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("sentence_transformers_model_id")
# Run inference
sentences = [
    'paint sealant sonax profiline polymer net shield 75 ml aerosol can 1994 bmw 318is base coupe miscellaneous page 24 note innovative surface protection based on hybrid polymers protects the paintwork by means of a resistant network made from organic and inorganic components can be applied quickly easily intensively freshens up paint color produces silky smooth with an outstanding drip off effect one 75 ml should complete average size car produced by sonax identifiers is 223000m941 category of automotive',
    'paint sealant sonax profiline polymer net shield 75 ml aerosol can 1991 bmw 325i base convertible miscellaneous page 23 note innovative surface protection based on hybrid polymers protects the paintwork by means of a resistant network made from organic and inorganic components can be applied quickly easily intensively freshens up paint color produces silky smooth with an outstanding drip off effect one 75 ml should complete average size car produced by sonax identifiers is 223000m941 category of automotive',
    'honeywell accessories for terminal cod99exmb12 honeywell cod871238012 honeywell dolphin 99ex mobile base vehicle kit charging cradle rs232 universal mounting bracket and 12v cigarette lighter power adapter produced by honeywell metrologic identifiers is 99exmb12 category of computersandaccessories',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 281,342 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 25 tokens
mean: 74.69 tokens
max: 384 tokens
min: 25 tokens
mean: 75.63 tokens
max: 384 tokens

Samples:

anchor	positive
`honeywell hand held products dolphin 99509951 series mobile computer usb cable 6 ft 18m 80000355e usb cable 6 ft 18m identifiers is 80000355e category of computersandaccessories`	`hand held usb cable 6 ft hand ft 80000355e scanner accessories cdwcom hand held products is the leading provider of imagebased data collection solutions for mobile wireless and transaction processing applications to end users throughout world by investing in hhp products its customers are able reduce costs improve service position their companies future growth identifiers is 26121604 category of computersandaccessories`
`intake boot air mass sensor to throttle housing 1995 bmw 318i base convertible intake system page 2 note from 0994 produced by oem identifiers is 13711247829m58 category of automotive`	`intake boot air mass sensor to throttle housing 1995 bmw 318i base convertible intake system page 2 produced by crp identifiers is 13711247829int category of automotive`
`blue sky panorama with transparent clouds vector image sky images over 150 000 vector blue sky panorama with transparent clouds vector background image identifiers is 15266707 category of officeproducts`	`blue sky panorama with transparent clouds vector image images within landscapes nature over 55 000 vector blue sky panorama with transparent clouds vector background image identifiers is 15266707 category of officeproducts`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 70,336 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 25 tokens
mean: 78.58 tokens
max: 384 tokens
min: 22 tokens
mean: 81.44 tokens
max: 384 tokens

Samples:

anchor	positive
`heater hose inlet from cylinder head to water valve 1997 bmw 318i base sedan heater system page 3 produced by genuine bmw identifiers is 64211394295boe category of automotive`	`heater hose inlet from cylinder head to water valve 1996 bmw 318i base convertible heater system page 3 produced by genuine bmw identifiers is 64211394295boe category of automotive`
`harris harris group inc group 1 full quote netdaniacom produced by source nasdaq identifiers is isinus4138331040 category of toolsandhomeimprovement`	`harris harris group inc 1 statistics netdaniacom group produced by source nasdaq identifiers is isinus4138331040 category of toolsandhomeimprovement`
`swiffer dusters with extendable handledusters plastic handle extends to 3 ft 1 per kit handledusters ft kitpag82074 buy online at janeice products identifiers is pag82074 category of toolsandhomeimprovement key specifications are weight per case std pkg quantity package one handle and three dusters description includes item cube 008276 upc code 037000447504 pack 00037000820741 length 092 width 022 height 042 0476`	6 pack value bundle pag82074 dusters plastic handle extends to 3 ft 1 dusters per kitus feather page 5 the janitorial marketus now its easier than ever to get those hardtoreach places pivoting head can be adjusted and locked into place for cleaning angled surfaces such as ceiling fans cabinet corners baseboards refill dusters sold separately one handle three per box bristle material fiber color white plastic greenus produced by pag82074us identifiers is pag82074 category of toolsandhomeimprovement

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
learning_rate: 1e-05
num_train_epochs: 2
warmup_ratio: 0.1
fp16: True
auto_find_batch_size: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
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: 1e-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: 2
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: False
fp16: True
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: True
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss
0.1990	7000	0.0083	0.0029
0.3981	14000	0.0026	0.0019
0.5971	21000	0.0015	0.0014
0.7962	28000	0.0013	0.0011
0.9952	35000	0.0013	0.0010
1.1943	42000	0.0008	0.0010
1.3933	49000	0.0005	0.0009
1.5924	56000	0.0003	0.0009

Framework Versions

Python: 3.10.13
Sentence Transformers: 3.0.1
Transformers: 4.44.0
PyTorch: 2.2.1
Accelerate: 0.33.0
Datasets: 2.21.0
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",
}

CachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, 
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

	anchor	positive
type	string	string
details	min: 25 tokens mean: 74.69 tokens max: 384 tokens	min: 25 tokens mean: 75.63 tokens max: 384 tokens

spl4shedEdu/mpnet_IAB

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

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CachedMultipleNegativesRankingLoss

spl4shedEdu/
mpnet_IAB