ozgur-celik/all-mpnet-base-v2-qaitrain500-500

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

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': 384, '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})
  (2): Normalize()
)

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("ozgur-celik/all-mpnet-base-v2-qaitrain500-500")
# Run inference
sentences = [
    'How long should a patient wait for soft tissue to heal after a tooth extraction before undergoing regenerative surgery for ridge augmentation?',
    '# Vertical periodontal regeneration\nin combination with ridge\naugmentation\n\ninterproximal bone loss, the smile line, and esthetic\nexpectations should be considered. In cases where\nthe decision is difficult to make, even after the clin-\nician has evaluated these criteria, it can also be de-\ncided during the regenerative surgery. In some cas-\nes, the clinician should consider whether there will\nbe a soft tissue defect when the extraction is per-\nformed during the regenerative surgery, and then\nthe flap design has to be at least one tooth larger. In\ncases where the extraction is performed before the\nidge augmentation, a complete soft tissue healing\ntime of about 2 months should be allowed before\nthe regenerative surgery.\nIn this chapter, technical details of interproximal\nbone and soft tissue regeneration are reviewed\nthrough a representative case of vertical periodon-\ntal regeneration in combination with ridge augmen-\ntation. The 55-year-old, healthy, male patient was\ntreated with an immediate implant at site 12 a dec-\nade previously at another clinic. The patient experi-\nenced bleeding and purulent exudate around the\nimplant and sensitivity of the neighboring lateral\nincisor shortly after implant placement. He sought\ntreatment due to an abscess around the implant.',
    'Fig 9   Ridge preservation via placement of a biomaterial to reduce shrink-\nage following tooth extraction.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6634, 0.3224],
#         [0.6634, 1.0000, 0.3342],
#         [0.3224, 0.3342, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 793 training samples
• Columns: anchor, positive, negative_1, negative_2, negative_3, and negative_4
• Approximate statistics based on the first 793 samples:

  	anchor	positive	negative_1	negative_2	negative_3	negative_4
  type	string	string	string	string	string	string
  details	min: 9 tokens mean: 26.75 tokens max: 70 tokens	min: 13 tokens mean: 220.18 tokens max: 384 tokens	min: 7 tokens mean: 130.22 tokens max: 384 tokens	min: 9 tokens mean: 124.06 tokens max: 384 tokens	min: 9 tokens mean: 129.57 tokens max: 384 tokens	min: 8 tokens mean: 132.48 tokens max: 384 tokens

• Samples:

  anchor	positive	negative_1	negative_2	negative_3	negative_4
  What specific term does Larsson use to describe the personality-dependent, additional sucking urge observed in children who engage in non-nutritive sucking behaviors?	# B) Abbau schädlicher Gewohnheiten (Habits) llerdings lutschen nicht alle diese Kinder am Daumen, während viele Kin- der auch in offensichtlich nicht belastender Umwelt dieses Verhalten durch- us zeigen. Dies führte zur Annahme, dass persönlichkeitsabhängige Ursa- chen für das Zustandekommen des Habits zumindest mitveranwortlich ind. Larsson spricht hier von einem »surplus sucking urge« (zusätzlichen Saugbedürfnis), das manche Kinder haben und das evtl. mit dem zu tun hat, was viele Eltern als »besondere Anhänglichkeit« oder » Verschmustheit« bezeichnen.	# ugar and childhood obesity same but the amount of sugar was reduced, positive effects such as lowered blood sugar, lowered LDL cholesterol, lowered triglycerides, improved liver function, and lowered insulin levels became apparent quickly. In addition, the subjects under a low-sugar diet reacted much more strongly to satiety stimuli. This shows that “one calorie is not equal to one calorie."27 Obesity and its accompanying symptoms are now among the most pressing health issues worldwide. In most cases, it is children who suffer from obesity for the rest of their lives, because, contrary to popular belief, people do not grow out of being overweight; rather it is a serious disease. While diet is not the only contributing factor to obesity, it is our responsibility as pediatric dentists to address it for the sake of our young patients’ health. Specifically, we can raise parents' awareness of this subject and expose and thereby prevent possible dietary traps. These include too frequent ...	# 8.5.1 Gingival recessions Patient-related factors include oral hygiene habits and general factors that affect wound healing after oral surgery, including the patient's age and general medic- al factors such as preexisting medical conditions like diabetes, current medications like immunosuppres- sants, and external factors like smoking and stress. Tooth- and defect-related factors of prognostic rele- vance include tooth malpositions, the presence of cer- vical defects, and the initial recession depth as well as the vertical level of the papillae adjacent to the reces- sion defect. Technique-related factors include, for ex- ample, the type of flap used, the amount of soft tissue trauma during the procedure, and the manual skill of the clinician. While patient- as well as tooth- and de- fect-related factors can be controlled to some extent by careful case and defect selection, it is above all the technique-related factors that give the clinician the op- portunity to directly influence ...	# WHEN BRUSHING IS A STRUGGLE At routine checkups, many parents report that brushing their child’s teeth is a daily struggle and they are helplessly seeking tips and tricks to avoid a “wrestling match.” What worked entirely fine when the child was an infant has uddenly become an ordeal for all concerned. Parents do not always manage to motivate their little ones to brush or have their teeth brushed with patience and fun by including play. Especially with the youngest children, discussions or positive reinforcement with different reward systems are only possible to a limited extent. Nonetheless, a solution must be found, because not brushing for days or weeks is not an option in terms of the child’s well-being and oral health. The solution is simple: Parents just have to persevere. The more calmly and confidently parents act, the quicker this period of refusal from children is over. Refusal to brush may be a child’s way of expressing desire for independence, which can turn other everyd...	# REFERENCES 23. Roberts GJ, Cleaton-Jones PE, Fatti LP, et al. Patterns of breast and bottle feeding and their associ- ation with dental caries in 1- to 4-year-old South African children. 1. Dental caries prevalence and experience. Community Dent Health 1993;10:405-413. 24. Koletzko B, Brönstrup A, Cremer M, et al. Säuglingsernährung und Ernährung der stillenden Mutter:
  What type of implant is recommended for a narrow-diameter implant case according to the Esthetic Risk Assessment?	# Table 1 Esthetic Risk Assessment (ERA) * If three-dimensional imaging is available with the tooth in place 1 Standard-diameter implant, regular connection 2 Narrow-diameter implant, narrow connection 1 Standard-diameter implant, regular connection 2 Narrow-diameter implant, narrow connection	# Single-tooth gap The implant rehabilitation of a single-tooth gap in the anterior mandible, following the loss of one mandibular incisor, requires not only a special set of instruments, but also a particularly high level of precision from the implantologist. In most cases, the resultant gap is very narrow and the available space between the roots of the neighboring teeth extreme- ly limited. Iatrogenic injury to the neighboring teeth can only be avoided with detailed preoperative radi- ologic investigations and by performing the surgical procedure with great care. If the residual ridge has healed, the freshly inserted implant can be loaded directly with a prosthesis, which is highly advanta- geous for both the patient and the dentist. Because of the limited space, implants of reduced diameter, ie, narrow platform (NP) implants such as NobelActive (diameter 3.0 and 3.3 mm) and Nobel- Direct (diameter 3.0 mm) from Nobel Biocare, or Touareg (diameter 3.0 mm) from Adin or the K.S.I. Bau...	# 2.3.1 Application of Computer Technology in Surgical Implant Dentistry Computer-guided (static) surgery. The use of a static sur- gical template that reproduces the virtual implant posi- tion directly from computerized tomographic data with- out allowing the intraoperative modification of implant position. Computer-navigated (dynamic) surgery. The use of a sur- gical navigation system that reproduces the virtual im- plant position directly from computerized tomography and allows intraoperative changes in implant position. The group recommended that, with appropriate training, experience, and pre-surgical planning, these systems might be clinically beneficial in the following clinical sit- uations: Complex anatomy • • Minimally invasive surgery . Optimization of implant placement in critical esthetic cases . Immediate loading	# The Relationship Between Peri-implant Esthetics and the Biologic Width The presence of a papilla between teeth and implants and between implants is fundamental to an accept- able esthetic result. When single implants are placed between healthy teeth, the interproximal soft tissues are maintained by adjacent bone crests. The position of the bone crest must be analyzed through an ultrasound	# Treatment guidelines Esthetic outcomes can be achieved at post-extraction sites irrespective of the timing of implant placement. Different placement times, however, present with spe- cific treatment challenges and variable predictability of esthetic outcomes. With immediate placement, a high level of clinical com- petence and experience in performing the treatment is needed. Careful case selection is required to achieve sat- isfactory esthetic outcomes. The following clinical condi- tions should be satisfied:
  Which specific shade of composite material was utilized to simulate the incisal halo effect on the left central incisor during the restorative procedure described in the Manauta case?	Q: You invented the centripetal technique in the year 1994. Is it only for posteriors? n search of a solution to overcome the challenges of restoring the proximal cavities of posterior teeth and in order to facilitate an easier and quicker technique with a more effective result, the idea of the centripetal buildup was born. The idea was to define and delimit the proximal surface with a thin layer of composite that is actually contoured with the aid of a precontoured matrix band and then to fill up the inner part of the cavity with a composite resin restorative material. This process allows for the utilization of different translucencies, transparencies, and colors of the composite resins according to the area that is being restored, meaning that the enamel that is more translucent and has a more pronounced opalescence will be restored with a certain type of composite, whereas the dentin part will be restored with a more chromatic dentin shade. The initial idea was to implement this te...	# The incisal hook technique The workflow selected in this case was first a morphological restoration of the palatal areas together with an increase in the vertical dimension and design of the incisal edges (type 1 palatal veneer). 563	Fig 4-33h Left lateral incisor. Folded protective matrices in place for enamel etching, followed by drying.	# LITERATUR 2 Chiche GJ, Pinault A. Artistic and scientific principles applied to esthetic dentistry. In: Chiche GJ, Pinault A (eds). Esthetics of Anterior Fixed Prosthodontics. Chi- cago: Quintessence, 1994:13-32	# References Pfeiffer J. Dental CAD/CAM technologies: the optical impression (I). Int 299. J Comput Dent 1998;1:29-33.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• learning_rate: 2e-05
• num_train_epochs: 2
• warmup_ratio: 0.1
• data_seed: 42
• dataloader_pin_memory: False
• hub_model_id: ozgur-celik/all-mpnet-base-v2-qaitrain500-500
• hub_private_repo: True
• eval_on_start: 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: 8
• per_device_eval_batch_size: 8
• 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: 2
• 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: 42
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• 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}
• 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: False
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: ozgur-celik/all-mpnet-base-v2-qaitrain500-500
• hub_strategy: every_save
• hub_private_repo: True
• 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: True
• 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
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0	0	-
0.5	50	1.7984
1.0	100	1.3651
1.5	150	0.704
2.0	200	0.6485

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 5.2.2
• Transformers: 4.51.3
• PyTorch: 2.8.0+cu128
• Accelerate: 1.14.0
• Datasets: 5.0.0
• Tokenizers: 0.21.4

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}
}