MPNet base trained on GooAQ triplets with hard negatives
This is a sentence-transformers model finetuned from microsoft/mpnet-base on the train 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 tokens
- 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("tomaarsen/mpnet-base-gooaq-hard-negatives")
# Run inference
sentences = [
'are hard seltzers malt liquor?',
'Seltzer is carbonated water. “Hard seltzer” is a flavored malt beverage — essentially the same as a Lime-A-Rita or a Colt 45 or a Smirnoff Ice. These products derive their alcohol from fermented malted grains and are then carbonated, flavored and sweetened.',
'Bleaching action of chlorine is based on oxidation while that of sulphur is based on reduction. Chlorine acts with water to produce nascent oxygen. ... Sulphour dioxide removes oxygen from the coloured substance and makes it colourless.',
]
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
- Dataset:
gooaq-dev
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.7413 |
cosine_accuracy@3 | 0.8697 |
cosine_accuracy@5 | 0.9055 |
cosine_accuracy@10 | 0.9427 |
cosine_precision@1 | 0.7413 |
cosine_precision@3 | 0.2899 |
cosine_precision@5 | 0.1811 |
cosine_precision@10 | 0.0943 |
cosine_recall@1 | 0.7413 |
cosine_recall@3 | 0.8697 |
cosine_recall@5 | 0.9055 |
cosine_recall@10 | 0.9427 |
cosine_ndcg@10 | 0.8442 |
cosine_mrr@10 | 0.8124 |
cosine_map@100 | 0.8148 |
dot_accuracy@1 | 0.7384 |
dot_accuracy@3 | 0.8669 |
dot_accuracy@5 | 0.9039 |
dot_accuracy@10 | 0.9389 |
dot_precision@1 | 0.7384 |
dot_precision@3 | 0.289 |
dot_precision@5 | 0.1808 |
dot_precision@10 | 0.0939 |
dot_recall@1 | 0.7384 |
dot_recall@3 | 0.8669 |
dot_recall@5 | 0.9039 |
dot_recall@10 | 0.9389 |
dot_ndcg@10 | 0.8411 |
dot_mrr@10 | 0.8095 |
dot_map@100 | 0.812 |
Training Details
Training Dataset
train
- Dataset: train at 87594a1
- Size: 2,286,783 training samples
- Columns:
question
,answer
,negative_1
,negative_2
,negative_3
,negative_4
, andnegative_5
- Approximate statistics based on the first 1000 samples:
question answer negative_1 negative_2 negative_3 negative_4 negative_5 type string string string string string string string details - min: 8 tokens
- mean: 11.84 tokens
- max: 23 tokens
- min: 13 tokens
- mean: 59.41 tokens
- max: 158 tokens
- min: 13 tokens
- mean: 59.09 tokens
- max: 139 tokens
- min: 14 tokens
- mean: 58.61 tokens
- max: 139 tokens
- min: 14 tokens
- mean: 58.98 tokens
- max: 173 tokens
- min: 15 tokens
- mean: 59.43 tokens
- max: 137 tokens
- min: 13 tokens
- mean: 60.03 tokens
- max: 146 tokens
- Samples:
question answer negative_1 negative_2 negative_3 negative_4 negative_5 is toprol xl the same as metoprolol?
Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.
Secondly, metoprolol and metoprolol ER have different brand-name equivalents: Brand version of metoprolol: Lopressor. Brand version of metoprolol ER: Toprol XL.
Pill with imprint 1 is White, Round and has been identified as Metoprolol Tartrate 25 mg.
Interactions between your drugs No interactions were found between Allergy Relief and metoprolol. This does not necessarily mean no interactions exist. Always consult your healthcare provider.
Metoprolol is a type of medication called a beta blocker. It works by relaxing blood vessels and slowing heart rate, which improves blood flow and lowers blood pressure. Metoprolol can also improve the likelihood of survival after a heart attack.
Metoprolol starts to work after about 2 hours, but it can take up to 1 week to fully take effect. You may not feel any different when you take metoprolol, but this doesn't mean it's not working. It's important to keep taking your medicine.
are you experienced cd steve hoffman?
The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.
I Saw the Light. Showcasing the unique talent and musical influence of country-western artist Hank Williams, this candid biography also sheds light on the legacy of drug abuse and tormented relationships that contributes to the singer's legend.
(Read our ranking of his top 10.) And while Howard dresses the part of director, any notion of him as a tortured auteur or dictatorial taskmasker — the clichés of the Hollywood director — are tossed aside. He's very nice.
He was a music star too. Where're you people born and brought up? We 're born and brought up here in Anambra State at Nkpor town, near Onitsha.
At the age of 87 he has now retired from his live shows and all the traveling involved. And although he still picks up his Martin Guitar and does a show now and then, his life is now devoted to writing his memoirs.
The owner of the mysterious voice behind all these videos is a man who's seen a lot, visiting a total of 56 intimate celebrity spaces over the course of five years. His name is Joe Sabia — that's him in the photo — and he's currently the VP of creative development at Condé Nast Entertainment.
how are babushka dolls made?
Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.
A quick scan of the auction and buy-it-now listings on eBay finds porcelain doll values ranging from around $5 and $10 to several thousand dollars or more but no dolls listed above $10,000.
Japanese dolls are called as ningyō in Japanese and literally translates to 'human form'.
Matyoo: All Fresno Girl dolls come just as real children are born.
As of September 2016, there are over 100 characters. The main toy line includes 13-inch Dolls, the mini-series, and a variety of mini play-sets and plush dolls as well as Lalaloopsy Littles, smaller siblings of the 13-inch dolls. A spin-off known as "Lala-Oopsies" came out in late 2012.
LOL dolls are little baby dolls that come wrapped inside a surprise toy ball. Each ball has layers that contain stickers, secret messages, mix and match accessories–and finally–a doll. ... The doll on the ball is almost never the doll inside. Dolls are released in series, so not every doll is available all the time.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Evaluation Dataset
sentence-transformers/gooaq
- Dataset: sentence-transformers/gooaq at b089f72
- Size: 10,000 evaluation samples
- Columns:
question
andanswer
- Approximate statistics based on the first 1000 samples:
question answer type string string details - min: 8 tokens
- mean: 11.89 tokens
- max: 22 tokens
- min: 14 tokens
- mean: 59.65 tokens
- max: 131 tokens
- Samples:
question answer how to transfer data from ipad to usb?
First, in “Locations,” tap the “On My iPhone” or “On My iPad” section. Here, tap and hold the empty space, and then select “New Folder.” Name it, and then tap “Done” to create a new folder for the files you want to transfer. Now, from the “Locations” section, select your USB flash drive.
what quorn products are syn free?
['bacon style pieces.', 'bacon style rashers, chilled.', 'BBQ sliced fillets.', 'beef style and red onion burgers.', 'pieces.', 'chicken style slices.', 'fajita strips.', 'family roast.']
what is the difference between turmeric ginger?
Ginger offers a sweet and spicy zing to dishes. Turmeric provides a golden yellow colour and a warm and bitter taste with a peppery aroma.
- Loss:
MultipleNegativesRankingLoss
with these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 32per_device_eval_batch_size
: 32learning_rate
: 2e-05num_train_epochs
: 1warmup_ratio
: 0.1bf16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_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
: 42data_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
: Falseeval_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
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Click to expand
Epoch | Step | Training Loss | loss | gooaq-dev_cosine_map@100 |
---|---|---|---|---|
0 | 0 | - | - | 0.1405 |
0.2869 | 20500 | 0.5303 | - | - |
0.2939 | 21000 | 0.5328 | - | - |
0.3009 | 21500 | 0.515 | - | - |
0.3079 | 22000 | 0.5264 | 0.0297 | 0.7919 |
0.3149 | 22500 | 0.5189 | - | - |
0.3218 | 23000 | 0.5284 | - | - |
0.3288 | 23500 | 0.5308 | - | - |
0.3358 | 24000 | 0.509 | 0.0281 | 0.7932 |
0.3428 | 24500 | 0.5074 | - | - |
0.3498 | 25000 | 0.5196 | - | - |
0.3568 | 25500 | 0.5041 | - | - |
0.3638 | 26000 | 0.4976 | 0.0291 | 0.7950 |
0.3708 | 26500 | 0.5025 | - | - |
0.3778 | 27000 | 0.5175 | - | - |
0.3848 | 27500 | 0.4921 | - | - |
0.3918 | 28000 | 0.4924 | 0.0298 | 0.7938 |
0.3988 | 28500 | 0.49 | - | - |
0.4058 | 29000 | 0.4924 | - | - |
0.4128 | 29500 | 0.4902 | - | - |
0.4198 | 30000 | 0.4846 | 0.0269 | 0.7966 |
0.4268 | 30500 | 0.4815 | - | - |
0.4338 | 31000 | 0.4881 | - | - |
0.4408 | 31500 | 0.4848 | - | - |
0.4478 | 32000 | 0.4882 | 0.0264 | 0.8004 |
0.4548 | 32500 | 0.4809 | - | - |
0.4618 | 33000 | 0.4896 | - | - |
0.4688 | 33500 | 0.4744 | - | - |
0.4758 | 34000 | 0.4827 | 0.0252 | 0.8038 |
0.4828 | 34500 | 0.4703 | - | - |
0.4898 | 35000 | 0.4765 | - | - |
0.4968 | 35500 | 0.4625 | - | - |
0.5038 | 36000 | 0.4698 | 0.0269 | 0.8025 |
0.5108 | 36500 | 0.4666 | - | - |
0.5178 | 37000 | 0.4594 | - | - |
0.5248 | 37500 | 0.4621 | - | - |
0.5318 | 38000 | 0.4538 | 0.0266 | 0.8047 |
0.5387 | 38500 | 0.4576 | - | - |
0.5457 | 39000 | 0.4594 | - | - |
0.5527 | 39500 | 0.4503 | - | - |
0.5597 | 40000 | 0.4538 | 0.0265 | 0.8038 |
0.5667 | 40500 | 0.4521 | - | - |
0.5737 | 41000 | 0.4575 | - | - |
0.5807 | 41500 | 0.4544 | - | - |
0.5877 | 42000 | 0.4462 | 0.0245 | 0.8077 |
0.5947 | 42500 | 0.4491 | - | - |
0.6017 | 43000 | 0.4651 | - | - |
0.6087 | 43500 | 0.4549 | - | - |
0.6157 | 44000 | 0.4461 | 0.0262 | 0.8046 |
0.6227 | 44500 | 0.4571 | - | - |
0.6297 | 45000 | 0.4478 | - | - |
0.6367 | 45500 | 0.4482 | - | - |
0.6437 | 46000 | 0.4439 | 0.0244 | 0.8070 |
0.6507 | 46500 | 0.4384 | - | - |
0.6577 | 47000 | 0.446 | - | - |
0.6647 | 47500 | 0.4425 | - | - |
0.6717 | 48000 | 0.4308 | 0.0248 | 0.8067 |
0.6787 | 48500 | 0.4374 | - | - |
0.6857 | 49000 | 0.4342 | - | - |
0.6927 | 49500 | 0.4455 | - | - |
0.6997 | 50000 | 0.4322 | 0.0242 | 0.8077 |
0.7067 | 50500 | 0.4288 | - | - |
0.7137 | 51000 | 0.4317 | - | - |
0.7207 | 51500 | 0.4295 | - | - |
0.7277 | 52000 | 0.4291 | 0.0231 | 0.8130 |
0.7347 | 52500 | 0.4279 | - | - |
0.7417 | 53000 | 0.4287 | - | - |
0.7486 | 53500 | 0.4252 | - | - |
0.7556 | 54000 | 0.4341 | 0.0243 | 0.8112 |
0.7626 | 54500 | 0.419 | - | - |
0.7696 | 55000 | 0.4323 | - | - |
0.7766 | 55500 | 0.4252 | - | - |
0.7836 | 56000 | 0.4313 | 0.0264 | 0.8107 |
0.7906 | 56500 | 0.4222 | - | - |
0.7976 | 57000 | 0.4226 | - | - |
0.8046 | 57500 | 0.4152 | - | - |
0.8116 | 58000 | 0.4222 | 0.0236 | 0.8131 |
0.8186 | 58500 | 0.4184 | - | - |
0.8256 | 59000 | 0.4144 | - | - |
0.8326 | 59500 | 0.4242 | - | - |
0.8396 | 60000 | 0.4148 | 0.0242 | 0.8125 |
0.8466 | 60500 | 0.4222 | - | - |
0.8536 | 61000 | 0.4184 | - | - |
0.8606 | 61500 | 0.4138 | - | - |
0.8676 | 62000 | 0.4119 | 0.0240 | 0.8133 |
0.8746 | 62500 | 0.411 | - | - |
0.8816 | 63000 | 0.4172 | - | - |
0.8886 | 63500 | 0.4145 | - | - |
0.8956 | 64000 | 0.4168 | 0.0240 | 0.8137 |
0.9026 | 64500 | 0.4071 | - | - |
0.9096 | 65000 | 0.4119 | - | - |
0.9166 | 65500 | 0.403 | - | - |
0.9236 | 66000 | 0.4092 | 0.0238 | 0.8141 |
0.9306 | 66500 | 0.4079 | - | - |
0.9376 | 67000 | 0.4129 | - | - |
0.9446 | 67500 | 0.4082 | - | - |
0.9516 | 68000 | 0.4054 | 0.0235 | 0.8149 |
0.9586 | 68500 | 0.4129 | - | - |
0.9655 | 69000 | 0.4085 | - | - |
0.9725 | 69500 | 0.414 | - | - |
0.9795 | 70000 | 0.4075 | 0.0239 | 0.8142 |
0.9865 | 70500 | 0.4104 | - | - |
0.9935 | 71000 | 0.4087 | - | - |
1.0 | 71462 | - | - | 0.8148 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 3.989 kWh
- Carbon Emitted: 1.551 kg of CO2
- Hours Used: 11.599 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: 3.1.0.dev0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
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",
}
MultipleNegativesRankingLoss
@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}
}
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Model tree for tomaarsen/mpnet-base-gooaq-hard-negatives
Base model
microsoft/mpnet-baseDatasets used to train tomaarsen/mpnet-base-gooaq-hard-negatives
Evaluation results
- Cosine Accuracy@1 on gooaq devself-reported0.741
- Cosine Accuracy@3 on gooaq devself-reported0.870
- Cosine Accuracy@5 on gooaq devself-reported0.905
- Cosine Accuracy@10 on gooaq devself-reported0.943
- Cosine Precision@1 on gooaq devself-reported0.741
- Cosine Precision@3 on gooaq devself-reported0.290
- Cosine Precision@5 on gooaq devself-reported0.181
- Cosine Precision@10 on gooaq devself-reported0.094
- Cosine Recall@1 on gooaq devself-reported0.741
- Cosine Recall@3 on gooaq devself-reported0.870