This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2
Maximum Sequence Length: 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("jet-taekyo/mpnet_finetuned_recursive")
# Run inference
sentences = [
    'What impact do automated systems have on underserved communities?',
    "automated systems make on underserved communities and to institute proactive protections that support these \ncommunities. \n•\nAn automated system using nontraditional factors such as educational attainment and employment history as\npart of its loan underwriting and pricing model was found to be much more likely to charge an applicant who\nattended a Historically Black College or University (HBCU) higher loan prices for refinancing a student loan\nthan an applicant who did not attend an HBCU. This was found to be true even when controlling for\nother credit-related factors.32\n•\nA hiring tool that learned the features of a company's employees (predominantly men) rejected women appli\xad\ncants for spurious and discriminatory reasons; resumes with the word “women’s,” such as “women’s\nchess club captain,” were penalized in the candidate ranking.33\n•\nA predictive model marketed as being able to predict whether students are likely to drop out of school was",
    'on a principle of local control, such that those individuals closest to the data subject have more access while \nthose who are less proximate do not (e.g., a teacher has access to their students’ daily progress data while a \nsuperintendent does not). \nReporting. In addition to the reporting on data privacy (as listed above for non-sensitive data), entities devel-\noping technologies related to a sensitive domain and those collecting, using, storing, or sharing sensitive data \nshould, whenever appropriate, regularly provide public reports describing: any data security lapses or breaches \nthat resulted in sensitive data leaks; the number, type, and outcomes of ethical pre-reviews undertaken; a \ndescription of any data sold, shared, or made public, and how that data was assessed to determine it did not pres-\nent a sensitive data risk; and ongoing risk identification and management procedures, and any mitigation added',
]
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	0.8882
cosine_accuracy@3	0.9934
cosine_accuracy@5	0.9934
cosine_accuracy@10	1.0
cosine_precision@1	0.8882
cosine_precision@3	0.3311
cosine_precision@5	0.1987
cosine_precision@10	0.1
cosine_recall@1	0.8882
cosine_recall@3	0.9934
cosine_recall@5	0.9934
cosine_recall@10	1.0
cosine_ndcg@10	0.955
cosine_mrr@10	0.9395
cosine_map@100	0.9395
dot_accuracy@1	0.8882
dot_accuracy@3	0.9934
dot_accuracy@5	0.9934
dot_accuracy@10	1.0
dot_precision@1	0.8882
dot_precision@3	0.3311
dot_precision@5	0.1987
dot_precision@10	0.1
dot_recall@1	0.8882
dot_recall@3	0.9934
dot_recall@5	0.9934
dot_recall@10	1.0
dot_ndcg@10	0.955
dot_mrr@10	0.9395
dot_map@100	0.9395

Training Details

Training Dataset

Unnamed Dataset

Size: 714 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 714 samples:
sentence_0 sentence_1
type string string
details
min: 10 tokens
mean: 18.43 tokens
max: 32 tokens
min: 22 tokens
mean: 175.4 tokens
max: 384 tokens

Samples:

sentence_0	sentence_1
`What information should designers and developers provide about automated systems to ensure transparency?`	You should know that an automated system is being used, [object Object]and understand how and why it contributes to outcomes [object Object]that impact you. Designers, developers, and deployers of automat[object Object]ed systems should provide generally accessible plain language docu[object Object]mentation including clear descriptions of the overall system func[object Object]tioning and the role automation plays, notice that such systems are in [object Object]use, the individual or organization responsible for the system, and ex[object Object]planations of outcomes that are clear, timely, and accessible. Such [object Object]notice should be kept up-to-date and people impacted by the system [object Object]should be notified of significant use case or key functionality chang[object Object]es. You should know how and why an outcome impacting you was de[object Object]termined by an automated system, including when the automated [object Object]system is not the sole input determining the outcome. Automated [object Object]systems should provide explanations that are technically valid, [object Object]meaningful and useful to you and to any operators or others who
`Why is it important for individuals impacted by automated systems to be notified of significant changes in functionality?`	You should know that an automated system is being used, [object Object]and understand how and why it contributes to outcomes [object Object]that impact you. Designers, developers, and deployers of automat[object Object]ed systems should provide generally accessible plain language docu[object Object]mentation including clear descriptions of the overall system func[object Object]tioning and the role automation plays, notice that such systems are in [object Object]use, the individual or organization responsible for the system, and ex[object Object]planations of outcomes that are clear, timely, and accessible. Such [object Object]notice should be kept up-to-date and people impacted by the system [object Object]should be notified of significant use case or key functionality chang[object Object]es. You should know how and why an outcome impacting you was de[object Object]termined by an automated system, including when the automated [object Object]system is not the sole input determining the outcome. Automated [object Object]systems should provide explanations that are technically valid, [object Object]meaningful and useful to you and to any operators or others who
`What specific technical questions does the questionnaire for evaluating software workers cover?`	questionnaire that businesses can use proactively when procuring software to evaluate workers. It covers [object Object]specific technical questions such as the training data used, model training process, biases identified, and [object Object]mitigation steps employed.55 [object Object]Standards organizations have developed guidelines to incorporate accessibility criteria [object Object]into technology design processes. The most prevalent in the United States is the Access Board’s Section [object Object]508 regulations,56 which are the technical standards for federal information communication technology (software, [object Object]hardware, and web). Other standards include those issued by the International Organization for [object Object]Standardization,57 and the World Wide Web Consortium Web Content Accessibility Guidelines,58 a globally [object Object]recognized voluntary consensus standard for web content and other information and communications [object Object]technology. [object Object]NIST has released Special Publication 1270, Towards a Standard for Identifying and Managing Bias

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	36	0.9395

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.1.0
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: 10 tokens mean: 18.43 tokens max: 32 tokens	min: 22 tokens mean: 175.4 tokens max: 384 tokens