Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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 +1,668 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:12689
+- loss:TripletLossWithLogging
+base_model: Alibaba-NLP/gte-modernbert-base
+widget:
+- source_sentence: 'Which of the following statements is true regarding the properties
+    of zinc-activated ion channels and quaternary carbon atoms?
+
+    A. Quaternary carbon atoms are primarily involved in the activation of zinc-activated
+    ion channels.
+
+    B. Both zinc-activated ion channels and quaternary carbon atoms are unique to
+    the rat genome.
+
+    C. Zinc-activated ion channels are cation-permeable and can activate spontaneously,
+    while quaternary carbon atoms are found in hydrocarbons with at least five carbon
+    atoms.
+
+    D. Zinc-activated ion channels are exclusively found in the human genome, while
+    quaternary carbon atoms can only exist in linear alkanes.'
+  sentences:
+  - "A quaternary carbon is a carbon atom bound to four other carbon atoms. For this\
+    \ reason, quaternary carbon atoms are found only in hydrocarbons having at least\
+    \ five carbon atoms. Quaternary carbon atoms can occur in branched alkanes, but\
+    \ not in linear alkanes.\n\nSynthesis \nThe formation of chiral quaternary carbon\
+    \ centers has been a synthetic challenge. Chemists have developed asymmetric Diels–Alder\
+    \ reactions, Heck reaction, Enyne cyclization, cycloaddition reactions, C–H activation,\
+    \ Allylic substitution,  Pauson–Khand reaction,  etc. to construct asymmetric\
+    \ quaternary carbons.\n\nReferences \n\nChemical nomenclature\nOrganic chemistry"
+  - "Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious\
+    \ disease caused by Dabie bandavirus also known as the SFTS virus, first reported\
+    \ between late March and mid-July 2009 in rural areas of Hubei and Henan provinces\
+    \ in Central China. SFTS has fatality rates ranging from 12% to as high as 30%\
+    \ in some areas. The major clinical symptoms of SFTS are fever, vomiting, diarrhea,\
+    \ multiple organ failure, thrombocytopenia (low platelet count), leucopenia (low\
+    \ white blood cell count), and elevated liver enzyme levels.\n\nVirology\nSFTS\
+    \ virus (SFTSV) is a virus in the order Bunyavirales. Person-to-person transmission\
+    \ was not noted in early reports but has since been documented.\n\nThe life cycle\
+    \ of the SFTSV most likely involves arthropod vectors and animal hosts. Humans\
+    \ appear to be largely accidental hosts. SFTSV has been detected in Haemaphysalis\
+    \ longicornis ticks.\n\nEpidemiology\nSFTS occurs in China's rural areas from\
+    \ March to November with the majority of cases from April to July. In 2013, Japan\
+    \ and Korea also reported several cases with deaths.\n\nIn July 2013, South Korea\
+    \ reported a total of eight deaths since August 2012.\n\nIn July 2017, Japanese\
+    \ doctors reported that a woman had died of SFTS after being bitten by a cat that\
+    \ may have itself infected by a tick. The woman had no visible tick bites, leading\
+    \ doctors to believe that the cat — which died as well — was the transmission\
+    \ vector.\n\nIn early 2020 an outbreak occurred in East China, more than 37 people\
+    \ were found with SFTS in Jiangsu province, while 23 more were found infected\
+    \ in Anhui province in August 2020. Seven people have died.\n\nEvolution\nThe\
+    \ virus originated 50–150 years ago and has undergone a recent population expansion.\n\
+    \nHistory\nIn 2009 Xue-jie Yu and colleagues isolated the SFTS virus (SFTSV) from\
+    \ SFTS patients’ blood.\n\nReferences\n\nExternal links \n\nArthropod-borne viral\
+    \ fevers and viral haemorrhagic fevers\nInsect-borne diseases\nZoonoses"
+  - "Lecticans, also known as hyalectans, are a family of proteoglycans (a type protein\
+    \ that is attached to chains of negatively charged polysaccharides) that are components\
+    \ of the extracellular matrix.  There are four members of the lectican family:\
+    \ aggrecan, brevican, neurocan, and versican.  Lecticans interact with hyaluronic\
+    \ acid and tenascin-R to form a ternary complex.\n\nTissue distribution \n\nAggrecan\
+    \ is a major component of extracellular matrix in cartilage whereas versican is\
+    \ widely expressed in a number of connective tissues including those in vascular\
+    \ smooth muscle, skin epithelial cells, and the cells of central and peripheral\
+    \ nervous system. The expression of neurocan and brevican is largely restricted\
+    \ to neural tissues.\n\nStructure \n\nAll four lecticans contain an N-terminal\
+    \ globular domain (G1 domain) that in turn contains an immunoglobulin V-set domain\
+    \ and a Link domain that binds hyaluronic acid; a long extended central domain\
+    \ (CS) that is modified with covalently attached sulfated glycosaminoglycan chains,\
+    \ and a C-terminal globular domain (G3 domain) containing of one or more EGF repeats,\
+    \ a C-type lectin domain and a CRP-like domain. Aggrecan has in addition a globular\
+    \ domain (G2 domain) that is situated between the G1 and CS domains.\n\nSee also\
+    \ \nHyaladherin\n\nReferences \n\nProtein families"
+- source_sentence: 'What is the primary physiological process that causes the corpora
+    cavernosa to become engorged with blood during an erection?
+
+    A. Tumescence
+
+    B. Hyperemia
+
+    C. Contraction
+
+    D. Vasodilation'
+  sentences:
+  - 'Leukotriene D4 (LTD4) is one of the leukotrienes. Its main function in the body
+    is to induce the contraction of smooth muscle, resulting in bronchoconstriction
+    and vasoconstriction. It also increases vascular permeability. LTD4 is released
+    by basophils. Other leukotrienes that function in a similar manner are leukotrienes
+    C4 and E4. Pharmacological agents that inhibit the function of these leukotrienes
+    are leukotriene receptor antagonists (e.g. Zafirlukast, montelukast) and are useful
+    for asthmatic individuals.
+
+
+    References
+
+
+    Eicosanoids'
+  - "The Panasonic Lumix DMC-FZ45 (a.k.a. DMC-FZ40 in North American markets) is a\
+    \ superzoom bridge digital camera, replacing the similar Panasonic Lumix DMC-FZ38\
+    \ and earlier Panasonic Lumix DMC-FZ28. The Panasonic Lumix DMC-FZ40/FZ45 superzoom\
+    \ slots in where the FZ38/35 left off, featuring the same 25-600mm equiv. lens\
+    \ as the FZ100, but with a 14.1MP CCD sensor and simpler 230K dot 3.0 inch fixed\
+    \ LCD (as opposed to the FZ100's CMOS sensor and high-res screen). The FZ40 also\
+    \ offers AVCHD Lite 720p HD video recording, manual shooting modes and the company’s\
+    \ Sonic Speed auto-focus system that offers the industry's fastest focus times.\n\
+    \nExternal links \nSpecs on panasonic.it\nInformation regarding DMC-FZ45: https://www.dpreview.com/products/panasonic/compacts/panasonic_dmcfz40\n\
+    \nBridge digital cameras\nSuperzoom cameras\nFZ45"
+  - 'Erectile tissue is tissue in the body with numerous vascular spaces, or cavernous
+    tissue, that may become engorged with blood. However, tissue that is devoid of
+    or otherwise lacking erectile tissue (such as the labia minora, the vestibule/vagina
+    and the urethra) may also be described as engorging with blood, often with regard
+    to sexual arousal.
+
+
+    In the clitoris and penis
+
+
+    Erectile tissue exists in places such as the corpora cavernosa of the penis, and
+    in the clitoris or in the bulbs of vestibule. During erection, the corpora cavernosa
+    will become engorged with arterial blood, a process called tumescence. This may
+    result from any of various physiological stimuli, also known as sexual arousal.
+    The corpus spongiosum is a single tubular structure located just below the corpora
+    cavernosa. This may also become slightly engorged with blood, but less so than
+    the corpora cavernosa.
+
+
+    Other types
+
+    Erectile tissue is also found in the nose (turbinates), ear, urethral sponge and
+    perineal sponge. The erection of nipples is not due to erectile tissue, but rather
+    due to the contraction of smooth muscle under the control of the autonomic nervous
+    system.
+
+
+    References
+
+
+    Sexual anatomy
+
+
+    ru:Пещеристое тело'
+- source_sentence: 'What is the primary function of the supratrochlear nerve?
+
+    A. Sensory innervation to the lower jaw
+
+    B. Motor function to the muscles of facial expression
+
+    C. Motor innervation to the superior oblique muscle
+
+    D. Sensory innervation to the skin of the forehead and upper eyelid'
+  sentences:
+  - "A lung counter is a system consisting of a radiation detector, or detectors,\
+    \ and associated electronics that is used to measure radiation emitted from radioactive\
+    \ material that has been inhaled by a person and is sufficiently insoluble as\
+    \ to remain in the lung for weeks, months, or years.\n\nOften, such a system is\
+    \ housed in a low background counting chamber whose thick walls will be made of\
+    \ low-background steel (~20 cm thick) and will be lined with ~1 cm of lead, then\
+    \ perhaps thin layers of cadmium, or tin, with a final layer of copper.  The purpose\
+    \ of the lead, cadmium (or tin), and copper is to reduce the background in the\
+    \ low energy region of a gamma spectrum (typically less than 200 keV)\n\nCalibration\
+    \ \nAs a lung counter is primarily measuring radioactive materials that emit low\
+    \ energy gamma rays or x-rays, the phantom used to calibrate the system must be\
+    \ anthropometric. An example of such a phantom is the Lawrence Livermore National\
+    \ Laboratory Torso Phantom.\n\nSee also \n Bomab\n\nMedical equipment\nRadiobiology"
+  - "The supratrochlear nerve is a branch of the frontal nerve, itself a branch of\
+    \ the ophthalmic nerve (CN V1) from the trigeminal nerve (CN V). It provides sensory\
+    \ innervation to the skin of the forehead and the upper eyelid.\n\nStructure \n\
+    The supratrochlear nerve is a branch of the frontal nerve, itself a branch of\
+    \ the ophthalmic nerve (CN V1) from the trigeminal nerve (CN V). It is smaller\
+    \ than the supraorbital nerve from the frontal nerve. It branches midway between\
+    \ the base and apex of the orbit. It passes above the trochlea of the superior\
+    \ oblique muscle. It then travels anteriorly above the levator palpebrae superioris\
+    \ muscle. It exits the orbit through the frontal notch in the superomedial margin\
+    \ of the orbit. It then ascends onto the forehead beneath the corrugator supercilii\
+    \ muscle and frontalis muscle. It then divides into sensory branches.\n\nThe supratrochlear\
+    \ nerve travels with the supratrochlear artery, a branch of the ophthalmic artery.\n\
+    \nFunction \nThe supratrochlear nerve provides sensory innervation to the skin\
+    \ of the lateral lower forehead, upper eyelid, and the conjunctiva. It may also\
+    \ supply sensation to the periosteum of part of the frontal bone of the skull.\n\
+    \nClinical significance \nThe supratrochlear nerve may be anaesthetised for surgery\
+    \ of parts of the scalp. This can be used for small lesions of the scalp. It can\
+    \ also be used for more extensive injury to the scalp. It is often anaesthetised\
+    \ alongside the supraorbital artery.\n\nEtymology \nThe supratrochlear nerve is\
+    \ named for its passage above the trochlea of the superior oblique muscle.\n\n\
+    Additional images\n\nReferences\n\nExternal links \n \n \n  ()\n  ()\n http://www.dartmouth.edu/~humananatomy/figures/chapter_47/47-2.HTM\n\
+    \nOphthalmic nerve"
+  - "A Y-SNP is a single-nucleotide polymorphism on the Y chromosome. Y-SNPs are often\
+    \ used in paternal genealogical DNA testing.\n\nSNP markers\n\nA single nucleotide\
+    \ polymorphism (SNP) is a change to a single nucleotide in a DNA sequence. The\
+    \ relative mutation rate for an SNP is extremely low. This makes them ideal for\
+    \ marking the history of the human genetic tree. SNPs are named with a letter\
+    \ code and a number. The letter indicates the lab or research team that discovered\
+    \ the SNP. The number indicates the order in which it was discovered. For example\
+    \ M173 is the 173rd SNP documented by the Human Population Genetics Laboratory\
+    \ at Stanford University, which uses the letter M.\n\nSee also \nMt-SNP\nShort\
+    \ tandem repeat\nHaplogroup\nHaplotype\nGenealogical DNA test\n\nSingle-nucleotide\
+    \ polymorphisms"
+- source_sentence: 'What is the primary function of the enzyme encoded by the GCNT2
+    gene in humans?
+
+    A. Synthesis of hemoglobin
+
+    B. Formation of the blood group I antigen
+
+    C. Conversion of glucose to glycogen
+
+    D. Degradation of fatty acids'
+  sentences:
+  - 'N-acetyllactosaminide beta-1,6-N-acetylglucosaminyl-transferase is an enzyme
+    that in humans is encoded by the GCNT2 gene.
+
+
+    This gene encodes the enzyme responsible for formation of the blood group I antigen.
+    The i and I antigens are distinguished by linear and branched poly-N-acetyllactosaminoglycans,
+    respectively. The encoded protein is the I-branching enzyme, a beta-1,6-N-acetylglucosaminyltransferase
+    responsible for the conversion of fetal i antigen to adult I antigen in erythrocytes
+    during embryonic development. Mutations in this gene have been associated with
+    adult i blood group phenotype. Alternatively spliced transcript variants encoding
+    different isoforms have been described.
+
+
+    References
+
+
+    Further reading'
+  - "Telapristone (), as telapristone acetate (proposed brand names Proellex, Progenta;\
+    \ former code name CDB-4124), is a synthetic, steroidal selective progesterone\
+    \ receptor modulator (SPRM) related to mifepristone which is under development\
+    \ by Repros Therapeutics for the treatment of breast cancer, endometriosis, and\
+    \ uterine fibroids. It was originally developed by the National Institutes of\
+    \ Health (NIH), and, as of 2017, is in phase II clinical trials for the aforementioned\
+    \ indications. In addition to its activity as an SPRM, the drug also has some\
+    \ antiglucocorticoid activity.\n\nSee also\n List of investigational sex-hormonal\
+    \ agents § Progestogenics\n Aglepristone\n Lilopristone\n Onapristone\n Toripristone\n\
+    \nReferences\n\nExternal links\n Telapristone - AdisInsight\n\nAcetate esters\n\
+    Dimethylamino compounds\nAntiglucocorticoids\nEstranes\nKetones\nSelective progesterone\
+    \ receptor modulators"
+  - "Eclipse chasing is the pursuit of observing solar eclipses when they occur around\
+    \ the Earth. Solar eclipses must occur at least twice and as often as five times\
+    \ a year across the Earth. Total eclipses may occur multiple times every few years.\n\
+    \nA person who chases eclipses is known as a umbraphile, meaning shadow lover.\
+    \ Umbraphiles often travel for eclipses and use various tools to help view the\
+    \ sun including solar viewers also known as eclipse glasses, as well as telescopes.\n\
+    \nAs of 2017, three New Yorkers, Glenn Schneider, Jay Pasachoff, and John Beattie\
+    \ have each seen 33 total solar eclipses, the current record. Donald Liebenberg,\
+    \ professor of astronomy at Clemson University in South Carolina has seen 26 traveling\
+    \ to Turkey, Zambia, China, the Cook Islands and others.\n\nHistory\n\nIn the\
+    \ 19th century, Mabel Loomis Todd, an American editor and writer, and her husband\
+    \ David Peck Todd, a professor of astronomy at Amherst College, traveled around\
+    \ the world to view solar eclipses. During the solar eclipse of June 30, 1973,\
+    \ Donald Liebenberg and a group of eclipse experts observed the eclipse on board\
+    \ the Concorde and experienced 74 minutes of totality.\n\nSee also\n Solar eclipse\n\
+    \ Weather spotting\n Storm chasing\n\nReferences\n\nObservation hobbies\n2010s\
+    \ fads and trends"
+- source_sentence: 'What is the primary role of davemaoite in Earth''s lower mantle?
+
+    A. It is the most abundant mineral in the crust.
+
+    B. It acts as a catalyst for mineral formation.
+
+    C. It serves as a primary source of diamonds.
+
+    D. It contributes to heat flow through radioactive decay.'
+  sentences:
+  - "McKusick–Kaufman/Bardet–Biedl syndromes putative chaperonin is a protein that\
+    \ in humans is encoded by the MKKS gene.\n\nThis gene encodes a protein with sequence\
+    \ similarity to the chaperonin family. The encoded protein may have a role in\
+    \ protein processing in limb, cardiac and reproductive system development. Mutations\
+    \ in this gene have been observed in patients with Bardet–Biedl syndrome type\
+    \ 6 and McKusick–Kaufman syndrome. Two transcript variants encoding the same protein\
+    \ have been identified for this gene.\n\nReferences\n\nExternal links\n GeneReviews/NIH/NCBI/UW\
+    \ entry on Bardet–Biedl syndrome\n GeneReviews/NIH/NCBI/UW entry on McKusick–Kaufman\
+    \ syndrome\n\nFurther reading"
+  - "Davemaoite  is  a high-pressure calcium silicate perovskite (CaSiO3) mineral\
+    \ with a distinctive cubic crystal structure. It is named after geophysicist Ho-kwang\
+    \ (Dave) Mao, who pioneered in many discoveries in high-pressure geochemistry\
+    \ and geophysics.  \n\nIt is one of three main minerals in Earth’s lower mantle,\
+    \ making up around 5–7% of the material there. Significantly, davemaoite can host\
+    \ uranium and thorium, radioactive isotopes which produce heat through radioactive\
+    \ decay and contribute greatly to heating within this region giving the material\
+    \ a major role in how heat flows deep below the earth's surface.\n\nDavemaoite\
+    \ has been artificially synthesized in the laboratory, but was thought to be too\
+    \ extreme to exist in the Earth's crust. Then in 2021, the mineral was discovered\
+    \ as specks within a diamond that formed between 660 and 900 km beneath the Earth's\
+    \ surface, within the mantle. The diamond had been extracted from the Orapa diamond\
+    \ mine in Botswana. The discovery was made  by focusing a high-energy beam of\
+    \ X-rays on precise spots within the diamond  using a technique known as synchrotron\
+    \ X-ray diffraction. \n\nCalcium silicate is found in other forms, such as wollastonite\
+    \ in the crust and breyite in the middle and lower regions of the mantle. However,\
+    \ this version can exist only at very high pressure of around 200,000 times that\
+    \ found at Earth’s surface.\n\nSee also\n\n Perovskite (structure)\nList of minerals\n\
+    \nReferences \n\nPerovskites\nCalcium minerals"
+  - 'In molecular biology, the calcipressin family of proteins negatively regulate
+    calcineurin by direct binding. They are essential for the survival of T helper
+    type 1 cells. Calcipressin 1 is a phosphoprotein that increases its capacity to
+    inhibit calcineurin when phosphorylated at the conserved FLISPP motif; this phosphorylation
+    also controls the half-life of calcipressin 1 by accelerating its degradation.
+
+
+    In humans, the Calcipressins family of proteins is derived from three genes. Calcipressin
+    1 is also known as modulatory calcineurin-interacting protein 1 (MCIP1), Adapt78
+    and Down syndrome critical region 1 (DSCR1). Calcipressin 2 is variously known
+    as MCIP2, ZAKI-4 and DSCR1-like 1. Calcipressin 3 is also called MCIP3 and DSCR1-like
+    2.
+
+
+    References
+
+
+    Protein families'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+model-index:
+- name: SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: validation
+      type: validation
+    metrics:
+    - type: cosine_accuracy
+      value: 1.0
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base). 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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision bc02f0a92d1b6dd82108036f6cb4b7b423fb7434 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("anasse15/MNLP_M3_document_encoder")
+# Run inference
+sentences = [
+    "What is the primary role of davemaoite in Earth's lower mantle?\nA. It is the most abundant mineral in the crust.\nB. It acts as a catalyst for mineral formation.\nC. It serves as a primary source of diamonds.\nD. It contributes to heat flow through radioactive decay.",
+    "Davemaoite  is  a high-pressure calcium silicate perovskite (CaSiO3) mineral with a distinctive cubic crystal structure. It is named after geophysicist Ho-kwang (Dave) Mao, who pioneered in many discoveries in high-pressure geochemistry and geophysics.  \n\nIt is one of three main minerals in Earth’s lower mantle, making up around 5–7% of the material there. Significantly, davemaoite can host uranium and thorium, radioactive isotopes which produce heat through radioactive decay and contribute greatly to heating within this region giving the material a major role in how heat flows deep below the earth's surface.\n\nDavemaoite has been artificially synthesized in the laboratory, but was thought to be too extreme to exist in the Earth's crust. Then in 2021, the mineral was discovered as specks within a diamond that formed between 660 and 900 km beneath the Earth's surface, within the mantle. The diamond had been extracted from the Orapa diamond mine in Botswana. The discovery was made  by focusing a high-energy beam of X-rays on precise spots within the diamond  using a technique known as synchrotron X-ray diffraction. \n\nCalcium silicate is found in other forms, such as wollastonite in the crust and breyite in the middle and lower regions of the mantle. However, this version can exist only at very high pressure of around 200,000 times that found at Earth’s surface.\n\nSee also\n\n Perovskite (structure)\nList of minerals\n\nReferences \n\nPerovskites\nCalcium minerals",
+    'In molecular biology, the calcipressin family of proteins negatively regulate calcineurin by direct binding. They are essential for the survival of T helper type 1 cells. Calcipressin 1 is a phosphoprotein that increases its capacity to inhibit calcineurin when phosphorylated at the conserved FLISPP motif; this phosphorylation also controls the half-life of calcipressin 1 by accelerating its degradation.\n\nIn humans, the Calcipressins family of proteins is derived from three genes. Calcipressin 1 is also known as modulatory calcineurin-interacting protein 1 (MCIP1), Adapt78 and Down syndrome critical region 1 (DSCR1). Calcipressin 2 is variously known as MCIP2, ZAKI-4 and DSCR1-like 1. Calcipressin 3 is also called MCIP3 and DSCR1-like 2.\n\nReferences\n\nProtein families',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### 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
+
+* Dataset: `validation`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | Value   |
+|:--------------------|:--------|
+| **cosine_accuracy** | **1.0** |
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 12,689 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                          | sentence_1                                                                           | sentence_2                                                                            |
+  |:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                               | string                                                                                |
+  | details | <ul><li>min: 30 tokens</li><li>mean: 84.52 tokens</li><li>max: 198 tokens</li></ul> | <ul><li>min: 94 tokens</li><li>mean: 261.34 tokens</li><li>max: 818 tokens</li></ul> | <ul><li>min: 101 tokens</li><li>mean: 257.86 tokens</li><li>max: 752 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | sentence_2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What type of model is the TaiWan Ionospheric Model (TWIM)?<br>A. A one-dimensional thermal model of the Earth's crust<br>B. A two-dimensional statistical model of atmospheric pressure<br>C. A four-dimensional quantum model of particle interactions<br>D. A three-dimensional numerical and phenomenological model of ionospheric electron density</code>                                                                                                               | <code>The TaiWan Ionospheric Model (TWIM) developed in 2008 is a three-dimensional numerical and phenomenological model of ionospheric electron density (Ne). The TWIM has been constructed from global distributed ionosonde foF2 and foE data and vertical Ne profiles retrieved from FormoSat3/COSMIC GPS radio occultation measurements. The TWIM consists of vertically fitted α-Chapman-type layers, with distinct F2, F1, E, and D layers, for which the layer parameters such as peak density, peak density height, and scale height are represented by surface spherical harmonics. These results are useful for providing reliable radio propagation predictions and in investigation of near-Earth space and large-scale Ne distribution with diurnal and seasonal variations, along with geographic features such as the equatorial anomaly. This way the continuity of Ne and its derivatives is also maintained for practical schemes for providing reliable radio propagation predictions.<br><br>References <br><br>The information in thi...</code> | <code>Chandrasekhar–Kendall functions are the axisymmetric eigenfunctions of the curl operator, derived by Subrahmanyan Chandrasekhar and P.C. Kendall in 1957, in attempting to solve the force-free magnetic fields. The results were independently derived by both, but were agreed to publish the paper together.<br><br>If the force-free magnetic field equation is written as  with the assumption of divergence free field (), then the most general solution for axisymmetric case is<br><br>where  is a unit vector and the scalar function  satisfies the Helmholtz equation, i.e.,<br><br>The same equation also appears in fluid dynamics in Beltrami flows where, vorticity vector is parallel to the velocity vector, i.e., .<br><br>Derivation<br><br>Taking curl of the equation  and using this same equation, we get<br><br>.<br><br>In the vector identity , we can set  since it is solenoidal, which leads to a vector Helmholtz equation,<br><br>.<br><br>Every solution of above equation is not the solution of original equation, but the converse is true.  If  is a scal...</code> |
+  | <code>What is the primary function of the protein encoded by the PFN2 gene?<br>A. Facilitating lipid metabolism<br>B. Regulating actin polymerization<br>C. Encoding DNA repair enzymes<br>D. Transporting oxygen in blood</code>                                                                                                                                                                                                                                                 | <code>Profilin-2 is a protein that in humans is encoded by the PFN2 gene.<br><br>The protein encoded by this gene is a ubiquitous actin monomer-binding protein belonging to the profilin family. It is thought to regulate actin polymerization in response to extracellular signals. There are two alternatively spliced transcript variants encoding different isoforms described for this gene.<br><br>Interactions<br>PFN2 has been shown to interact with ROCK1, Vasodilator-stimulated phosphoprotein, CCDC113 and FMNL1.<br><br>References<br><br>Further reading<br><br>External links</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>Stearoyl-CoA is a coenzyme involved in the metabolism of fatty acids. Stearoyl-CoA is an 18-carbon long fatty acyl-CoA chain that participates in an unsaturation reaction. The reaction is catalyzed by the enzyme stearoyl-CoA desaturase, which is located in the endoplasmic reticulum. It forms a cis-double bond between the ninth and tenth carbons within the chain to form the product oleoyl-CoA.<br><br>References<br><br>Bibliography <br><br>Metabolism<br>Thioesters of coenzyme A</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+  | <code>Which of the following statements is true regarding the properties of certain mathematical spaces and their relevance in functional analysis?<br>A. Souslin spaces are always separable and complete metrizable.<br>B. All Polish spaces are K-analytic but not all K-analytic spaces are Polish.<br>C. The Borel graph theorem applies only to finite-dimensional spaces.<br>D. The VEZF1 gene is involved in the continuity of linear maps in functional analysis.</code> | <code>Vascular endothelial zinc finger 1 is a protein that in humans is encoded by the VEZF1 gene.<br><br>Function<br><br>Transcriptional regulatory proteins containing tandemly repeated zinc finger domains are thought to be involved in both normal and abnormal cellular proliferation and differentiation. ZNF161 is a C2H2-type zinc finger protein (Koyano-Nakagawa et al., 1994 [PubMed 8035792]). See MIM 603971 for general information on zinc finger proteins.<br><br>References<br><br>Further reading</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | <code>In mathematics, a trivial semigroup (a  semigroup with one element) is a semigroup for which the cardinality of the underlying set is one. The number of distinct nonisomorphic semigroups with one element is one. If S = { a } is a semigroup with one element, then the Cayley table of S is<br><br> {| class="wikitable"<br>|-<br>!<br>! a<br>|-<br>|  a <br>| a<br>|}<br><br>The only element in S is the zero element 0 of S and is also the identity element 1 of S. However not all semigroup theorists consider the unique element in a semigroup with one element as the zero element of the semigroup. They define zero elements only in semigroups having at least two elements.<br><br>In spite of its extreme triviality, the semigroup with one element is important in many situations. It is the starting point for understanding the structure of semigroups. It serves as a counterexample in illuminating many situations. For example, the semigroup with one element is the only semigroup in which 0 = 1, that is, the zero element and the identity ele...</code>                |
+* Loss: <code>__main__.TripletLossWithLogging</code> with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 1
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### 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
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `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`: batch_sampler
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | validation_cosine_accuracy |
+|:------:|:----:|:-------------:|:--------------------------:|
+| 0.1259 | 100  | -             | 1.0                        |
+| 0.2519 | 200  | -             | 1.0                        |
+| 0.3778 | 300  | -             | 1.0                        |
+| 0.5038 | 400  | -             | 1.0                        |
+| 0.6297 | 500  | 0.1864        | 1.0                        |
+| 0.7557 | 600  | -             | 1.0                        |
+| 0.8816 | 700  | -             | 1.0                        |
+| 1.0    | 794  | -             | 1.0                        |
+
+
+### Framework Versions
+- Python: 3.12.8
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.3
+- PyTorch: 2.7.0+cu126
+- Accelerate: 1.3.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### TripletLossWithLogging
+```bibtex
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification},
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
+    year={2017},
+    eprint={1703.07737},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+<!--
+## 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 +1,45 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.3",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.3",
+    "pytorch": "2.7.0+cu126"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1afe355ed6a222045d5098d1bbc1a8087eea2c88771347f696b3f0c2c7b687d4
+size 596070136

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": 8192,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
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+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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