Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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
+}

README.md

@@ -0,0 +1,812 @@
+---
+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; [email protected] or [email protected]'
+  - '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) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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-epoch2")
+# 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, 0.6796, 0.0930],
+#         [0.6796, 1.0000, 0.0629],
+#         [0.0930, 0.0629, 1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Datasets: `initial_test` and `final_test`
+* Evaluated with [<code>TripletEvaluator</code>](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**   |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### json
+
+* Dataset: json
+* Size: 800 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 800 samples:
+  |         | anchor                                                                            | positive                                                                             | negative                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               | string                                                                               |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 16.79 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 39 tokens</li><li>mean: 117.74 tokens</li><li>max: 265 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 116.14 tokens</li><li>max: 356 tokens</li></ul> |
+* Samples:
+  | anchor                                                                               | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the limitation of FBG-based sensors in tactile feedback?</code>        | <code>Furthermore, FBG-based 3-axis tactile sensors have been<br>proposed for a more comprehensive haptic perception tool<br>in surgeries (Figure 1D) (16). Five optical fibers merged<br>with FBG sensors are suspended in a deformable medium<br>and measure the compression or tension of the tissue as the<br>sensors are pressed against it, returning a _ surface<br>reaction map. While FBG-based sensors are small, flexible, and<br>sensitive, there are several challenges that need to be<br>addressed for optimal performance for tactile feedback. These<br>sensors are temperature sensitive, requiring temperature</code>                                                                                                          | <code>141]. Therefore, it is not known to what extent spared<br>axons are remyelinated by transplanted Schwann cells,<br>nor is the contribution of this myelin to functional im-<br>provements proven. Transplantation of Schwann cells<br>incapable of producing myelin, such as cells derived<br>from trembler (Pmp22Tr) mutant mice, may be useful<br>in establishing a causal relationship between myelin re-<br>generation and functional improvements. Several MSC<br>transplantations demonstrate an increase of myelin re-<br>tention and the number of myelinated axons in the le-<br>sion site during a chronic post-injury period [57]. Thus,</code>                                     |
+  | <code>What are the advantages of strain elastography?</code>                         | <code>frontiersin.org<br><br>--- Page 8 ---<br>Kumar et al.<br><br>TABLE 2 Modalities of ultrasound elastography.<br><br>Modality<br>Strain elastography<br><br>Excitation<br>Applied manual compression (38)<br><br>Advantages<br><br>No additional specialized equipment<br>required (40)<br><br>10.3389/fmedt.2023.1238129<br><br>Limitations<br><br>Qualitative measurements (39)<br><br>Internal physiological mechanism (42)<br><br>Simple low-cost design (40)<br><br>Applied compression is operator-dependent (51)<br><br>More commonly used (52)<br><br>High inter-observer variability (51)<br><br>coustic radiation force impulse Acoustic radiation force (43)<br><br>(ARFI) imaging<br><br>Image beyond slip boundaries (45)</code> | <code>Publisher’s Note: MDPI stays neutral<br>with regard to jurisdictional claims in<br>published maps and institutional afil-<br><br>iations.<br><br>onon)<br><br>Copyright: © 2021 by the author.<br>Licensee MDPI, Basel, Switzerland.<br>This article is an open access article<br>distributed under the terms and<br>conditions of the Creative Commons<br>Attribution (CC BY) license (https://<br>creativecommons.org/licenses/by/<br>4.0/).<br><br>Joan and Sanford I. Weill Department of Medicine, Weill Cornell Medical College, 525 East 68th Street,<br>Room M-522, Box 130, New York, NY 10065, USA; [email protected] or [email protected]</code>                     |
+  | <code>What is the material used for the substrate in a piezoelectric element?</code> | <code>gain for biomedical applications.<br><br>frontiersin.org<br><br>--- Page 9 ---<br>Kumar et al.<br><br>><br><br>[PMUT ]<br><br>Electrode: Voltage Electrode2<br><br>© piezoelectric elements<br>o<br><br>—: OSi02<br><br>©) silicon substrate<br><br>B [ CMUT ]<br>AC DC<br><br>membrane<br><br>—————<br><br>vacuum<br>insulator<br><br>substrate<br><br>= ground<br><br>FIGURE 3</code>                                                                                                                                                                                                                                                                                                                                                     | <code>Histopatholo<br>Cytology Total, n (%) Benign, n (%) P ey Cancer, n (%)<br>FA 2 (15.4%) FTC 2 (25%)<br>0 GD (7.7%) PTC 6 (75%)<br>I 21 (4.0%) NG 9 (69.2%)<br>Other diagnosis (7.7%)<br>FA 15 (9.9%) FIC 4 (14.3%)<br>FT-UMP (0.7%) MTC 3 (10.7%)<br>GD (0.7%) PTC 21 (75%)<br>Il 180 (34.5%) OA (0.7%)<br>LT (0.7%)<br>NG 130 (85.5%)<br>NIFTP 2 (1.3%)<br>FA 14 (23.7%) FIC 7 (28.0%)<br>FI-UMP 2 (3.4%) OTC 1 (4.0%)<br>OA (1.7%) PTC 17 (68.0%)<br>Il 84 (16.1%) LT 3 (5.1%)<br>NG 35 (59.3%)<br>NIFTP 2 (3.4%)<br>WDT-UMP 2 (3.4%)<br>FA 15 (26.3%) OTC 1 (7.7%)<br>FT-UMP 5 (8.8%) PTC 12 (92.3%)<br>OA 13 (22.8%)<br>IV 70 (13.4%) LT 2 (3.5%)<br>NG 18 (31.6%)<br>NIFTP 2 (3.5%)</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](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: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 200 samples:
+  |         | anchor                                                                            | positive                                                                            | negative                                                                             |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              | string                                                                               |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 17.14 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 121.3 tokens</li><li>max: 356 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 119.75 tokens</li><li>max: 356 tokens</li></ul> |
+* Samples:
+  | anchor                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What can differentiate into a very wide variety of tissues?</code>   | <code>lead to decreased rates of graft-versus-host disease. They<br>also can differentiate into a very wide variety of tissues. For<br>example, when compared with bone marrow stem cells or<br>mobilized peripheral blood, umbilical cord blood stem cells<br>have a greater repopulating ability.5° Cord blood derived<br>CD34+ cells have very potent hematopoietic abilities, and<br>this is attributed to the immaturity of the stem cells rela-<br>tive to adult derived cells. Studies have been done that an-<br>alyze long term survival of children with hematologic dis-<br>orders who were transplanted with umbilical cord blood</code> | <code>metabolic regulation may affect the function of more than one organelle. Therefore, if the<br>miR-17-92 regulatory cluster can perturb genes related to mitochondrial metabolic function,<br>it could be also related, in some way, to genes involved in lysosomal metabolic function.<br>Lysosomes are intracellular organelles that, in form of small vesicles, participate in<br>several cellular functions, mainly digestion, but also vesicle trafficking, autophagy, nutrient<br>sensing, cellular growth, signaling [85], and even enzyme secretion. The membrane-bound</code>                              |
+  | <code>What are the two most common types of pluripotent stem cells?</code> | <code>III]. AMNIOTIC CELLS AS A SOURCE FOR STEM<br>CELLS<br><br>Historically, the two most common types of pluripotent<br>stem cells include embryonic stem cells (ESCs) and induced<br>pluripotent stem cells (iPSCs).35 However, despite the many<br>research efforts to improve ESC and iPSC technologies,<br>there are still enormous clinical challenges.°> Two signif-<br>icant issues posed by ESC and iPSC technologies include<br>low survival rate of transplanted cells and tumorigenicity.°><br>Recently, researchers have isolated pluripotent stem cells</code>                                                                        | <code>Explanation: criterion 6 indicates a positive diagnosis only within the DC VI group<br>relative to all other categories. Criterion 5 indicates a positive diagnosis within the DCs VI<br>and V relative to all other categories.<br><br>The highest positive predictive value (PPV) confirming malignancy through histopatho-<br>logical examination for criterion 6 was 0.93, and for criterion 5, it was 0.92. For the subsequent<br>criteria, the PPVs were as follows: criterion 4—0.66; criterion 3—0.55; criterion 2—0.40.</code>                                                                            |
+  | <code>What percentage of stem cells are present in bone marrow?</code>     | <code>ing 30% in some tissues.43-45 This is a significant difference<br>from the .0001-.0002% stem cells present in bone marrow.43<br>Given this difference in stem cell concentration between<br>the sources, there will be more ADSCs per sample of WAT</code>                                                                                                                                                                                                                                                                                                                                                                                     | <code>migration of bCSCs. This finding raises the possibil-<br>ity that LIPUS may decrease the ability of these cells to<br>invade adjacent tissues and start the process of metasta-<br>ses. These results also suggested that some of the changes<br>induced by LIPUS take longer to be detected in this type<br>of 2D migration model, possible due to changes in gene<br>expression pattern. To further study this hypothesis, we<br>performed a Transwell invasion assay. The data revealed<br>a reduced number of cells crossing the membrane after<br>LIPUS stimulation, indicating that therapeutic LIPUS</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](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
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `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`: 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`: 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`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| 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    | 3.3266        | -               | -                            | -                          |
+| 0.12   | 6    | 2.9528        | -               | -                            | -                          |
+| 0.14   | 7    | 3.0521        | -               | -                            | -                          |
+| 0.16   | 8    | 2.5702        | -               | -                            | -                          |
+| 0.18   | 9    | 2.4701        | -               | -                            | -                          |
+| 0.2    | 10   | 2.3816        | -               | -                            | -                          |
+| 0.22   | 11   | 1.6773        | -               | -                            | -                          |
+| 0.24   | 12   | 1.5529        | -               | -                            | -                          |
+| 0.26   | 13   | 1.7036        | -               | -                            | -                          |
+| 0.28   | 14   | 1.5104        | -               | -                            | -                          |
+| 0.3    | 15   | 1.3331        | -               | -                            | -                          |
+| 0.32   | 16   | 1.8093        | -               | -                            | -                          |
+| 0.34   | 17   | 1.5907        | -               | -                            | -                          |
+| 0.36   | 18   | 1.3202        | -               | -                            | -                          |
+| 0.38   | 19   | 1.247         | -               | -                            | -                          |
+| 0.4    | 20   | 1.0266        | 0.8487          | 0.9200                       | -                          |
+| 0.42   | 21   | 1.0923        | -               | -                            | -                          |
+| 0.44   | 22   | 0.9558        | -               | -                            | -                          |
+| 0.46   | 23   | 0.9531        | -               | -                            | -                          |
+| 0.48   | 24   | 1.1518        | -               | -                            | -                          |
+| 0.5    | 25   | 0.5961        | -               | -                            | -                          |
+| 0.52   | 26   | 0.8555        | -               | -                            | -                          |
+| 0.54   | 27   | 0.3773        | -               | -                            | -                          |
+| 0.56   | 28   | 0.7775        | -               | -                            | -                          |
+| 0.58   | 29   | 0.6595        | -               | -                            | -                          |
+| 0.6    | 30   | 0.5545        | -               | -                            | -                          |
+| 0.62   | 31   | 0.6099        | -               | -                            | -                          |
+| 0.64   | 32   | 1.0897        | -               | -                            | -                          |
+| 0.66   | 33   | 0.8707        | -               | -                            | -                          |
+| 0.68   | 34   | 0.5433        | -               | -                            | -                          |
+| 0.7    | 35   | 0.1551        | -               | -                            | -                          |
+| 0.72   | 36   | 0.5062        | -               | -                            | -                          |
+| 0.74   | 37   | 0.4382        | -               | -                            | -                          |
+| 0.76   | 38   | 0.813         | -               | -                            | -                          |
+| 0.78   | 39   | 0.3402        | -               | -                            | -                          |
+| 0.8    | 40   | 0.5362        | 0.5955          | 0.9800                       | -                          |
+| 0.82   | 41   | 0.4337        | -               | -                            | -                          |
+| 0.84   | 42   | 0.4311        | -               | -                            | -                          |
+| 0.86   | 43   | 0.2757        | -               | -                            | -                          |
+| 0.88   | 44   | 0.4837        | -               | -                            | -                          |
+| 0.9    | 45   | 0.8246        | -               | -                            | -                          |
+| 0.92   | 46   | 0.2713        | -               | -                            | -                          |
+| 0.94   | 47   | 1.2741        | -               | -                            | -                          |
+| 0.96   | 48   | 0.3547        | -               | -                            | -                          |
+| 0.98   | 49   | 0.4662        | -               | -                            | -                          |
+| 1.0    | 50   | 0.5558        | -               | -                            | -                          |
+| 1.02   | 51   | 0.2286        | -               | -                            | -                          |
+| 1.04   | 52   | 0.1806        | -               | -                            | -                          |
+| 1.06   | 53   | 0.1947        | -               | -                            | -                          |
+| 1.08   | 54   | 0.5536        | -               | -                            | -                          |
+| 1.1    | 55   | 0.1474        | -               | -                            | -                          |
+| 1.12   | 56   | 0.5316        | -               | -                            | -                          |
+| 1.1400 | 57   | 0.1787        | -               | -                            | -                          |
+| 1.16   | 58   | 0.3009        | -               | -                            | -                          |
+| 1.18   | 59   | 0.0757        | -               | -                            | -                          |
+| 1.2    | 60   | 0.1751        | 0.4981          | 1.0                          | -                          |
+| 1.22   | 61   | 0.1504        | -               | -                            | -                          |
+| 1.24   | 62   | 0.2225        | -               | -                            | -                          |
+| 1.26   | 63   | 0.0926        | -               | -                            | -                          |
+| 1.28   | 64   | 0.2145        | -               | -                            | -                          |
+| 1.3    | 65   | 0.0699        | -               | -                            | -                          |
+| 1.32   | 66   | 0.3843        | -               | -                            | -                          |
+| 1.34   | 67   | 0.235         | -               | -                            | -                          |
+| 1.3600 | 68   | 0.3296        | -               | -                            | -                          |
+| 1.38   | 69   | 0.0503        | -               | -                            | -                          |
+| 1.4    | 70   | 0.3067        | -               | -                            | -                          |
+| 1.42   | 71   | 0.0799        | -               | -                            | -                          |
+| 1.44   | 72   | 0.2317        | -               | -                            | -                          |
+| 1.46   | 73   | 0.1423        | -               | -                            | -                          |
+| 1.48   | 74   | 0.1322        | -               | -                            | -                          |
+| 1.5    | 75   | 0.2092        | -               | -                            | -                          |
+| 1.52   | 76   | 0.6533        | -               | -                            | -                          |
+| 1.54   | 77   | 0.1548        | -               | -                            | -                          |
+| 1.56   | 78   | 0.5765        | -               | -                            | -                          |
+| 1.58   | 79   | 0.3432        | -               | -                            | -                          |
+| 1.6    | 80   | 0.1581        | 0.4722          | 0.9800                       | -                          |
+| 1.62   | 81   | 0.2133        | -               | -                            | -                          |
+| 1.6400 | 82   | 0.1788        | -               | -                            | -                          |
+| 1.6600 | 83   | 0.1909        | -               | -                            | -                          |
+| 1.6800 | 84   | 0.1055        | -               | -                            | -                          |
+| 1.7    | 85   | 0.5633        | -               | -                            | -                          |
+| 1.72   | 86   | 0.5562        | -               | -                            | -                          |
+| 1.74   | 87   | 0.2456        | -               | -                            | -                          |
+| 1.76   | 88   | 0.1299        | -               | -                            | -                          |
+| 1.78   | 89   | 0.127         | -               | -                            | -                          |
+| 1.8    | 90   | 0.1508        | -               | -                            | -                          |
+| 1.8200 | 91   | 0.2308        | -               | -                            | -                          |
+| 1.8400 | 92   | 0.525         | -               | -                            | -                          |
+| 1.8600 | 93   | 0.1216        | -               | -                            | -                          |
+| 1.88   | 94   | 0.1122        | -               | -                            | -                          |
+| 1.9    | 95   | 0.379         | -               | -                            | -                          |
+| 1.92   | 96   | 0.1595        | -               | -                            | -                          |
+| 1.94   | 97   | 0.1224        | -               | -                            | -                          |
+| 1.96   | 98   | 0.1442        | -               | -                            | -                          |
+| 1.98   | 99   | 0.1012        | -               | -                            | -                          |
+| 2.0    | 100  | 0.2018        | 0.4420          | 0.9800                       | -                          |
+| -1     | -1   | -             | -               | -                            | 0.9800                     |
+
+</details>
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.52.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:127c02899d65b9366b7d33010f2b2adaf4c5db0d306dcda592bb24b040eb734c
+size 437967672

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,66 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}