--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:800 - loss:MultipleNegativesRankingLoss base_model: microsoft/mpnet-base widget: - source_sentence: What is the department of medicine located at? sentences: - 'Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afil- iations. onon) Copyright: © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medical College, 525 East 68th Street, Room M-522, Box 130, New York, NY 10065, USA; str2020@med.cornell.edu or Stefan.Ryter@proterris.com' - 'Results At the parameters used, the ultrasound did not directly affect bCSC proliferation, with no evident changes in morphology. In contrast, the ultrasound protocol affected the migration and invasion ability of bCSCs, limiting their capacity to advance while a major affection was detected on their angiogenic properties. LIPUS-treated bCSCs were unable to transform into aggressive metastatic cancer cells, by decreasing their migration and invasion capacity as well as vessel formation. Finally, RNA-seq analysis revealed major changes in gene expression, with 676 differentially' - 'Tesfaye, M. & Savoldo, B. Adoptive cell therapy in treating pediatric solid tumors. Curr. Oncol. Rep. 20, 73 (2018). Marofi, F. et al. CAR T cells in solid tumors: challenges and opportunities. Stem Cell Res. Ther. 12, 81 (2021). Deng, Q. et al. Characteristics of anti-CD19 CAR T cell infusion products associated with efficacy and toxicity in patients with large B cell lymphomas. Nat. Med. 26, 1878-1887 (2020). Boulch, M. A cross-talk between CAR T cell subsets and the tumor microenvironment is essential for sustained cytotoxic activity. Sci. Immunol. 6, eabd4344 (2021).' - source_sentence: What is the result of LIPUS treatment on the formation of new vessels and tubes? sentences: - 'apparatus), and mitochondrial damage, which then leads to eventual cell death [112,114]. Accordingly, alterations that affect the lysosomal-mitochondria relationship and their metabolic equilibrium generate a defective metabolism, which contributes to disease pro- gression [115]. Consequently, the identification of regulatory molecular links between these two organelles will most probably cause the rise of novel targets for the treatment of NPC. Therefore, we propose that members of the miRNA-17-92 cluster could be relevant actors' - 'A tube formation assay was conducted on Matrigel to study the impact of LIPUS stimulation on bCSCs’ angio- genic activity (Fig. 5). After 2 h, both control and LIPUS- stimulated cells exhibited signs of angiogenesis (Fig. 5A and B). This observation was further confirmed by count- ing the number of panel-like structures and vessels in both conditions, which were slightly higher in control cells (Fig. 5C). Statistical analysis using Student’s t-test revealed that LIPUS treatment significantly reduced the formation of new vessels and tubes (y=0.0039). These' - 'Although a number of preclinical studies, like the ones previously described, have shown considerable promise re- garding the use of ADSC-therapy, more studies are needed. Future studies can continue to work toward determining if hADSCs are capable of being used for cell replacement and better elucidate the mechanisms by which hADSCs work. IV. ADIPOSE TISSUE AS A SOURCE FOR STEM CELLS' - source_sentence: What percentage of cases had malignant lesions? sentences: - 'Vedolizumab Monoclonal antibody anti «487 integrins, blocks gut homing of T lymphocytes “These drugs are used as second line treatments for SR aGvHD, as reviewed by Penack et al. (11). ’Ruxolitinib has been recently approved by FDA as second line therapy for SR aGVHD. TABLE 3 | Major drugs used as second line treatment of cGvHD and their mechanisms. Drug* Major mechanisms identified Cyclosporin A, tacrolimus Calcineurin inhibitors that block downstrem TCR signalling leading to NFAT regulated genes transcription; block T cells activation' - '--- Page 4 --- J. Clin. Med. 2024, 13, 7559 4 of 13 lesions were found in 59 cases (70.24%) and malignant lesions in 25 cases (29.76%). In DC IV, benign lesions were found in 57 cases (81.4%) and malignant lesions in 13 cases (18.6%). There were no statistically significant associations between gender (p = 0.76), BMI (p = 0.52), and obesity (p = 0.76) and the presence of thyroid malignancy. Table 1. Demographic and pathologic features of 521 patients who underwent surgery due to thyroid nodules.' - 'MSCs showed that these exosomes induce angiogenesis in endothelial cells via the activation of the NF«B pathway (141). However, in another study exosomes derived from hypoxia- preconditioned MSCs contributed to the attenuation of the injury resulting from an ischemia/reperfusion episode via the Wnt signaling pathway (142). Beyond that, hypoxia seems to increase exosome secretion in general (141). Also, in a fat graft model, co-transplantation of exosomes from hypoxia pre- conditioned adipose-derived MSC improved vascularization and graft survival (143) (see Table 5).' - source_sentence: When is routine fine-needle aspiration biopsy (FS) recommended during thyroidectomy? sentences: - 'ing queries about its routine use due to the improved preoperative diagnosis. Nowadays, while the use of FS during thyroidectomy has decreased, it is still used as an additional method for different purposes intraoperatively. FS may not always provide definitive results. If FS will alter the surgical plan or extent, it should be applied. Routine FS is not recommended for evaluating thyroid nod- ules. But in addition to FNAB, if FS results may change the operation plan or extent, they can be utilized. FS should not be applied' - 'Approximately 15% of FNABs take part in this category. After their initial Bethesda | FNAB, the malignancy risk in nodules surgically excised, ranges between 5-20%. Repeat FNAB is recommended if the initial FNAB result is Bethes- da |, and in 60-80% of cases, the repeat FNAB results in a diagnostic category.''''?*°! If the second FNAB also yields a nondiagnostic result, surgical resection is recommended. 21] Especially in cases with Bethesda | FNAB and with a sur- gical indication, an intraoperative FS can be utilized.® It has been reported that FS significantly contributes to the' - 'Preconditioning with a myriad of other soluble factors, such as growth factors or hormones, seems to also potentiate MSCs regenerative capacity, mainly by stimulating angiogenesis and inhibiting fibrosis. For example, intracardiac transplantation of SDF-1-preconditioned MSCs increased angiogenesis and reduced fibrosis in the ischemic area of a post-infarct heart (89). The effects observed were attributed to the activation of the Akt signaling pathway, similarly to what was described for hypoxia- preconditioned MSCs. TGF-a-preconditioned MSCs enhanced' - source_sentence: What is the number of genes obtained from comparing control and LIPUS-stimulated samples? sentences: - 'Differentially expressed genes (DEGs) were obtained between control and LIPUS-stimulated samples using an adjusted P<0.05 and|log2FC| > 1 as cutoffs to define statistically significant differential expression. 676 genes were obtained from which 578 were upregulated when stimulated with LIPUS and 98 genes were subregulated (Supp. Figure 1). To further understand the functions and pathways associated with the differentially expressed genes (DEG), Gene Ontology (GO) and Kyoto Encyclo- pedia of Genes and Genomes (KEGG) analyses were con- ducted using the DAVID database [37, 38].' - 'Another advantage of ADSCs is their immune privilege status due to a lack of major histocompatibility complex II (MHC Il) and costimulatory molecules.42,43,45,.47 Some studies have even demonstrated a higher immunosuppres- sion capacity in ADSCs compared to BMSCs as ADSCs ex- pressed lower levels of human antigen class I (HLA I) anti- gen.47 They also have a unique secretome and can produce immunomodulatory, anti-apoptotic, hematopoietic, and angiogenic factors that can help with repair of tissues - characteristics that may support successful transplanta-' - 'independent studies have shown a raising trend in both cancer incidence [2] and a high-salt dietary lifestyle [7], there is no direct correlation between dietary salt intake and breast cancer. Interestingly, in the human body, certain organs such as the skin and lymph nodes have a natural tendency to accumulate salt [8]. Although unknown, the pathophysiological significance of this selective accumulation of sodium in certain organs and solid tumors is an area of intense research.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy model-index: - name: SentenceTransformer based on microsoft/mpnet-base results: - task: type: triplet name: Triplet dataset: name: initial test type: initial_test metrics: - type: cosine_accuracy value: 0.9800000190734863 name: Cosine Accuracy - task: type: triplet name: Triplet dataset: name: final test type: final_test metrics: - type: cosine_accuracy value: 0.9800000190734863 name: Cosine Accuracy --- # SentenceTransformer based on microsoft/mpnet-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the json 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](https://huggingface.co/microsoft/mpnet-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```bash 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("sahithkumar7/final-mpnet-base-fullfinetuned-epoch3") # Run inference sentences = [ 'What is the number of genes obtained from comparing control and LIPUS-stimulated samples?', '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].', '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.', ] 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, 0.6291, -0.0130], # [ 0.6291, 1.0000, -0.0026], # [-0.0130, -0.0026, 1.0000]]) ``` ## Evaluation ### Metrics #### Triplet * Datasets: `initial_test` and `final_test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | initial_test | final_test | |:--------------------|:-------------|:-----------| | **cosine_accuracy** | **0.98** | **0.98** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 800 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 800 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What is the limitation of FBG-based sensors in tactile feedback? | Furthermore, FBG-based 3-axis tactile sensors have been
proposed for a more comprehensive haptic perception tool
in surgeries (Figure 1D) (16). Five optical fibers merged
with FBG sensors are suspended in a deformable medium
and measure the compression or tension of the tissue as the
sensors are pressed against it, returning a _ surface
reaction map. While FBG-based sensors are small, flexible, and
sensitive, there are several challenges that need to be
addressed for optimal performance for tactile feedback. These
sensors are temperature sensitive, requiring temperature | 141]. Therefore, it is not known to what extent spared
axons are remyelinated by transplanted Schwann cells,
nor is the contribution of this myelin to functional im-
provements proven. Transplantation of Schwann cells
incapable of producing myelin, such as cells derived
from trembler (Pmp22Tr) mutant mice, may be useful
in establishing a causal relationship between myelin re-
generation and functional improvements. Several MSC
transplantations demonstrate an increase of myelin re-
tention and the number of myelinated axons in the le-
sion site during a chronic post-injury period [57]. Thus, | | What are the advantages of strain elastography? | frontiersin.org

--- Page 8 ---
Kumar et al.

TABLE 2 Modalities of ultrasound elastography.

Modality
Strain elastography

Excitation
Applied manual compression (38)

Advantages

No additional specialized equipment
required (40)

10.3389/fmedt.2023.1238129

Limitations

Qualitative measurements (39)

Internal physiological mechanism (42)

Simple low-cost design (40)

Applied compression is operator-dependent (51)

More commonly used (52)

High inter-observer variability (51)

coustic radiation force impulse Acoustic radiation force (43)

(ARFI) imaging

Image beyond slip boundaries (45) | Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional afil-

iations.

onon)

Copyright: © 2021 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).

Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medical College, 525 East 68th Street,
Room M-522, Box 130, New York, NY 10065, USA; str2020@med.cornell.edu or Stefan.Ryter@proterris.com | | What is the material used for the substrate in a piezoelectric element? | gain for biomedical applications.

frontiersin.org

--- Page 9 ---
Kumar et al.

>

[PMUT ]

Electrode: Voltage Electrode2

© piezoelectric elements
o

—: OSi02

©) silicon substrate

B [ CMUT ]
AC DC

membrane

—————

vacuum
insulator

substrate

= ground

FIGURE 3 | Histopatholo
Cytology Total, n (%) Benign, n (%) P ey Cancer, n (%)
FA 2 (15.4%) FTC 2 (25%)
0 GD (7.7%) PTC 6 (75%)
I 21 (4.0%) NG 9 (69.2%)
Other diagnosis (7.7%)
FA 15 (9.9%) FIC 4 (14.3%)
FT-UMP (0.7%) MTC 3 (10.7%)
GD (0.7%) PTC 21 (75%)
Il 180 (34.5%) OA (0.7%)
LT (0.7%)
NG 130 (85.5%)
NIFTP 2 (1.3%)
FA 14 (23.7%) FIC 7 (28.0%)
FI-UMP 2 (3.4%) OTC 1 (4.0%)
OA (1.7%) PTC 17 (68.0%)
Il 84 (16.1%) LT 3 (5.1%)
NG 35 (59.3%)
NIFTP 2 (3.4%)
WDT-UMP 2 (3.4%)
FA 15 (26.3%) OTC 1 (7.7%)
FT-UMP 5 (8.8%) PTC 12 (92.3%)
OA 13 (22.8%)
IV 70 (13.4%) LT 2 (3.5%)
NG 18 (31.6%)
NIFTP 2 (3.5%) | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### json * Dataset: json * Size: 200 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 200 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What can differentiate into a very wide variety of tissues? | lead to decreased rates of graft-versus-host disease. They
also can differentiate into a very wide variety of tissues. For
example, when compared with bone marrow stem cells or
mobilized peripheral blood, umbilical cord blood stem cells
have a greater repopulating ability.5° Cord blood derived
CD34+ cells have very potent hematopoietic abilities, and
this is attributed to the immaturity of the stem cells rela-
tive to adult derived cells. Studies have been done that an-
alyze long term survival of children with hematologic dis-
orders who were transplanted with umbilical cord blood | metabolic regulation may affect the function of more than one organelle. Therefore, if the
miR-17-92 regulatory cluster can perturb genes related to mitochondrial metabolic function,
it could be also related, in some way, to genes involved in lysosomal metabolic function.
Lysosomes are intracellular organelles that, in form of small vesicles, participate in
several cellular functions, mainly digestion, but also vesicle trafficking, autophagy, nutrient
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
CELLS

Historically, the two most common types of pluripotent
stem cells include embryonic stem cells (ESCs) and induced
pluripotent stem cells (iPSCs).35 However, despite the many
research efforts to improve ESC and iPSC technologies,
there are still enormous clinical challenges.°> Two signif-
icant issues posed by ESC and iPSC technologies include
low survival rate of transplanted cells and tumorigenicity.°>
Recently, researchers have isolated pluripotent stem cells | Explanation: criterion 6 indicates a positive diagnosis only within the DC VI group
relative to all other categories. Criterion 5 indicates a positive diagnosis within the DCs VI
and V relative to all other categories.

The highest positive predictive value (PPV) confirming malignancy through histopatho-
logical examination for criterion 6 was 0.93, and for criterion 5, it was 0.92. For the subsequent
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
from the .0001-.0002% stem cells present in bone marrow.43
Given this difference in stem cell concentration between
the sources, there will be more ADSCs per sample of WAT | migration of bCSCs. This finding raises the possibil-
ity that LIPUS may decrease the ability of these cells to
invade adjacent tissues and start the process of metasta-
ses. These results also suggested that some of the changes
induced by LIPUS take longer to be detected in this type
of 2D migration model, possible due to changes in gene
expression pattern. To further study this hypothesis, we
performed a Transwell invasion assay. The data revealed
a reduced number of cells crossing the membrane after
LIPUS stimulation, indicating that therapeutic LIPUS | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `warmup_ratio`: 0.1 - `fp16`: 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`: 16 - `per_device_eval_batch_size`: 16 - `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`: 5e-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`: 3 - `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`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `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} - `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`: 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 - `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`: False - `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
Click to expand | 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 | 3.3326 | - | - | - | | 0.12 | 6 | 2.9797 | - | - | - | | 0.14 | 7 | 3.0933 | - | - | - | | 0.16 | 8 | 2.7409 | - | - | - | | 0.18 | 9 | 2.7381 | - | - | - | | 0.2 | 10 | 2.6301 | - | - | - | | 0.22 | 11 | 2.005 | - | - | - | | 0.24 | 12 | 2.1863 | - | - | - | | 0.26 | 13 | 2.8065 | - | - | - | | 0.28 | 14 | 1.6524 | - | - | - | | 0.3 | 15 | 1.7121 | - | - | - | | 0.32 | 16 | 1.9863 | - | - | - | | 0.34 | 17 | 1.4783 | - | - | - | | 0.36 | 18 | 1.0542 | - | - | - | | 0.38 | 19 | 1.1223 | - | - | - | | 0.4 | 20 | 1.0425 | 0.9097 | 0.9000 | - | | 0.42 | 21 | 1.2517 | - | - | - | | 0.44 | 22 | 1.048 | - | - | - | | 0.46 | 23 | 1.0064 | - | - | - | | 0.48 | 24 | 0.9887 | - | - | - | | 0.5 | 25 | 0.6468 | - | - | - | | 0.52 | 26 | 0.8978 | - | - | - | | 0.54 | 27 | 0.439 | - | - | - | | 0.56 | 28 | 0.8051 | - | - | - | | 0.58 | 29 | 0.7684 | - | - | - | | 0.6 | 30 | 0.573 | - | - | - | | 0.62 | 31 | 0.6101 | - | - | - | | 0.64 | 32 | 0.9438 | - | - | - | | 0.66 | 33 | 0.8656 | - | - | - | | 0.68 | 34 | 0.5758 | - | - | - | | 0.7 | 35 | 0.2412 | - | - | - | | 0.72 | 36 | 0.4738 | - | - | - | | 0.74 | 37 | 0.7844 | - | - | - | | 0.76 | 38 | 0.7517 | - | - | - | | 0.78 | 39 | 0.3222 | - | - | - | | 0.8 | 40 | 0.466 | 0.6199 | 0.9600 | - | | 0.82 | 41 | 0.5259 | - | - | - | | 0.84 | 42 | 0.3936 | - | - | - | | 0.86 | 43 | 0.23 | - | - | - | | 0.88 | 44 | 0.4184 | - | - | - | | 0.9 | 45 | 0.7641 | - | - | - | | 0.92 | 46 | 0.2579 | - | - | - | | 0.94 | 47 | 1.2493 | - | - | - | | 0.96 | 48 | 0.4205 | - | - | - | | 0.98 | 49 | 0.4778 | - | - | - | | 1.0 | 50 | 0.545 | - | - | - | | 1.02 | 51 | 0.2018 | - | - | - | | 1.04 | 52 | 0.2048 | - | - | - | | 1.06 | 53 | 0.2031 | - | - | - | | 1.08 | 54 | 0.5784 | - | - | - | | 1.1 | 55 | 0.2764 | - | - | - | | 1.12 | 56 | 0.5112 | - | - | - | | 1.1400 | 57 | 0.2482 | - | - | - | | 1.16 | 58 | 0.3772 | - | - | - | | 1.18 | 59 | 0.1247 | - | - | - | | 1.2 | 60 | 0.1832 | 0.5882 | 1.0 | - | | 1.22 | 61 | 0.1802 | - | - | - | | 1.24 | 62 | 0.3174 | - | - | - | | 1.26 | 63 | 0.0836 | - | - | - | | 1.28 | 64 | 0.2814 | - | - | - | | 1.3 | 65 | 0.0926 | - | - | - | | 1.32 | 66 | 0.3834 | - | - | - | | 1.34 | 67 | 0.2547 | - | - | - | | 1.3600 | 68 | 0.3229 | - | - | - | | 1.38 | 69 | 0.0441 | - | - | - | | 1.4 | 70 | 0.1735 | - | - | - | | 1.42 | 71 | 0.0494 | - | - | - | | 1.44 | 72 | 0.2197 | - | - | - | | 1.46 | 73 | 0.2218 | - | - | - | | 1.48 | 74 | 0.2196 | - | - | - | | 1.5 | 75 | 0.2516 | - | - | - | | 1.52 | 76 | 0.6337 | - | - | - | | 1.54 | 77 | 0.1729 | - | - | - | | 1.56 | 78 | 0.5629 | - | - | - | | 1.58 | 79 | 0.4224 | - | - | - | | 1.6 | 80 | 0.1977 | 0.4683 | 1.0 | - | | 1.62 | 81 | 0.2117 | - | - | - | | 1.6400 | 82 | 0.2423 | - | - | - | | 1.6600 | 83 | 0.2047 | - | - | - | | 1.6800 | 84 | 0.1741 | - | - | - | | 1.7 | 85 | 0.4539 | - | - | - | | 1.72 | 86 | 0.5744 | - | - | - | | 1.74 | 87 | 0.2697 | - | - | - | | 1.76 | 88 | 0.1926 | - | - | - | | 1.78 | 89 | 0.1882 | - | - | - | | 1.8 | 90 | 0.1527 | - | - | - | | 1.8200 | 91 | 0.2154 | - | - | - | | 1.8400 | 92 | 0.5145 | - | - | - | | 1.8600 | 93 | 0.1294 | - | - | - | | 1.88 | 94 | 0.1499 | - | - | - | | 1.9 | 95 | 0.2143 | - | - | - | | 1.92 | 96 | 0.2039 | - | - | - | | 1.94 | 97 | 0.1662 | - | - | - | | 1.96 | 98 | 0.1414 | - | - | - | | 1.98 | 99 | 0.0743 | - | - | - | | 2.0 | 100 | 0.1603 | 0.4067 | 0.9800 | - | | 2.02 | 101 | 0.1885 | - | - | - | | 2.04 | 102 | 0.1539 | - | - | - | | 2.06 | 103 | 0.0592 | - | - | - | | 2.08 | 104 | 0.0874 | - | - | - | | 2.1 | 105 | 0.0873 | - | - | - | | 2.12 | 106 | 0.057 | - | - | - | | 2.14 | 107 | 0.0317 | - | - | - | | 2.16 | 108 | 0.0807 | - | - | - | | 2.18 | 109 | 0.0232 | - | - | - | | 2.2 | 110 | 0.0847 | - | - | - | | 2.22 | 111 | 0.0811 | - | - | - | | 2.24 | 112 | 0.0688 | - | - | - | | 2.26 | 113 | 0.1392 | - | - | - | | 2.2800 | 114 | 0.0681 | - | - | - | | 2.3 | 115 | 0.0329 | - | - | - | | 2.32 | 116 | 0.0177 | - | - | - | | 2.34 | 117 | 0.0794 | - | - | - | | 2.36 | 118 | 0.1128 | - | - | - | | 2.38 | 119 | 0.095 | - | - | - | | 2.4 | 120 | 0.0384 | 0.4131 | 0.9800 | - | | 2.42 | 121 | 0.0791 | - | - | - | | 2.44 | 122 | 0.078 | - | - | - | | 2.46 | 123 | 0.0232 | - | - | - | | 2.48 | 124 | 0.0265 | - | - | - | | 2.5 | 125 | 0.023 | - | - | - | | 2.52 | 126 | 0.1105 | - | - | - | | 2.54 | 127 | 0.0114 | - | - | - | | 2.56 | 128 | 0.1051 | - | - | - | | 2.58 | 129 | 0.0178 | - | - | - | | 2.6 | 130 | 0.0731 | - | - | - | | 2.62 | 131 | 0.051 | - | - | - | | 2.64 | 132 | 0.0589 | - | - | - | | 2.66 | 133 | 0.1714 | - | - | - | | 2.68 | 134 | 0.0452 | - | - | - | | 2.7 | 135 | 0.0491 | - | - | - | | 2.7200 | 136 | 0.0652 | - | - | - | | 2.74 | 137 | 0.0534 | - | - | - | | 2.76 | 138 | 0.0414 | - | - | - | | 2.7800 | 139 | 0.0611 | - | - | - | | 2.8 | 140 | 0.1983 | 0.4193 | 0.9800 | - | | 2.82 | 141 | 0.0489 | - | - | - | | 2.84 | 142 | 0.0215 | - | - | - | | 2.86 | 143 | 0.0491 | - | - | - | | 2.88 | 144 | 0.0521 | - | - | - | | 2.9 | 145 | 0.1212 | - | - | - | | 2.92 | 146 | 0.0464 | - | - | - | | 2.94 | 147 | 0.0145 | - | - | - | | 2.96 | 148 | 0.0281 | - | - | - | | 2.98 | 149 | 0.1358 | - | - | - | | 3.0 | 150 | 0.0479 | - | - | - | | -1 | -1 | - | - | - | 0.9800 |
### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.0.0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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} } ```