metadata

base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:17093
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: In the realm of genetics , it is far better to be safe than sorry .
    sentences:
      - >-
        Marijuana use harms the brain, and legalization will increase mental
        health problems.
      - We are god now !
      - >-
        Likewise , the proposal that addictive drugs should be legalized ,
        regulated and opened to " free market dynamics " is immediately belied
        by the recognition that the drug market for an addict is no longer a
        free market – it is clear that they will pay any price when needing
        their drug .
  - source_sentence: >-
      The worldwide anti-nuclear power movement has provided enormous
      stimulation to the Australian movement , and the decline in nuclear power
      expansion since the late 1970s - due substantially to worldwide citizen
      opposition - has been a great setback for Australian uranium mining
      interests .
    sentences:
      - >-
        Just as the state has the authority ( and duty ) to act justly in
        allocating scarce resources , in meeting minimal needs of its (
        deserving ) citizens , in defending its citizens from violence and crime
        , and in not waging unjust wars ; so too does it have the authority ,
        flowing from its mission to promote justice and the good of its people ,
        to punish the criminal .
      - >-
        The long lead times for construction that invalidate nuclear power as a
        way of mitigating climate change was a point recognized in 2009 by the
        body whose mission is to promote the use of nuclear power , the
        International Atomic Energy Agency ( IAEA ) .
      - >-
        Gun control laws would reduce the societal costs associated with gun
        violence.
  - source_sentence: >-
      Requiring uniforms enhances school security by permitting identification
      of non-students who try to enter the campus .
    sentences:
      - >-
        Many students who are against school uniforms argue that they lose their
        â € ‹ self identity when they lose their right to express themselves
        through fashion .
      - >-
        If reproductive cloning is perfected , a quadriplegic can also choose to
        have himself cloned , so someone can take his place .
      - >-
        A higher minimum wage might also decrease turnover and thus keep
        training costs down , supporters say .
  - source_sentence: Minimum wage has long been a minimum standard of living .
    sentences:
      - >-
        A minimum wage job is suppose to be an entry level stepping stone – not
        a career goal .
      - >-
        It is argued that just as it would be permissible to " unplug " and
        thereby cause the death of the person who is using one 's kidneys , so
        it is permissible to abort the fetus ( who similarly , it is said , has
        no right to use one 's body 's life-support functions against one 's
        will ) .
      - Abortion reduces welfare costs to taxpayers .
  - source_sentence: >-
      Fanatics of the pro – life argument are sometimes so focused on the fetus
      that they put no value to the mother ’s life and do not even consider the
      viability of the fetus .
    sentences:
      - Life is life , whether it s outside the womb or not .
      - >-
        Legalization of marijuana is phasing out black markets and taking money
        away from drug cartels, organized crime, and street gangs.
      - 'Response 2 : A child is not replaceable .'
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.7294675022492696
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7234943835496113
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7104391963353577
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7118078150763045
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7212412855224142
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7234943835496113
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7294674862347428
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7234943835496113
            name: Spearman Dot
          - type: pearson_max
            value: 0.7294675022492696
            name: Pearson Max
          - type: spearman_max
            value: 0.7234943835496113
            name: Spearman Max
          - type: pearson_cosine
            value: 0.7146126101962849
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6886131469202397
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7069653659670995
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6837201725651982
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7115078495768724
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6886131469202397
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7146126206763159
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6886131469202397
            name: Spearman Dot
          - type: pearson_max
            value: 0.7146126206763159
            name: Pearson Max
          - type: spearman_max
            value: 0.6886131469202397
            name: Spearman Max

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

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: 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: 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("armaniii/all-mpnet-base-v2-augmentation-indomain-bm25-sts")
# Run inference
sentences = [
    'Fanatics of the pro – life argument are sometimes so focused on the fetus that they put no value to the mother ’s life and do not even consider the viability of the fetus .',
    'Life is life , whether it s outside the womb or not .',
    'Legalization of marijuana is phasing out black markets and taking money away from drug cartels, organized crime, and street gangs.',
]
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

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7295
spearman_cosine	0.7235
pearson_manhattan	0.7104
spearman_manhattan	0.7118
pearson_euclidean	0.7212
spearman_euclidean	0.7235
pearson_dot	0.7295
spearman_dot	0.7235
pearson_max	0.7295
spearman_max	0.7235

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7146
spearman_cosine	0.6886
pearson_manhattan	0.707
spearman_manhattan	0.6837
pearson_euclidean	0.7115
spearman_euclidean	0.6886
pearson_dot	0.7146
spearman_dot	0.6886
pearson_max	0.7146
spearman_max	0.6886

Training Details

Training Dataset

Unnamed Dataset

Size: 17,093 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 7 tokens
mean: 33.23 tokens
max: 97 tokens

min: 4 tokens
mean: 30.75 tokens
max: 96 tokens

min: 0.09
mean: 0.55
max: 0.95

	sentence1	sentence2	score
type	string	string	float
details	min: 7 tokens mean: 33.23 tokens max: 97 tokens	min: 4 tokens mean: 30.75 tokens max: 96 tokens	min: 0.09 mean: 0.55 max: 0.95

Samples:

sentence1	sentence2	score
`It is true that a Colorado study found a post-legalization increase in youths being treated for marijuana exposure .`	`In Colorado , recent figures correlate with the years since marijuana legalization to show a dramatic decrease in overall highway fatalities – and a two-fold increase in the frequency of marijuana-positive drivers in fatal auto crashes .`	`0.4642857142857143`
`The idea of a school uniform is that students wear the uniform at school , but do not wear the uniform , say , at a disco or other events outside school .`	`If it means that the schoolrooms will be more orderly , more disciplined , and that our young people will learn to evaluate themselves by what they are on the inside instead of what they 're wearing on the outside , then our public schools should be able to require their students to wear school uniforms . "`	`0.5714285714285714`
`The resulting embryonic stem cells could then theoretically be grown into adult cells to replace the ailing person 's mutated cells .`	`However , there is a more serious , less cartoonish objection to turning procreation into manufacturing .`	`0.4464285714285714`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

Unnamed Dataset

Size: 340 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 8 tokens
mean: 33.76 tokens
max: 105 tokens

min: 6 tokens
mean: 31.86 tokens
max: 102 tokens

min: 0.09
mean: 0.5
max: 0.89

	sentence1	sentence2	score
type	string	string	float
details	min: 8 tokens mean: 33.76 tokens max: 105 tokens	min: 6 tokens mean: 31.86 tokens max: 102 tokens	min: 0.09 mean: 0.5 max: 0.89

Samples:

sentence1	sentence2	score
`[ quoting himself from Furman v. Georgia , 408 U.S. 238 , 257 ( 1972 ) ] As such it is a penalty that ' subjects the individual to a fate forbidden by the principle of civilized treatment guaranteed by the [ Clause ] . '`	`It provides a deterrent for prisoners already serving a life sentence .`	`0.3214285714285714`
`Of those savings , $ 25.7 billion would accrue to state and local governments , while $ 15.6 billion would accrue to the federal government .`	`Jaime Smith , deputy communications director for the governor ’s office , said , “ The legalization initiative was not driven by a desire for a revenue , but it has provided a small assist for our state budget . ”`	`0.5357142857142857`
`If the uterus is designed to sustain an unborn child ’s life , do n’t unborn children have a right to receive nutrition and shelter through the one organ designed to provide them with that ordinary care ?`	`We as parents are supposed to protect our children at all costs whether they are in the womb or not .`	`0.7678571428571428`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
warmup_ratio: 0.1
bf16: True

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: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: True
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: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-test_spearman_cosine
0.0935	100	0.0151	0.0098	0.7013
0.1871	200	0.0069	0.0112	0.6857
0.2806	300	0.0058	0.0106	0.6860
0.3742	400	0.0059	0.0102	0.6915
0.4677	500	0.0057	0.0097	0.6903
0.5613	600	0.0049	0.0100	0.6797
0.6548	700	0.0055	0.0101	0.6766
0.7484	800	0.0049	0.0116	0.6529
0.8419	900	0.0049	0.0105	0.6572
0.9355	1000	0.0051	0.0115	0.6842
1.0290	1100	0.0038	0.0094	0.7000
1.1225	1200	0.0029	0.0091	0.7027
1.2161	1300	0.0026	0.0093	0.7016
1.3096	1400	0.0027	0.0088	0.7192
1.4032	1500	0.0027	0.0097	0.7065
1.4967	1600	0.0028	0.0091	0.7011
1.5903	1700	0.0027	0.0095	0.7186
1.6838	1800	0.0026	0.0087	0.7277
1.7774	1900	0.0024	0.0085	0.7227
1.8709	2000	0.0025	0.0086	0.7179
1.9645	2100	0.0022	0.0086	0.7195
2.0580	2200	0.0017	0.0088	0.7183
2.1515	2300	0.0014	0.0088	0.7229
2.2451	2400	0.0014	0.0086	0.7200
2.3386	2500	0.0013	0.0088	0.7248
2.4322	2600	0.0014	0.0085	0.7286
2.5257	2700	0.0015	0.0085	0.7283
2.6193	2800	0.0014	0.0085	0.7263
2.7128	2900	0.0014	0.0085	0.7248
2.8064	3000	0.0013	0.0087	0.7191
2.8999	3100	0.0011	0.0086	0.7225
2.9935	3200	0.0012	0.0085	0.7235
3.0	3207	-	-	0.6886

Framework Versions

Python: 3.9.2
Sentence Transformers: 3.0.1
Transformers: 4.43.1
PyTorch: 2.3.1+cu121
Accelerate: 0.34.2
Datasets: 2.14.7
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",
}