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SentenceTransformer based on microsoft/mpnet-base

Name: mpnet-base-nli-matryoshka-reproduced
Availability: InStock

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the all-nli dataset. 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: microsoft/mpnet-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- all-nli
Language: en

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})
)

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("tomaarsen/mpnet-base-nli-matryoshka-reproduced")
# Run inference
sentences = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
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

Datasets: sts-dev-768 and sts-test-768
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 768 3}

Metric	sts-dev-768	sts-test-768
pearson_cosine	0.8428	0.8189
spearman_cosine	0.8509	0.8359

Semantic Similarity

Datasets: sts-dev-512 and sts-test-512
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 512 3}

Metric	sts-dev-512	sts-test-512
pearson_cosine	0.8403	0.8186
spearman_cosine	0.8493	0.8362

Semantic Similarity

Datasets: sts-dev-256 and sts-test-256
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 256 3}

Metric	sts-dev-256	sts-test-256
pearson_cosine	0.8347	0.813
spearman_cosine	0.8463	0.8332

Semantic Similarity

Datasets: sts-dev-128 and sts-test-128
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 128 3}

Metric	sts-dev-128	sts-test-128
pearson_cosine	0.8258	0.803
spearman_cosine	0.8396	0.8262

Semantic Similarity

Datasets: sts-dev-64 and sts-test-64
Evaluated with EmbeddingSimilarityEvaluator with these parameters:
json
1{ 2 "truncate_dim": 64 3}

Metric	sts-dev-64	sts-test-64
pearson_cosine	0.8134	0.7904
spearman_cosine	0.8314	0.8194

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 557,850 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 7 tokens
mean: 10.46 tokens
max: 46 tokens
min: 6 tokens
mean: 12.81 tokens
max: 40 tokens
min: 5 tokens
mean: 13.4 tokens
max: 50 tokens

Samples:

anchor	positive	negative
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`A person is at a diner, ordering an omelette.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`The boy skates down the sidewalk.`

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}

Evaluation Dataset

all-nli

Dataset: all-nli at d482672
Size: 6,584 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 6 tokens
mean: 17.95 tokens
max: 63 tokens
min: 4 tokens
mean: 9.78 tokens
max: 29 tokens
min: 5 tokens
mean: 10.35 tokens
max: 29 tokens

Samples:

anchor	positive	negative
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`The men are fighting outside a deli.`
`Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.`	`Two kids in numbered jerseys wash their hands.`	`Two kids in jackets walk to school.`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`A man selling donuts to a customer.`	`A woman drinks her coffee in a small cafe.`

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: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: 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: 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: 1
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}
tp_size: 0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts-dev-768_spearman_cosine	sts-dev-512_spearman_cosine	sts-dev-256_spearman_cosine	sts-dev-128_spearman_cosine	sts-dev-64_spearman_cosine	sts-test-768_spearman_cosine	sts-test-512_spearman_cosine	sts-test-256_spearman_cosine	sts-test-128_spearman_cosine	sts-test-64_spearman_cosine
0.0459	1600	4.3243	1.5267	0.8525	0.8475	0.8438	0.8356	0.8155	-	-	-	-	-
0.0918	3200	2.4538	1.4448	0.8479	0.8439	0.8403	0.8346	0.8249	-	-	-	-	-
0.1377	4800	2.2829	1.5117	0.8507	0.8481	0.8429	0.8348	0.8203	-	-	-	-	-
0.1836	6400	2.0446	1.2684	0.8574	0.8541	0.8498	0.8413	0.8302	-	-	-	-	-
0.2294	8000	1.8867	1.3107	0.8452	0.8423	0.8400	0.8352	0.8255	-	-	-	-	-
0.2753	9600	1.747	1.1663	0.8456	0.8420	0.8384	0.8292	0.8229	-	-	-	-	-
0.3212	11200	1.6297	1.0809	0.8420	0.8388	0.8360	0.8294	0.8205	-	-	-	-	-
0.3671	12800	1.5974	1.0853	0.8374	0.8352	0.8310	0.8264	0.8184	-	-	-	-	-
0.4130	14400	1.5227	1.0440	0.8479	0.8457	0.8434	0.8380	0.8266	-	-	-	-	-
0.4589	16000	1.3835	1.0718	0.8365	0.8341	0.8310	0.8258	0.8172	-	-	-	-	-
0.5048	17600	1.3893	1.0140	0.8384	0.8363	0.8339	0.8275	0.8178	-	-	-	-	-
0.5507	19200	1.3203	1.0048	0.8418	0.8400	0.8364	0.8292	0.8204	-	-	-	-	-
0.5966	20800	1.2396	0.9407	0.8458	0.8439	0.8404	0.8353	0.8274	-	-	-	-	-
0.6425	22400	1.1842	0.9541	0.8435	0.8404	0.8384	0.8335	0.8257	-	-	-	-	-
0.6883	24000	1.1217	0.9000	0.8534	0.8512	0.8478	0.8408	0.8297	-	-	-	-	-
0.7342	25600	1.093	0.8731	0.8525	0.8503	0.8467	0.8406	0.8313	-	-	-	-	-
0.7801	27200	1.0609	0.8238	0.8528	0.8510	0.8469	0.8399	0.8312	-	-	-	-	-
0.8260	28800	0.9807	0.8264	0.8497	0.8478	0.8448	0.8384	0.8295	-	-	-	-	-
0.8719	30400	1.0061	0.8135	0.8455	0.8439	0.8405	0.8338	0.8256	-	-	-	-	-
0.9178	32000	0.9724	0.7965	0.8517	0.8499	0.8465	0.8401	0.8319	-	-	-	-	-
0.9637	33600	0.9057	0.7841	0.8509	0.8493	0.8463	0.8396	0.8314	-	-	-	-	-
-1	-1	-	-	-	-	-	-	-	0.8359	0.8362	0.8332	0.8262	0.8194

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.529 kWh
Carbon Emitted: 0.206 kg of CO2
Hours Used: 2.452 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 4.1.0.dev0
Transformers: 4.51.1
PyTorch: 2.6.0+cu124
Accelerate: 1.5.1
Datasets: 3.3.2
Tokenizers: 0.21.1

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}
}

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 10.46 tokens max: 46 tokens	min: 6 tokens mean: 12.81 tokens max: 40 tokens	min: 5 tokens mean: 13.4 tokens max: 50 tokens