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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})
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("sahithkumar7/final-mpnet-base-fullfinetuned-epoch1")
5# Run inference
6sentences = [
7 'What is the number of genes obtained from comparing control and LIPUS-stimulated samples?',
8 'Differentially expressed genes (DEGs) were obtained\nbetween control and LIPUS-stimulated samples using\nan adjusted P<0.05 and|log2FC| > 1 as cutoffs to define\nstatistically significant differential expression. 676 genes\nwere obtained from which 578 were upregulated when\nstimulated with LIPUS and 98 genes were subregulated\n(Supp. Figure 1). To further understand the functions\nand pathways associated with the differentially expressed\ngenes (DEG), Gene Ontology (GO) and Kyoto Encyclo-\npedia of Genes and Genomes (KEGG) analyses were con-\nducted using the DAVID database [37, 38].',
9 'independent studies have shown a raising trend in both cancer incidence [2] and a high-salt\ndietary lifestyle [7], there is no direct correlation between dietary salt intake and breast\ncancer. Interestingly, in the human body, certain organs such as the skin and lymph nodes\nhave a natural tendency to accumulate salt [8]. Although unknown, the pathophysiological\nsignificance of this selective accumulation of sodium in certain organs and solid tumors is\nan area of intense research.',
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 768]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities)
18# tensor([[1.0000, 0.7401, 0.1070],
19# [0.7401, 1.0000, 0.1609],
20# [0.1070, 0.1609, 1.0000]])initial_test and final_testTripletEvaluator| Metric | initial_test | final_test |
|---|---|---|
| cosine_accuracy | 1.0 | 1.0 |
anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
|
|
|
| anchor | positive | negative |
|---|---|---|
What is the limitation of FBG-based sensors in tactile feedback? | Furthermore, FBG-based 3-axis tactile sensors have been[object Object]proposed for a more comprehensive haptic perception tool[object Object]in surgeries (Figure 1D) (16). Five optical fibers merged[object Object]with FBG sensors are suspended in a deformable medium[object Object]and measure the compression or tension of the tissue as the[object Object]sensors are pressed against it, returning a _ surface[object Object]reaction map. While FBG-based sensors are small, flexible, and[object Object]sensitive, there are several challenges that need to be[object Object]addressed for optimal performance for tactile feedback. These[object Object]sensors are temperature sensitive, requiring temperature | 141]. Therefore, it is not known to what extent spared[object Object]axons are remyelinated by transplanted Schwann cells,[object Object]nor is the contribution of this myelin to functional im-[object Object]provements proven. Transplantation of Schwann cells[object Object]incapable of producing myelin, such as cells derived[object Object]from trembler (Pmp22Tr) mutant mice, may be useful[object Object]in establishing a causal relationship between myelin re-[object Object]generation and functional improvements. Several MSC[object Object]transplantations demonstrate an increase of myelin re-[object Object]tention and the number of myelinated axons in the le-[object Object]sion site during a chronic post-injury period [57]. Thus, |
What are the advantages of strain elastography? | frontiersin.org[object Object][object Object]--- Page 8 ---[object Object]Kumar et al.[object Object][object Object]TABLE 2 Modalities of ultrasound elastography.[object Object][object Object]Modality[object Object]Strain elastography[object Object][object Object]Excitation[object Object]Applied manual compression (38)[object Object][object Object]Advantages[object Object][object Object]No additional specialized equipment[object Object]required (40)[object Object][object Object]10.3389/fmedt.2023.1238129[object Object][object Object]Limitations[object Object][object Object]Qualitative measurements (39)[object Object][object Object]Internal physiological mechanism (42)[object Object][object Object]Simple low-cost design (40)[object Object][object Object]Applied compression is operator-dependent (51)[object Object][object Object]More commonly used (52)[object Object][object Object]High inter-observer variability (51)[object Object][object Object]coustic radiation force impulse Acoustic radiation force (43)[object Object][object Object](ARFI) imaging[object Object][object Object]Image beyond slip boundaries (45) | Publisher’s Note: MDPI stays neutral[object Object]with regard to jurisdictional claims in[object Object]published maps and institutional afil-[object Object][object Object]iations.[object Object][object Object]onon)[object Object][object Object]Copyright: © 2021 by the author.[object Object]Licensee MDPI, Basel, Switzerland.[object Object]This article is an open access article[object Object]distributed under the terms and[object Object]conditions of the Creative Commons[object Object]Attribution (CC BY) license (https://[object Object]creativecommons.org/licenses/by/[object Object]4.0/).[object Object][object Object]Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medical College, 525 East 68th Street,[object Object]Room M-522, Box 130, New York, NY 10065, USA; [object Object] or [object Object] |
What is the material used for the substrate in a piezoelectric element? | gain for biomedical applications.[object Object][object Object]frontiersin.org[object Object][object Object]--- Page 9 ---[object Object]Kumar et al.[object Object][object Object]>[object Object][object Object][PMUT ][object Object][object Object]Electrode: Voltage Electrode2[object Object][object Object]© piezoelectric elements[object Object]o[object Object][object Object]—: OSi02[object Object][object Object]©) silicon substrate[object Object][object Object]B [ CMUT ][object Object]AC DC[object Object][object Object]membrane[object Object][object Object]—————[object Object][object Object]vacuum[object Object]insulator[object Object][object Object]substrate[object Object][object Object]= ground[object Object][object Object]FIGURE 3 | Histopatholo[object Object]Cytology Total, n (%) Benign, n (%) P ey Cancer, n (%)[object Object]FA 2 (15.4%) FTC 2 (25%)[object Object]0 GD (7.7%) PTC 6 (75%)[object Object]I 21 (4.0%) NG 9 (69.2%)[object Object]Other diagnosis (7.7%)[object Object]FA 15 (9.9%) FIC 4 (14.3%)[object Object]FT-UMP (0.7%) MTC 3 (10.7%)[object Object]GD (0.7%) PTC 21 (75%)[object Object]Il 180 (34.5%) OA (0.7%)[object Object]LT (0.7%)[object Object]NG 130 (85.5%)[object Object]NIFTP 2 (1.3%)[object Object]FA 14 (23.7%) FIC 7 (28.0%)[object Object]FI-UMP 2 (3.4%) OTC 1 (4.0%)[object Object]OA (1.7%) PTC 17 (68.0%)[object Object]Il 84 (16.1%) LT 3 (5.1%)[object Object]NG 35 (59.3%)[object Object]NIFTP 2 (3.4%)[object Object]WDT-UMP 2 (3.4%)[object Object]FA 15 (26.3%) OTC 1 (7.7%)[object Object]FT-UMP 5 (8.8%) PTC 12 (92.3%)[object Object]OA 13 (22.8%)[object Object]IV 70 (13.4%) LT 2 (3.5%)[object Object]NG 18 (31.6%)[object Object]NIFTP 2 (3.5%) |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim"
4}anchor, positive, and negative| anchor | positive | negative | |
|---|---|---|---|
| type | string | string | string |
| details |
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|
|
| anchor | positive | negative |
|---|---|---|
What can differentiate into a very wide variety of tissues? | lead to decreased rates of graft-versus-host disease. They[object Object]also can differentiate into a very wide variety of tissues. For[object Object]example, when compared with bone marrow stem cells or[object Object]mobilized peripheral blood, umbilical cord blood stem cells[object Object]have a greater repopulating ability.5° Cord blood derived[object Object]CD34+ cells have very potent hematopoietic abilities, and[object Object]this is attributed to the immaturity of the stem cells rela-[object Object]tive to adult derived cells. Studies have been done that an-[object Object]alyze long term survival of children with hematologic dis-[object Object]orders who were transplanted with umbilical cord blood | metabolic regulation may affect the function of more than one organelle. Therefore, if the[object Object]miR-17-92 regulatory cluster can perturb genes related to mitochondrial metabolic function,[object Object]it could be also related, in some way, to genes involved in lysosomal metabolic function.[object Object]Lysosomes are intracellular organelles that, in form of small vesicles, participate in[object Object]several cellular functions, mainly digestion, but also vesicle trafficking, autophagy, nutrient[object Object]sensing, cellular growth, signaling [85], and even enzyme secretion. The membrane-bound |
What are the two most common types of pluripotent stem cells? | III]. AMNIOTIC CELLS AS A SOURCE FOR STEM[object Object]CELLS[object Object][object Object]Historically, the two most common types of pluripotent[object Object]stem cells include embryonic stem cells (ESCs) and induced[object Object]pluripotent stem cells (iPSCs).35 However, despite the many[object Object]research efforts to improve ESC and iPSC technologies,[object Object]there are still enormous clinical challenges.°> Two signif-[object Object]icant issues posed by ESC and iPSC technologies include[object Object]low survival rate of transplanted cells and tumorigenicity.°>[object Object]Recently, researchers have isolated pluripotent stem cells | Explanation: criterion 6 indicates a positive diagnosis only within the DC VI group[object Object]relative to all other categories. Criterion 5 indicates a positive diagnosis within the DCs VI[object Object]and V relative to all other categories.[object Object][object Object]The highest positive predictive value (PPV) confirming malignancy through histopatho-[object Object]logical examination for criterion 6 was 0.93, and for criterion 5, it was 0.92. For the subsequent[object Object]criteria, the PPVs were as follows: criterion 4—0.66; criterion 3—0.55; criterion 2—0.40. |
What percentage of stem cells are present in bone marrow? | ing 30% in some tissues.43-45 This is a significant difference[object Object]from the .0001-.0002% stem cells present in bone marrow.43[object Object]Given this difference in stem cell concentration between[object Object]the sources, there will be more ADSCs per sample of WAT | migration of bCSCs. This finding raises the possibil-[object Object]ity that LIPUS may decrease the ability of these cells to[object Object]invade adjacent tissues and start the process of metasta-[object Object]ses. These results also suggested that some of the changes[object Object]induced by LIPUS take longer to be detected in this type[object Object]of 2D migration model, possible due to changes in gene[object Object]expression pattern. To further study this hypothesis, we[object Object]performed a Transwell invasion assay. The data revealed[object Object]a reduced number of cells crossing the membrane after[object Object]LIPUS stimulation, indicating that therapeutic LIPUS |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim"
4}eval_strategy: stepsper_device_train_batch_size: 16per_device_eval_batch_size: 16num_train_epochs: 1warmup_ratio: 0.1fp16: Truebatch_sampler: no_duplicatesoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-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: Falsefp16: Truefp16_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: Nonehub_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: 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: no_duplicatesmulti_dataset_batch_sampler: proportionalrouter_mapping: {}learning_rate_mapping: {}| Epoch | Step | Training Loss | Validation Loss | initial_test_cosine_accuracy | final_test_cosine_accuracy |
|---|---|---|---|---|---|
| -1 | -1 | - | - | 0.7800 | - |
| 0.02 | 1 | 3.124 | - | - | - |
| 0.04 | 2 | 3.2227 | - | - | - |
| 0.06 | 3 | 3.1108 | - | - | - |
| 0.08 | 4 | 3.1317 | - | - | - |
| 0.1 | 5 | 3.302 | - | - | - |
| 0.12 | 6 | 2.7996 | - | - | - |
| 0.14 | 7 | 2.9064 | - | - | - |
| 0.16 | 8 | 2.0643 | - | - | - |
| 0.18 | 9 | 1.496 | - | - | - |
| 0.2 | 10 | 1.503 | - | - | - |
| 0.22 | 11 | 1.085 | - | - | - |
| 0.24 | 12 | 0.8107 | - | - | - |
| 0.26 | 13 | 1.2284 | - | - | - |
| 0.28 | 14 | 1.2056 | - | - | - |
| 0.3 | 15 | 1.153 | - | - | - |
| 0.32 | 16 | 1.4283 | - | - | - |
| 0.34 | 17 | 1.4376 | - | - | - |
| 0.36 | 18 | 0.9779 | - | - | - |
| 0.38 | 19 | 0.9583 | - | - | - |
| 0.4 | 20 | 0.7239 | 0.7211 | 0.9400 | - |
| 0.42 | 21 | 0.953 | - | - | - |
| 0.44 | 22 | 0.8459 | - | - | - |
| 0.46 | 23 | 0.7843 | - | - | - |
| 0.48 | 24 | 0.9788 | - | - | - |
| 0.5 | 25 | 0.4665 | - | - | - |
| 0.52 | 26 | 0.8278 | - | - | - |
| 0.54 | 27 | 0.3721 | - | - | - |
| 0.56 | 28 | 0.629 | - | - | - |
| 0.58 | 29 | 0.5347 | - | - | - |
| 0.6 | 30 | 0.5305 | - | - | - |
| 0.62 | 31 | 0.4816 | - | - | - |
| 0.64 | 32 | 1.0347 | - | - | - |
| 0.66 | 33 | 0.6689 | - | - | - |
| 0.68 | 34 | 0.4525 | - | - | - |
| 0.7 | 35 | 0.1287 | - | - | - |
| 0.72 | 36 | 0.6027 | - | - | - |
| 0.74 | 37 | 0.5101 | - | - | - |
| 0.76 | 38 | 0.6194 | - | - | - |
| 0.78 | 39 | 0.2503 | - | - | - |
| 0.8 | 40 | 0.4503 | 0.6049 | 1.0 | - |
| 0.82 | 41 | 0.3446 | - | - | - |
| 0.84 | 42 | 0.3489 | - | - | - |
| 0.86 | 43 | 0.2458 | - | - | - |
| 0.88 | 44 | 0.3015 | - | - | - |
| 0.9 | 45 | 0.9199 | - | - | - |
| 0.92 | 46 | 0.334 | - | - | - |
| 0.94 | 47 | 1.2261 | - | - | - |
| 0.96 | 48 | 0.3365 | - | - | - |
| 0.98 | 49 | 0.4694 | - | - | - |
| 1.0 | 50 | 0.5731 | - | - | - |
| -1 | -1 | - | - | - | 1.0 |
1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{henderson2017efficient,
2 title={Efficient Natural Language Response Suggestion for Smart Reply},
3 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},
4 year={2017},
5 eprint={1705.00652},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}