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MPNet base trained on Natural Questions pairs

Name: mpnet-base-nq-prompts
Rating: 2.5 (70 reviews)

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the natural-questions 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.

This model was trained using the script from the Training with Prompts Sentence Transformers documentation.

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:
- natural-questions
Language: en
License: apache-2.0

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-nq-prompts")
# Run inference
sentences = [
    'query: where does the last name francisco come from',
    'document: Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
    'document: Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]',
]
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

Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.32	0.52	0.5	0.34	0.58	0.24	0.36	0.42	0.84	0.38	0.22	0.44	0.4694
cosine_accuracy@3	0.44	0.82	0.66	0.54	0.68	0.58	0.48	0.58	0.9	0.54	0.68	0.62	0.8163
cosine_accuracy@5	0.54	0.88	0.76	0.56	0.7	0.62	0.52	0.72	0.94	0.64	0.84	0.72	0.898
cosine_accuracy@10	0.72	0.9	0.84	0.66	0.72	0.84	0.58	0.78	0.96	0.7	0.94	0.72	0.9796
cosine_precision@1	0.32	0.52	0.5	0.34	0.58	0.24	0.36	0.42	0.84	0.38	0.22	0.44	0.4694
cosine_precision@3	0.1667	0.4733	0.2333	0.2333	0.2933	0.1933	0.2733	0.2	0.3667	0.2667	0.2267	0.22	0.5102
cosine_precision@5	0.128	0.44	0.16	0.164	0.188	0.124	0.244	0.152	0.244	0.24	0.168	0.156	0.4898
cosine_precision@10	0.094	0.4	0.09	0.1	0.104	0.084	0.192	0.082	0.13	0.168	0.094	0.082	0.4184
cosine_recall@1	0.1483	0.034	0.49	0.1884	0.29	0.24	0.0124	0.4	0.7407	0.0797	0.22	0.405	0.0363
cosine_recall@3	0.2283	0.1294	0.65	0.3364	0.44	0.58	0.0399	0.56	0.8547	0.1647	0.68	0.59	0.1153
cosine_recall@5	0.275	0.1709	0.74	0.3869	0.47	0.62	0.0629	0.69	0.9127	0.2477	0.84	0.695	0.179
cosine_recall@10	0.3857	0.2726	0.82	0.4773	0.52	0.84	0.075	0.74	0.95	0.3467	0.94	0.71	0.281
cosine_ndcg@10	0.3188	0.473	0.6605	0.3864	0.4999	0.5319	0.232	0.5749	0.889	0.3265	0.5876	0.5767	0.4619
cosine_mrr@10	0.4291	0.6735	0.6122	0.4464	0.6335	0.4349	0.4277	0.5314	0.88	0.4806	0.473	0.543	0.6599
cosine_map@100	0.2524	0.3353	0.6102	0.3311	0.4448	0.4388	0.0794	0.5242	0.8652	0.2539	0.4756	0.5335	0.3646

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.433
cosine_accuracy@3	0.6413
cosine_accuracy@5	0.7183
cosine_accuracy@10	0.7954
cosine_precision@1	0.433
cosine_precision@3	0.2813
cosine_precision@5	0.2229
cosine_precision@10	0.1568
cosine_recall@1	0.2527
cosine_recall@3	0.413
cosine_recall@5	0.4838
cosine_recall@10	0.566
cosine_ndcg@10	0.5015
cosine_mrr@10	0.5558
cosine_map@100	0.4238

Training Details

Training Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 100,231 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 12 tokens
mean: 13.74 tokens
max: 26 tokens
min: 17 tokens
mean: 139.2 tokens
max: 510 tokens

Samples:

query	answer
`query: who is required to report according to the hmda`	`document: Home Mortgage Disclosure Act US financial institutions must report HMDA data to their regulator if they meet certain criteria, such as having assets above a specific threshold. The criteria is different for depository and non-depository institutions and are available on the FFIEC website.[4] In 2012, there were 7,400 institutions that reported a total of 18.7 million HMDA records.[5]`
`query: what is the definition of endoplasmic reticulum in biology`	document: Endoplasmic reticulum The endoplasmic reticulum (ER) is a type of organelle in eukaryotic cells that forms an interconnected network of flattened, membrane-enclosed sacs or tube-like structures known as cisternae. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum occurs in most types of eukaryotic cells, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum: rough and smooth. The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes. The smooth endoplasmic reticulum lacks ribosomes and functions in lipid manufacture and metabolism, the production of steroid hormones, and detoxification.[1] The smooth ER is especially abundant in mammalian liver and gonad cells. The lacy membranes of the endoplasmic reticulum were first seen in 1945 u...
`query: what does the ski mean in polish names`	`document: Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today.`

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

Evaluation Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 100,231 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 12 tokens
mean: 13.78 tokens
max: 24 tokens
min: 13 tokens
mean: 137.63 tokens
max: 512 tokens

Samples:

query	answer
`query: difference between russian blue and british blue cat`	document: Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.
`query: who played the little girl on mrs doubtfire`	`document: Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.`
`query: what year did the movie the sound of music come out`	document: The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.

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
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
seed: 12
bf16: True
prompts: {'query': 'query: ', 'answer': 'document: '}
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: 256
per_device_eval_batch_size: 256
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: 2e-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: 12
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
use_liger_kernel: False
eval_use_gather_object: False
prompts: {'query': 'query: ', 'answer': 'document: '}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0	0	-	-	0.0442	0.0851	0.0326	0.0282	0.0625	0.0708	0.0262	0.0331	0.6747	0.0387	0.2764	0.0617	0.0721	0.1159
0.0026	1	5.0875	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1289	50	2.0474	0.2481	0.2817	0.4560	0.6297	0.3893	0.4392	0.4501	0.1952	0.4191	0.8709	0.3251	0.5181	0.5186	0.4715	0.4588
0.2577	100	0.2027	0.1365	0.2906	0.4798	0.6203	0.3737	0.4823	0.4927	0.2102	0.5126	0.9027	0.3347	0.5623	0.5201	0.4721	0.4811
0.3866	150	0.14	0.1168	0.3237	0.4950	0.6585	0.4020	0.4912	0.5350	0.2362	0.5483	0.8920	0.3322	0.5817	0.5364	0.4739	0.5005
0.5155	200	0.1253	0.1057	0.3334	0.4953	0.6676	0.3794	0.5071	0.5386	0.2416	0.5541	0.8771	0.3281	0.5820	0.5600	0.4737	0.5029
0.6443	250	0.1305	0.1016	0.3252	0.4768	0.6554	0.3825	0.5010	0.5261	0.2395	0.5590	0.8878	0.3277	0.5922	0.5730	0.4624	0.5006
0.7732	300	0.1183	0.0965	0.3111	0.4797	0.6638	0.3649	0.5166	0.5304	0.2236	0.5619	0.8889	0.3242	0.5809	0.5681	0.4615	0.4981
0.9021	350	0.1102	0.0939	0.3223	0.4723	0.6682	0.3768	0.4964	0.5312	0.2307	0.5738	0.8890	0.3245	0.5873	0.5783	0.4622	0.5010
1.0	388	-	-	0.3188	0.4730	0.6605	0.3864	0.4999	0.5319	0.2320	0.5749	0.8890	0.3265	0.5876	0.5767	0.4619	0.5015

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.386 kWh
Carbon Emitted: 0.150 kg of CO2
Hours Used: 0.992 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: 3.3.0.dev0
Transformers: 4.45.2
PyTorch: 2.5.0+cu121
Accelerate: 1.0.0
Datasets: 2.20.0
Tokenizers: 0.20.1-dev.0

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}
}

	query	answer
type	string	string
details	min: 12 tokens mean: 13.74 tokens max: 26 tokens	min: 17 tokens mean: 139.2 tokens max: 510 tokens