding an individual's records?
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.88
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9866666666666667
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9866666666666667
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.88
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3288888888888888
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1973333333333333
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.88
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9866666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9866666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9499978881111136
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9330158730158731
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9330158730158731
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.88
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9866666666666667
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9866666666666667
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.88
name: Dot Precision@1
- type: dot_precision@3
value: 0.3288888888888888
name: Dot Precision@3
- type: dot_precision@5
value: 0.1973333333333333
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.88
name: Dot Recall@1
- type: dot_recall@3
value: 0.9866666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.9866666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 1
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9499978881111136
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9330158730158731
name: Dot Mrr@10
- type: dot_map@100
value: 0.9330158730158731
name: Dot Map@100
SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
This is a sentence-transformers model finetuned from Alibaba-NLP/gte-large-en-v1.5 on the json dataset. It maps sentences & paragraphs to a 1024-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: Alibaba-NLP/gte-large-en-v1.5
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
- Training Dataset:
- json
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': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
)
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 = [
'SAFE AND EFFECTIVE \nSYSTEMS \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nDerived data sources tracked and reviewed carefully. Data that is derived from other data through',
'What is the purpose of the expectations set for automated systems in relation to technical standards and practices?',
'What factors influence the appropriate application of the principles outlined in the white paper regarding automated systems?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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 | 0.88 |
cosine_accuracy@3 | 0.9867 |
cosine_accuracy@5 | 0.9867 |
cosine_accuracy@10 | 1.0 |
cosine_precision@1 | 0.88 |
cosine_precision@3 | 0.3289 |
cosine_precision@5 | 0.1973 |
cosine_precision@10 | 0.1 |
cosine_recall@1 | 0.88 |
cosine_recall@3 | 0.9867 |
cosine_recall@5 | 0.9867 |
cosine_recall@10 | 1.0 |
cosine_ndcg@10 | 0.95 |
cosine_mrr@10 | 0.933 |
cosine_map@100 | 0.933 |
dot_accuracy@1 | 0.88 |
dot_accuracy@3 | 0.9867 |
dot_accuracy@5 | 0.9867 |
dot_accuracy@10 | 1.0 |
dot_precision@1 | 0.88 |
dot_precision@3 | 0.3289 |
dot_precision@5 | 0.1973 |
dot_precision@10 | 0.1 |
dot_recall@1 | 0.88 |
dot_recall@3 | 0.9867 |
dot_recall@5 | 0.9867 |
dot_recall@10 | 1.0 |
dot_ndcg@10 | 0.95 |
dot_mrr@10 | 0.933 |
dot_map@100 | 0.933 |
Training Details
Training Dataset
json
- Dataset: json
- Size: 500 training samples
- Columns:
anchor
andpositive
- Approximate statistics based on the first 500 samples:
anchor positive type string string details - min: 12 tokens
- mean: 21.76 tokens
- max: 37 tokens
- min: 11 tokens
- mean: 78.92 tokens
- max: 104 tokens
- Samples:
anchor positive What is the primary purpose of the AI Bill of Rights outlined in the October 2022 blueprint?
BLUEPRINT FOR AN
AI BILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022What was the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy?
About this Document
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was
published by the White House Office of Science and Technology Policy in October 2022. This framework was
released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-poweredWhat initiative did the OSTP announce a year prior to the release of the framework for a bill of rights for an AI-powered world?
released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered
world.” Its release follows a year of public engagement to inform this initiative. The framework is available
online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights
About the Office of Science and Technology Policy
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology - Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 16gradient_accumulation_steps
: 16learning_rate
: 2e-05num_train_epochs
: 5lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Trueload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 16eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_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
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Truelocal_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
: Trueignore_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_torch_fusedoptim_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
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | cosine_map@100 |
---|---|---|
1.0 | 1 | 0.9022 |
2.0 | 2 | 0.9311 |
3.0 | 3 | 0.9397 |
4.0 | 4 | 0.9330 |
5.0 | 5 | 0.9330 |
- The bold row denotes the saved checkpoint.
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: 3.0.1
- 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}
}