MPNet base trained on Natural Questions pairs
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.
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:
- 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.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'where does the last name francisco come from',
'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
'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
andNanoTouche2020
- Evaluated with
InformationRetrievalEvaluator
Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cosine_accuracy@1 | 0.16 | 0.52 | 0.28 | 0.14 | 0.32 | 0.14 | 0.22 | 0.14 | 0.78 | 0.28 | 0.18 | 0.3 | 0.4898 |
cosine_accuracy@3 | 0.34 | 0.62 | 0.5 | 0.32 | 0.44 | 0.28 | 0.3 | 0.24 | 0.88 | 0.42 | 0.52 | 0.34 | 0.7347 |
cosine_accuracy@5 | 0.56 | 0.7 | 0.52 | 0.36 | 0.48 | 0.34 | 0.34 | 0.32 | 0.9 | 0.52 | 0.64 | 0.42 | 0.8163 |
cosine_accuracy@10 | 0.64 | 0.78 | 0.62 | 0.46 | 0.58 | 0.44 | 0.36 | 0.48 | 0.94 | 0.62 | 0.88 | 0.5 | 0.9388 |
cosine_precision@1 | 0.16 | 0.52 | 0.28 | 0.14 | 0.32 | 0.14 | 0.22 | 0.14 | 0.78 | 0.28 | 0.18 | 0.3 | 0.4898 |
cosine_precision@3 | 0.12 | 0.36 | 0.1667 | 0.1333 | 0.1733 | 0.0933 | 0.1533 | 0.08 | 0.36 | 0.2267 | 0.1733 | 0.12 | 0.4014 |
cosine_precision@5 | 0.128 | 0.364 | 0.108 | 0.1 | 0.116 | 0.068 | 0.124 | 0.064 | 0.24 | 0.2 | 0.128 | 0.092 | 0.3673 |
cosine_precision@10 | 0.082 | 0.322 | 0.064 | 0.07 | 0.068 | 0.044 | 0.096 | 0.05 | 0.132 | 0.124 | 0.088 | 0.056 | 0.3102 |
cosine_recall@1 | 0.06 | 0.0337 | 0.28 | 0.0693 | 0.16 | 0.14 | 0.0071 | 0.13 | 0.6807 | 0.0587 | 0.18 | 0.265 | 0.0365 |
cosine_recall@3 | 0.1217 | 0.0601 | 0.48 | 0.2032 | 0.26 | 0.28 | 0.0129 | 0.22 | 0.8347 | 0.1407 | 0.52 | 0.315 | 0.0858 |
cosine_recall@5 | 0.2483 | 0.1119 | 0.51 | 0.2277 | 0.29 | 0.34 | 0.0203 | 0.29 | 0.8793 | 0.2057 | 0.64 | 0.4 | 0.1256 |
cosine_recall@10 | 0.3157 | 0.1975 | 0.6 | 0.3235 | 0.34 | 0.44 | 0.0256 | 0.46 | 0.9367 | 0.2557 | 0.88 | 0.485 | 0.2065 |
cosine_ndcg@10 | 0.228 | 0.373 | 0.4359 | 0.2272 | 0.305 | 0.276 | 0.117 | 0.2707 | 0.8529 | 0.2491 | 0.5102 | 0.3689 | 0.3575 |
cosine_mrr@10 | 0.3094 | 0.5887 | 0.3857 | 0.2399 | 0.3991 | 0.2249 | 0.2652 | 0.2217 | 0.8324 | 0.3833 | 0.3946 | 0.3477 | 0.6281 |
cosine_map@100 | 0.1655 | 0.2525 | 0.3903 | 0.1836 | 0.263 | 0.2466 | 0.0304 | 0.2263 | 0.8202 | 0.2016 | 0.4002 | 0.3412 | 0.2783 |
Nano BEIR
- Dataset:
NanoBEIR_mean
- Evaluated with
NanoBEIREvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.3038 |
cosine_accuracy@3 | 0.4565 |
cosine_accuracy@5 | 0.532 |
cosine_accuracy@10 | 0.6338 |
cosine_precision@1 | 0.3038 |
cosine_precision@3 | 0.197 |
cosine_precision@5 | 0.1615 |
cosine_precision@10 | 0.1159 |
cosine_recall@1 | 0.1616 |
cosine_recall@3 | 0.2718 |
cosine_recall@5 | 0.3299 |
cosine_recall@10 | 0.4205 |
cosine_ndcg@10 | 0.3516 |
cosine_mrr@10 | 0.4016 |
cosine_map@100 | 0.2923 |
Training Details
Training Dataset
natural-questions
- Dataset: natural-questions at f9e894e
- Size: 50,000 training samples
- Columns:
query
andanswer
- Approximate statistics based on the first 1000 samples:
query answer type string string details - min: 10 tokens
- mean: 11.74 tokens
- max: 24 tokens
- min: 15 tokens
- mean: 137.2 tokens
- max: 508 tokens
- Samples:
query answer who is required to report according to the hmda
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]
what is the definition of endoplasmic reticulum in biology
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 using elect...
what does the ski mean in polish names
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:
CachedGISTEmbedLoss
with these parameters:{'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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() ), 'temperature': 0.01}
Evaluation Dataset
natural-questions
- Dataset: natural-questions at f9e894e
- Size: 100,231 evaluation samples
- Columns:
query
andanswer
- Approximate statistics based on the first 1000 samples:
query answer type string string details - min: 10 tokens
- mean: 11.78 tokens
- max: 22 tokens
- min: 11 tokens
- mean: 135.64 tokens
- max: 512 tokens
- Samples:
query answer difference between russian blue and british blue cat
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.
who played the little girl on mrs doubtfire
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.
what year did the movie the sound of music come out
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:
CachedGISTEmbedLoss
with these parameters:{'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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() ), 'temperature': 0.01}
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 2048per_device_eval_batch_size
: 2048learning_rate
: 2e-05num_train_epochs
: 1warmup_ratio
: 0.1seed
: 12bf16
: True
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 2048per_device_eval_batch_size
: 2048per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 12data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: batch_samplermulti_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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.04 | 1 | 15.537 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
0.2 | 5 | 11.6576 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
0.4 | 10 | 7.1392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
0.6 | 15 | 5.0005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
0.8 | 20 | 4.0541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1.0 | 25 | 3.4117 | 2.3797 | 0.2280 | 0.3730 | 0.4359 | 0.2272 | 0.3050 | 0.2760 | 0.1170 | 0.2707 | 0.8529 | 0.2491 | 0.5102 | 0.3689 | 0.3575 | 0.3516 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 0.157 kWh
- Carbon Emitted: 0.061 kg of CO2
- Hours Used: 0.407 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: 3.4.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 0.35.0.dev0
- Datasets: 2.20.0
- Tokenizers: 0.20.3
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",
}
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Model tree for tomaarsen/mpnet-base-nq-cgist-1-gte
Base model
microsoft/mpnet-baseDataset used to train tomaarsen/mpnet-base-nq-cgist-1-gte
Evaluation results
- Cosine Accuracy@1 on NanoClimateFEVERself-reported0.160
- Cosine Accuracy@3 on NanoClimateFEVERself-reported0.340
- Cosine Accuracy@5 on NanoClimateFEVERself-reported0.560
- Cosine Accuracy@10 on NanoClimateFEVERself-reported0.640
- Cosine Precision@1 on NanoClimateFEVERself-reported0.160
- Cosine Precision@3 on NanoClimateFEVERself-reported0.120
- Cosine Precision@5 on NanoClimateFEVERself-reported0.128
- Cosine Precision@10 on NanoClimateFEVERself-reported0.082
- Cosine Recall@1 on NanoClimateFEVERself-reported0.060
- Cosine Recall@3 on NanoClimateFEVERself-reported0.122