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MPNet base trained on GooAQ using QAT with InfoNCE + GOR

Name: mpnet-base-gooaq-qat-nanobeir-eval
Availability: InStock

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the gooaq 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:
- gooaq
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, 'architecture': '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-gooaq-qat-nanobeir-eval")
# Run inference
queries = [
    "is duchenne muscular dystrophy a dominant or recessive trait?",
]
documents = [
    'Duchenne muscular dystrophy is inherited in an X-linked recessive pattern. Males have only one copy of the X chromosome from their mother and one copy of the Y chromosome from their father. If their X chromosome has a DMD gene mutation, they will have Duchenne muscular dystrophy.',
    'The dream suggests captivity and it refers to your fear of punishment. Another interpretation of this dream refers to a need to do what you feel is right in waking life. Being in jail suggests that your feelings may be trapped by a limited mind and body. ... Jail also suggests repressed feelings.',
    "An automatic transmission will downshift for you when you drive uphill. However, for moderately steep slopes, it's wise to shift to the gear range marked D2, 2, or L to ascend and descend the hill. For steep slopes that you can't ascend at a speed faster than 10 mph (about 15 kph), shift to D1 or 1.",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.8144, 0.1688, 0.1896]])

Evaluation

Metrics

Information Retrieval

Datasets: NanoMSMARCO_float32, NanoNQ_float32, NanoMSMARCO_int8, NanoNQ_int8, NanoMSMARCO_binary and NanoNQ_binary
Evaluated with InformationRetrievalEvaluator

Metric	NanoMSMARCO_float32	NanoNQ_float32	NanoMSMARCO_int8	NanoNQ_int8	NanoMSMARCO_binary	NanoNQ_binary
cosine_accuracy@1	0.26	0.38	0.26	0.38	0.28	0.4
cosine_accuracy@3	0.54	0.52	0.52	0.54	0.48	0.54
cosine_accuracy@5	0.68	0.66	0.64	0.62	0.58	0.6
cosine_accuracy@10	0.72	0.76	0.76	0.72	0.68	0.7
cosine_precision@1	0.26	0.38	0.26	0.38	0.28	0.4
cosine_precision@3	0.18	0.18	0.1733	0.1867	0.16	0.1867
cosine_precision@5	0.136	0.14	0.128	0.132	0.116	0.124
cosine_precision@10	0.072	0.082	0.076	0.078	0.068	0.072
cosine_recall@1	0.26	0.35	0.26	0.35	0.28	0.38
cosine_recall@3	0.54	0.48	0.52	0.49	0.48	0.49
cosine_recall@5	0.68	0.62	0.64	0.58	0.58	0.55
cosine_recall@10	0.72	0.73	0.76	0.69	0.68	0.65
cosine_ndcg@10	0.4898	0.532	0.4973	0.5162	0.4748	0.5118
cosine_mrr@10	0.4149	0.4847	0.4141	0.4777	0.4094	0.4866
cosine_map@100	0.4276	0.4721	0.4227	0.4668	0.4217	0.4757

Nano BEIR

Dataset: NanoBEIR_mean_float32
Evaluated with NanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nq" 5 ], 6 "dataset_id": "sentence-transformers/NanoBEIR-en" 7}

Metric	Value
float32_cosine_accuracy@1	0.32
float32_cosine_accuracy@3	0.53
float32_cosine_accuracy@5	0.67
float32_cosine_accuracy@10	0.74
float32_cosine_precision@1	0.32
float32_cosine_precision@3	0.18
float32_cosine_precision@5	0.138
float32_cosine_precision@10	0.077
float32_cosine_recall@1	0.305
float32_cosine_recall@3	0.51
float32_cosine_recall@5	0.65
float32_cosine_recall@10	0.725
float32_cosine_ndcg@10	0.5109
float32_cosine_mrr@10	0.4498
float32_cosine_map@100	0.4499

Nano BEIR

Dataset: NanoBEIR_mean_int8
Evaluated with NanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nq" 5 ], 6 "dataset_id": "sentence-transformers/NanoBEIR-en" 7}

Metric	Value
int8_cosine_accuracy@1	0.32
int8_cosine_accuracy@3	0.53
int8_cosine_accuracy@5	0.63
int8_cosine_accuracy@10	0.74
int8_cosine_precision@1	0.32
int8_cosine_precision@3	0.18
int8_cosine_precision@5	0.13
int8_cosine_precision@10	0.077
int8_cosine_recall@1	0.305
int8_cosine_recall@3	0.505
int8_cosine_recall@5	0.61
int8_cosine_recall@10	0.725
int8_cosine_ndcg@10	0.5067
int8_cosine_mrr@10	0.4459
int8_cosine_map@100	0.4447

Nano BEIR

Dataset: NanoBEIR_mean_binary
Evaluated with NanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nq" 5 ], 6 "dataset_id": "sentence-transformers/NanoBEIR-en" 7}

Metric	Value
binary_cosine_accuracy@1	0.34
binary_cosine_accuracy@3	0.51
binary_cosine_accuracy@5	0.59
binary_cosine_accuracy@10	0.69
binary_cosine_precision@1	0.34
binary_cosine_precision@3	0.1733
binary_cosine_precision@5	0.12
binary_cosine_precision@10	0.07
binary_cosine_recall@1	0.33
binary_cosine_recall@3	0.485
binary_cosine_recall@5	0.565
binary_cosine_recall@10	0.665
binary_cosine_ndcg@10	0.4933
binary_cosine_mrr@10	0.448
binary_cosine_map@100	0.4487

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 90,000 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.83 tokens
max: 20 tokens
min: 15 tokens
mean: 60.45 tokens
max: 180 tokens

Samples:

question	answer
`how long does halifax take to transfer mortgage funds?`	`Bear in mind that the speed of application will vary depending on your own personal circumstances and the lender's present day-to-day performance. In some cases, applications can be approved by the lender within 24 hours, while some can take weeks or even months.`
`can you get a false pregnancy test?`	`In very rare cases, you can have a false-positive result. This means you're not pregnant but the test says you are. You could have a false-positive result if you have blood or protein in your pee. Certain drugs, such as tranquilizers, anticonvulsants, hypnotics, and fertility drugs, could cause false-positive results.`
`are ahead of its time?`	`Definition of ahead of one's/its time : too advanced or modern to be understood or appreciated during the time when one lives or works As a director, he was ahead of his time.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 10,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.93 tokens
max: 25 tokens
min: 14 tokens
mean: 60.84 tokens
max: 127 tokens

Samples:

question	answer
`should you take ibuprofen with high blood pressure?`	`In general, people with high blood pressure should use acetaminophen or possibly aspirin for over-the-counter pain relief. Unless your health care provider has said it's OK, you should not use ibuprofen, ketoprofen, or naproxen sodium. If aspirin or acetaminophen doesn't help with your pain, call your doctor.`
`how old do you have to be to work in sc?`	`The general minimum age of employment for South Carolina youth is 14, although the state allows younger children who are performers to work in show business. If their families are agricultural workers, children younger than age 14 may also participate in farm labor.`
`how to write a topic proposal for a research paper?`	`['Write down the main topic of your paper. ... ', 'Write two or three short sentences under the main topic that explain why you chose that topic. ... ', 'Write a thesis sentence that states the angle and purpose of your research paper. ... ', 'List the items you will cover in the body of the paper that support your thesis statement.']`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
bf16: 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: 64
per_device_eval_batch_size: 64
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: None
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_float32_cosine_ndcg@10	NanoNQ_float32_cosine_ndcg@10	NanoBEIR_mean_float32_float32_cosine_ndcg@10	NanoMSMARCO_int8_cosine_ndcg@10	NanoNQ_int8_cosine_ndcg@10	NanoBEIR_mean_int8_int8_cosine_ndcg@10	NanoMSMARCO_binary_cosine_ndcg@10	NanoNQ_binary_cosine_ndcg@10	NanoBEIR_mean_binary_binary_cosine_ndcg@10
-1	-1	-	-	0.1310	0.0397	0.0854	0.2484	0.0918	0.1701	0.1774	0.1411	0.1593
0.0007	1	3.5504	-	-	-	-	-	-	-	-	-	-
0.0505	71	1.7507	-	-	-	-	-	-	-	-	-	-
0.1002	141	-	0.1079	0.4843	0.4871	0.4857	0.4710	0.4889	0.4800	0.4874	0.4294	0.4584
0.1009	142	0.2234	-	-	-	-	-	-	-	-	-	-
0.1514	213	0.124	-	-	-	-	-	-	-	-	-	-
0.2004	282	-	0.0689	0.5111	0.4930	0.5020	0.5333	0.4688	0.5011	0.4690	0.4491	0.4590
0.2018	284	0.1097	-	-	-	-	-	-	-	-	-	-
0.2523	355	0.1091	-	-	-	-	-	-	-	-	-	-
0.3006	423	-	0.0608	0.5018	0.4835	0.4926	0.4854	0.4945	0.4899	0.4940	0.4567	0.4753
0.3028	426	0.079	-	-	-	-	-	-	-	-	-	-
0.3532	497	0.0875	-	-	-	-	-	-	-	-	-	-
0.4009	564	-	0.0517	0.5000	0.5215	0.5108	0.5087	0.5089	0.5088	0.4790	0.4841	0.4816
0.4037	568	0.0723	-	-	-	-	-	-	-	-	-	-
0.4542	639	0.0785	-	-	-	-	-	-	-	-	-	-
0.5011	705	-	0.0461	0.4837	0.5264	0.5051	0.4931	0.5139	0.5035	0.4937	0.4672	0.4805
0.5046	710	0.0683	-	-	-	-	-	-	-	-	-	-
0.5551	781	0.0678	-	-	-	-	-	-	-	-	-	-
0.6013	846	-	0.0447	0.4946	0.5359	0.5153	0.5188	0.5073	0.5131	0.4854	0.4987	0.4921
0.6055	852	0.068	-	-	-	-	-	-	-	-	-	-
0.6560	923	0.0682	-	-	-	-	-	-	-	-	-	-
0.7015	987	-	0.0420	0.4608	0.5115	0.4862	0.5089	0.5208	0.5149	0.4510	0.5195	0.4852
0.7065	994	0.0538	-	-	-	-	-	-	-	-	-	-
0.7569	1065	0.0584	-	-	-	-	-	-	-	-	-	-
0.8017	1128	-	0.0407	0.4807	0.5269	0.5038	0.5072	0.5172	0.5122	0.4811	0.4883	0.4847
0.8074	1136	0.0543	-	-	-	-	-	-	-	-	-	-
0.8579	1207	0.0574	-	-	-	-	-	-	-	-	-	-
0.9019	1269	-	0.0389	0.4993	0.5248	0.5120	0.4963	0.4995	0.4979	0.4931	0.5029	0.4980
0.9083	1278	0.0571	-	-	-	-	-	-	-	-	-	-
0.9588	1349	0.0496	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.4898	0.5320	0.5109	0.4973	0.5162	0.5067	0.4748	0.5118	0.4933

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.090 kWh
Carbon Emitted: 0.024 kg of CO2
Hours Used: 0.247 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: 5.3.0.dev0
Transformers: 4.57.6
PyTorch: 2.10.0+cu126
Accelerate: 1.12.0
Datasets: 4.3.0
Tokenizers: 0.22.2

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",
}

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}
}

	question	answer
type	string	string
details	min: 8 tokens mean: 11.83 tokens max: 20 tokens	min: 15 tokens mean: 60.45 tokens max: 180 tokens