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1_Pooling/config.json

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+{
+    "word_embedding_dimension": 1024,
+    "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

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:16909
+- loss:TripletLoss
+base_model: BAAI/bge-large-en-v1.5
+widget:
+- source_sentence: Under what conditions is the default start position of 1 used for
+    a dimension in the resulting array?
+  sentences:
+  - "Array variables example 3: Variable Unit_Prices\n\nInheritance of dimension start\
+    \ position and index values in numerical\nexpressions.\n\nThe following non-aggregating\
+    \ non-portfolio 1-dimensional array currency variables are defined in\nthe assumption\
+    \ set Assumption_Set (in all cases the dimension name is Fund and it has character\n\
+    index values):\n\n | Dimension properties | \nVariable | Size | Start\n      \
+    \                                  position | Indices | Array elements\nUnit_Prices\
+    \ | 3 | 101 | \"A\", \"B\", \"C\" | 1.25, 0.93, 1.81\nUnit_Prices_2 | 3 | 101\
+    \ | \"A\", \"B\", \"C\" | 1.21, 0.97, 1.73\nUnit_Prices_3 | 3 | 201 | \"A\", \"\
+    B\", \"C\" | 1.32, 0.79, 1.35\nUnit_Prices_4 | 3 | 101 | \"X\", \"Y\", \"Z\" |\
+    \ 1.12, 0.89, 1.97\nUnit_Prices_5 | 2 | 102 | \"B\", \"C\" | 0.93, 1.93\nUnit_Prices_6\
+    \ | 3 | 201 | \"X\", \"Y\", \"Z\" | 1.19, 0.98, 1.95\n\nThese variables are used\
+    \ in the formula of the following variables in the program Program in the\nprojection\
+    \ process Projection_Process, which is used in the model Array_Model (all these\
+    \ variables\nhave a single dimension called Fund):\n\n |  | Dimension properties\
+    \ | \nVariable | Formula | Size | Start position | Indices | Array elements\n\
+    Variable_21 | Unit_Prices - Unit_Prices_2 | 3 | 101 | \"A\", \"B\", \"C\" | 0.04,\
+    \ -0.04, 0.08\nVariable_22 | Unit_Prices - Unit_Prices_3 | 3 | 1 | \"A\", \"B\"\
+    , \"C\" | -0.07, 0.14, 0.46\nVariable_23 | Unit_Prices - Unit_Prices_4 | 3 | 101\
+    \ | (undefined) | 0.13, 0.04, -0.16\nVariable_24 | Unit_Prices[<Fund.index= \"\
+    B\" : \"C\">] - Unit_Prices_5 | 2 | 102 | (undefined) | 0, -0.12\nVariable_25\
+    \ | Unit_Prices - Unit_Prices_6 | 3 | 1 | (undefined) | 0.06, -0.05, -0.14\n\n\
+    Notes:\n\n* The rank of the arrays (number of dimensions), dimension names and\
+    \ dimension sizes must be\nidentical for such numerical expressions to be valid.\n\
+    * If the indices in a particular dimension are the same in both arrays, they will\
+    \ be inherited by\nthe resulting array, otherwise no indices will be defined in\
+    \ that dimension.\n* If the start positions in a particular dimension are the\
+    \ same in both arrays, they will be\ninherited by the resulting array, otherwise\
+    \ the default start position of 1 will be used in that\ndimension.\n* We could\
+    \ not have a formula like Unit_Prices - Unit_Prices_5, because these arrays have\n\
+    differently sized dimensions.\n* The subset of an array variable in the formula\
+    \ of Variable_24 loses its indices. This means that\nVariable_24 cannot inherit\
+    \ consistent indices and so none are defined for it.\n* The subset of an array\
+    \ variable in the formula of Variable_24 inherits the numbering of its\nelement\
+    \ positions from the variable Unit_Prices, so its start position is set to 102.\
+    \ This is the\nsame as the start position of Unit_Prices_5, so Variable_24 has\
+    \ its dimension start position set to\n102."
+  - "## Examples\n\nSuppose:\n\nVariable is a 2-dimensional array\n\n | Dimension\
+    \ name | Size | Start position\n1 | Dimension_1 | 2 | 4\n2 | Dimension_2 | 3 |\
+    \ 7\n\nDimension_2 | Dimension_1\nPosition = 4 | Position = 5\nPosition = 7 |\
+    \ 1 | 2\nPosition = 8 | 3 | 4\nPosition = 9 | 5 | 6\n\nThen:\n\nDimension_Start(Variable,\
+    \ <Dimension_1>)\n\n= 4\n\nDimension_Start(Variable, <Dimension_2>)\n\n= 7"
+  - '## Other situations where indices are lost
+
+
+    There are a number of other circumstances where the indices for an array dimension
+    are lost:
+
+
+    * If an array has a changeable dimension and the array is aggregated acrosseventsusing
+    the.totalextension, the indices in
+
+    that dimension will be lost.
+
+    * If an array has a changeable dimension and the array is passed from a sub layer
+    to a calling
+
+    layer then the indices in that dimension will be lost.
+
+    * If an array has a changeable dimension and the array is calculated in a stochastic
+    return value
+
+    variable then the indices in that dimension will be lost.'
+- source_sentence: Where can I find examples of batches within the system?
+  sentences:
+  - "Grouping example 3: Admin_Grouping\n\nUsed in the Calculation grouping property\
+    \ of a parent program.\n\nThis grouping has the following properties:\n\nProperty\
+    \ | Value\nName | Admin_Grouping\nCategory | Policy\nDescription | Group by method\
+    \ of policy administration\nGroup identifier | Internet_Admin.text\n\nThis grouping\
+    \ contains just one group:\n\nProperty | Value\nName | Internet_Admin\nCategory\
+    \ | Policy\nDescription | Group Internet_Admin by value\nData type | Indicator\n\
+    Grouping expression | Internet_Admin\nMethod | By value\nRange boundaries | \n\
+    Boundary value in | [Range above]\n\nThe Grouping expression property is set to\
+    \ the indicator variable Internet_Admin, so the Data\ntype property must be set\
+    \ to\n\nIndicator\n\n.\n\nThe Method property is set to\n\nBy value\n\n(so the\
+    \ Range boundaries and Boundary value in\nproperties will be ignored) and the\
+    \ records will be grouped together according to the value of the\ngrouping expression.\
+    \ In the\n\ndata view\n\nTraditional_Data_View\n\nthe variable\nInternet_Admin\
+    \ is read from data, but is expected to take one of two possible values. Since\
+    \ this\nvariable defines the grouping expression of this group, there should be\
+    \ up to two groups. If the\ndata file contains additional values, there will be\
+    \ additional groups.\n\nThis grouping is specified as the\n\nCalculation grouping\n\
+    \nproperty of the program\n\nCompany\n\nof the projection process\n\nRealistic_Projection\n\
+    \n. This program is a parent program and the records being passed to it by\nits\
+    \ child programs will be grouped according to this grouping before being processed\
+    \ by the\nprogram.\n\nThe Group identifier property of the grouping will be used\
+    \ to provide the value of the\n\nsystem variable\n\nGroup_Identifier in this\n\
+    \nprogram\n\nand to provide a unique group\nidentifier for each of its groups.\
+    \ These group identifiers will be \"Internet_Admin=0\" and\n\"Internet_Admin=1\"\
+    ."
+  - "The main topic 'Batches' has the following related sub-topics:\n* **Batch examples**\
+    \ : \nThe example user workspace includes examples of batches."
+  - 'Batch examples
+
+
+    The example user workspace includes examples of batches.
+
+
+    No. | Name | Features
+
+    1 | EV_Batch | Contains very similar models that have slightly different realistic
+    assumptions
+
+    2 | EV_Batch_2 | Use of theModel string overrideproperty to access different external
+    assumption files'
+- source_sentence: 'How does accessing a subset of an array using an expression like
+    `Array_1[<Fund.position = 3 : 6>]` affect the dimension start positions?'
+  sentences:
+  - "## Inheritance rules for the dimension start positions of an array variable\n\
+    \nAn array variable inherits the dimension start positions of the array variables\
+    \ in its formula\naccording to the points below.\n\nIt is not necessary for different\
+    \ assignments for an array variable to return the same start\nposition. For example,\
+    \ the following formula is valid even when Array_2 and Array_3 have different\n\
+    dimension start positions:\n\nIf Scalar_A > 3 Then\n      Array_2\nElse\n    \
+    \  Array_3\nEndIf\n\nAn array variable inherits the dimension start positions\
+    \ (and hence the element position numbers)\n                        of the arrays\
+    \ (after any function calls) used in its formula. It is not\n                \
+    \        necessary for these to be identical. If the start positions in any dimension\n\
+    \                        differ between arrays in a formula then\n\nR³S Modeler\n\
+    \nsets the\n                        start position in that dimension in the calculated\
+    \ array to the default\n                        value of 1. A message will be\
+    \ added to the\n\nIndex\n                              and Position Warnings\n\
+    \nfolder of the\n\nRun summary\n\nto\n                        indicate this has\
+    \ happened.\n\nSimple mathematical operations on an array will preserve the dimension\
+    \ start positions.\n\nAccessing a subset of an array with an expression like\n\
+    \nArray_1[<Fund\n\n.position\n\n= 3 :\n6>]\n\nwill cause the dimension start positions\
+    \ in the resulting array to be set so as to\npreserve the numbering of the element\
+    \ positions for all its elements. In this example, the start\nposition of the\
+    \ dimension Fund will be set to 3 in the resulting array.\n\nFunctions of arrays\
+    \ generally produce an array with the same dimension start positions as the\n\
+    inherited dimensions."
+  - "## Example 1: Step_Length_PS\n\nProperty | Value\nName | Step_Length_PS\nCategory\
+    \ | \nDescription | \nDocumentation | \n\nThis layer module contains no sub layer\
+    \ modules and just one layer variable:\n\nVariable | Layer module | Formula\n\
+    Step_Length_PS | Step_Length_PS | Duration(Step_Date.start, Step_Date.end, \"\
+    Years\", \"One\", \"Exact\")\n\nThis layer module is a sub layer module of the\
+    \ layer module Expense_Renewal."
+  - '## Accessing a subset of an array
+
+
+    Any dimension indices will always be inherited when accessing a subset of another
+    array with an
+
+    expression like Array_1[<Fund
+
+
+    .position
+
+
+    = 3 : 6>] or Array_1[<Fund
+
+
+    .position
+
+
+    = First_Fund :
+
+    Last_Fund>]. When it is not possible to determine both the start and end indices
+    or element
+
+    positions (that is, the variables First_Fund and Last_Fund in the second example)
+    until runtime and
+
+    the subset of the array variable is used in a mathematical expression then an
+    index and position
+
+    warning message will be issued to state that the indices for a dimension may not
+    match, and if not
+
+    that only one of the set of indices will be used which may lead to runtime errors
+    or misleading
+
+    results.'
+- source_sentence: What kind of variable is Data_Process_Name considered?
+  sentences:
+  - 'Data_Process_Name
+
+
+    The
+
+
+    Data_Process_Name
+
+
+    system variable is a character variable that gives the name of the data process.
+
+
+    You can use this system variable in a data process in the data layer of a model.
+
+
+    This system variable is a placeholder variable.'
+  - "Assumption set example 4:\n        Traditional_Reserve_Assumptions\n\nAn assumption\
+    \ set used as the assumption set of a sub layer containing an assumption set\n\
+    variable that references an assumption set variable in the assumption set of the\
+    \ calling layer using\nthe\n\nSource\n\nqualifier.\n\nThis example describes the\
+    \ assumptions that might be used in a sub layer to calculate reserve\nprovisions.\n\
+    \nAssumption set local properties:\n\nProperty | Value\nName | Traditional_Reserve_Assumptions\n\
+    Category | Traditional_Component\nDescription | Traditional (non-linked without-profit)\
+    \ reserve assumptions\nAssumption connection string | \n\nThis assumption set\
+    \ has no sub assumption sets.\n\nThis assumption set contains several assumption\
+    \ set variables. These variables have the following\nglobal properties (they all\
+    \ have their\n\nAggregates\n\nand\n\nPortfolio\n\nproperties set to\n\nNo\n\n\
+    ):\n\nVariable | Data type | Display format\nDisc_Rate_Reserve | Numeric | Per\
+    \ cent\nMort_Table_F | Life table | [None]\nMort_Table_M | Life table | [None]\n\
+    \nThey have the following local properties in this assumption set (they all have\
+    \ their\n\nAssumption table\n\nproperty left blank):\n\nVariable | Formula\nDisc_Rate_Reserve\
+    \ | Max(Source.Int_Rate - 3%, 0%)\nMort_Table_F | 105% *AM92\nMort_Table_M | 120%\
+    \ * AM92 + 1‰\n\nNotes:\n\n* TheSourcequalifier in the formula of Disc_Rate_Reserve\
+    \ specifies that the value of Int_Rate in\nthe calling layer is to be used.\n\
+    * The per cent (%) and per mille (‰) characters may be included in formulas and\
+    \ have the\neffect of dividing by 100 and 1000 respectively, so 3% is interpreted\
+    \ as 0.03 and 1‰ is\ninterpreted as 0.001.\n\nThis assumption set is specified\
+    \ in the local properties of the sub layers Reserve_Sub_Layer and\nReserve_Sub_Layer_2\
+    \ of the layer\n\nRealistic_Layer\n\nof the\nmodel\n\nEV_Model\n\n."
+  - '## New system variables
+
+
+    The system variables
+
+
+    Data_Process_Name
+
+
+    ,
+
+
+    Data_Source_Name
+
+
+    ,
+
+
+    Layer_Name
+
+
+    , and
+
+
+    Program_Name
+
+
+    are placeholder variables.'
+- source_sentence: Is there a specific location where I can find workspace filters?
+  sentences:
+  - '## Windows
+
+
+    You can access the filters of a workspace in the grid of filters.
+
+
+    The filter window has most properties of the filter.'
+  - 'Category examples
+
+
+    The example user workspace includes examples of categories.
+
+
+    Some categories that might be useful in a present value of future profits model
+    include:
+
+
+    Name | Category type | Description
+
+    Ages_Dates_Durations |  | Items relating to ages, dates and durations
+
+    Asset_Shares |  | Items relating to asset shares
+
+    Benefits |  | Items relating to benefits
+
+    Bonuses |  | Items relating to bonuses
+
+    Cash_Flow_Module | Modules | Module for general cash flows
+
+    Cash_Flows |  | Items relating to cash flows
+
+    Commission |  | Items relating to commission
+
+    Commutation_Function_Reserve_Module | Modules | Module for reserving using commutation
+    functions
+
+    Decrements |  | Decrement tables and rates
+
+    Economic |  | Economic assumptions and variables
+
+    EU | Data flow | EU non-linked data
+
+    Data flow |  | Items relating to expenses
+
+    Flags |  | Items that are set as flags
+
+    Fund_Charges |  | Items related to unit fund charges
+
+    General_Module | Modules | Module suitable for many situations
+
+    Interest |  | Items relating to interest
+
+    Maturities |  | Items relating to maturities
+
+    Mortality |  | Items relating to mortality
+
+    Multiple_Currencies |  | Component for use with a multiple currency model
+
+    NP_End | Data flow | Programs, data sources, and so on, for non-profit endowment
+    assurances
+
+    NP_Term | Data flow | Programs, data sources and so on for non-profit term assurances
+
+    NP_WoL | Data flow | Programs, data sources, and so on, for non-profit whole of
+    life assurances
+
+    Policy |  | Items relating to policies
+
+    Premiums |  | Items relating to premiums
+
+    Probabilities |  | Items relating to probabilities
+
+    Profit |  | Items relating to profit
+
+    PVFP |  | Items relating to present value of future profits
+
+    PVFP_Module | Modules | Module for present value of future profits
+
+    Reserve_Module | Modules | Module for reserving by projection of cash flows
+
+    Reserves |  | Items relating to reserves
+
+    Solvency_Margin |  | Items relating to solvency margin
+
+    Statistics |  | Items relating to statistics
+
+    Surrenders |  | Items relating to surrenders
+
+    Tax |  | Items relating to tax
+
+    Traditional | Data flow | Traditional/non-profit/conventional non-linked business
+
+    Traditional_Component |  | Traditional/conventional/non-linked/non-profit business
+    component
+
+    UK | Data flow | UK non-linked data
+
+    Data flow | Data flow | Unit-linked endowment assurance
+
+    Unit_Fund |  | Items related to the unit fund
+
+    Unit_Linked | Data flow | Programs, and so on, for unit linked business
+
+    Unit_Linked_Component |  | Unit-linked business component
+
+    Unit_Linked_Module | Modules | Module for unit-linked business
+
+    US | Data flow | US non-linked data
+
+    Valuation |  | Items relating to valuations
+
+
+    Categories provide the items on a drop-down list in the
+
+
+    Category
+
+
+    property that can be used to help organize related components.
+
+
+    There are different possible values for the Category type property, including:
+
+
+    * If the category type is left blank, it may be used for most components. For
+    example, there are assumption set variables within assumption sets, events and
+    variables within modules that have the category Expenses.
+
+    * Modules- This type of category applies only to modules, initialization modules
+    and layer modules. For example, the modules NP_End_PVFP, NP_Term_PVFP and NP_WoL_PVFP
+    have the modules category PVFP_Module.
+
+    * Data flow- This type of category applies only to data sources and programs.
+    Data from a
+
+    data source will only be processed by programs assigned to the same category as
+    that data source, so
+
+    data flow categories can be used to control the flow of data through a model.
+    For example, in the
+
+    modelEV_Model, the data sources in the data process
+
+    Traditional_Data_Process have the data flow category Traditional and will only
+    pass to the programs
+
+    in the projection processRealistic_Projectionthat have the data flow category
+    Traditional.'
+  - "Find/Replace Panel\n\nSee\n\nchoosers and panels\n\nfor information on\n    displaying\
+    \ the Find/Replace Panel.\n\nThe Find/Replace Panel allows you to search for specific\
+    \ text in the\n\nproperties\n\nof the\n\ncomponents\n\nof the open\n\nworkspaces\n\
+    \nand\n\nresults workspaces\n\n.\n\nYou should enter the text for which you wish\
+    \ to search in the\n\nFind what\n\nedit field.\n\nYou should select the parts\
+    \ of the open workspaces and results workspaces within which you\n    wish to\
+    \ search in the tree under\n\nWithin\n\n.\n\nYou can select multiple items discontinuously\
+    \ by holding down the\n\nCtrl\n\nkey while clicking with the mouse.\n\nYou can\
+    \ use the\n\nName\n\n,\n\nFormula\n\nand\n\nAll\n     fields\n\ncheckboxes to\
+    \ specify whether the search should include the\n\nName\n\nproperty, the\n\nFormula\n\
+    \nproperty or all properties, respectively. You\n    must check at least one of\
+    \ these checkboxes so that there are some properties in which to\n    search.\n\
+    \nYou can also select further search options:\n\n* Match case- check this checkbox\
+    \ to perform a case-sensitive search\n* Match whole- check this checkbox to exclude\
+    \ matches with parts of words, including\n     names of variables and components\n\
+    * Ignore spaces- check this checkbox to ignore all white space in the properties\
+    \ being\n     searched\n* Ignore info fields- check this checkbox to exclude theDescription,Documentation,Last\
+    \ modified,Modified by,Path,Protected byandReserved byproperties.\n\nYou should\
+    \ press the\n\nFind\n\nbutton to start the search.\n\nAfter searching the lower\
+    \ pane will display the number of occurrences of the text that have\n    been\
+    \ found and provide a tree showing where these are. You can double-click on any\
+    \ of the\n    results to open that component in the Central Window, with the found\
+    \ item selected.\n\nYou can select items in the tree if you wish to replace the\
+    \ found text in these items. You\n    should then type the text to replace the\
+    \ found text in the\n\nReplace with\n\nedit field and click the\n\nReplace\n\n\
+    button.\n\nThe read-only icon\n\nnext to a tree\n    item indicates that it has\
+    \ been\n\nprotected\n\nand so none of its\n    text can be replaced using this\
+    \ feature.\n\nYou can drag or copy tree items from the Find/Replace Panel into\
+    \ the\n\nCentral Window\n\n."
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on BAAI/bge-large-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 1024 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': 384, 'do_lower_case': True, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+  (2): Normalize()
+)
+```
+
+## 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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Is there a specific location where I can find workspace filters?',
+    '## Windows\n\nYou can access the filters of a workspace in the grid of filters.\n\nThe filter window has most properties of the filter.',
+    'Find/Replace Panel\n\nSee\n\nchoosers and panels\n\nfor information on\n    displaying the Find/Replace Panel.\n\nThe Find/Replace Panel allows you to search for specific text in the\n\nproperties\n\nof the\n\ncomponents\n\nof the open\n\nworkspaces\n\nand\n\nresults workspaces\n\n.\n\nYou should enter the text for which you wish to search in the\n\nFind what\n\nedit field.\n\nYou should select the parts of the open workspaces and results workspaces within which you\n    wish to search in the tree under\n\nWithin\n\n.\n\nYou can select multiple items discontinuously by holding down the\n\nCtrl\n\nkey while clicking with the mouse.\n\nYou can use the\n\nName\n\n,\n\nFormula\n\nand\n\nAll\n     fields\n\ncheckboxes to specify whether the search should include the\n\nName\n\nproperty, the\n\nFormula\n\nproperty or all properties, respectively. You\n    must check at least one of these checkboxes so that there are some properties in which to\n    search.\n\nYou can also select further search options:\n\n* Match case- check this checkbox to perform a case-sensitive search\n* Match whole- check this checkbox to exclude matches with parts of words, including\n     names of variables and components\n* Ignore spaces- check this checkbox to ignore all white space in the properties being\n     searched\n* Ignore info fields- check this checkbox to exclude theDescription,Documentation,Last modified,Modified by,Path,Protected byandReserved byproperties.\n\nYou should press the\n\nFind\n\nbutton to start the search.\n\nAfter searching the lower pane will display the number of occurrences of the text that have\n    been found and provide a tree showing where these are. You can double-click on any of the\n    results to open that component in the Central Window, with the found item selected.\n\nYou can select items in the tree if you wish to replace the found text in these items. You\n    should then type the text to replace the found text in the\n\nReplace with\n\nedit field and click the\n\nReplace\n\nbutton.\n\nThe read-only icon\n\nnext to a tree\n    item indicates that it has been\n\nprotected\n\nand so none of its\n    text can be replaced using this feature.\n\nYou can drag or copy tree items from the Find/Replace Panel into the\n\nCentral Window\n\n.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000, -0.9967, -0.9964],
+#         [-0.9967,  1.0000,  0.9994],
+#         [-0.9964,  0.9994,  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.*
+-->
+
+<!--
+## 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: 16,909 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                            | negative                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              | string                                                                              |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 18.63 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 188.63 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 150.13 tokens</li><li>max: 384 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                      | positive                                                                                                                                                                                                                                                   | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What is the purpose of the Analyzer tab in a results workspace?</code>                | <code>Analyzer<br><br>The<br><br>Analyzer<br><br>tab of a results workspace shows how the variables in the results workspace depend on each other.<br>If the results workspace contains sample output, the Analyzer shows these calculated results.</code> | <code>Analyzer<br><br>The Analyzer tool for a component shows how variables in the component depend on each other.<br><br>Most components that contain variables with formulas have an<br><br>Analyzer<br><br>tab at the bottom of their component window.<br>The<br><br>Analyzer<br><br>tab gives access to the Analyzer tool.<br>Components with an<br><br>Analyzer<br><br>tab include<br><br>assumption sets<br><br>,<br><br>data views<br><br>,<br><br>database views<br><br>,<br><br>initialization modules<br><br>,<br><br>layer modules<br><br>,<br><br>modules<br><br>,<br><br>MtF views<br><br>,<br><br>programs<br><br>,<br><br>projection processes<br><br>,<br><br>stochastic processes<br><br>,<br>and<br><br>results workspaces<br><br>.<br>The<br><br>Analyzer<br><br>tab of a results workspace<br><br>differs from the<br><br>Analyzer<br><br>tab of the other components and is covered separately.</code>                                                                                                                                                                                               |
+  | <code>What kind of output is displayed in the Analyzer if available?</code>                 | <code>Analyzer<br><br>The<br><br>Analyzer<br><br>tab of a results workspace shows how the variables in the results workspace depend on each other.<br>If the results workspace contains sample output, the Analyzer shows these calculated results.</code> | <code>Accessing output<br><br>You can view and use the output from<br><br>R³S Modeler<br><br>in a variety of different ways.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>Where can I find the dependency relationships between variables in my results?</code> | <code>Analyzer<br><br>The<br><br>Analyzer<br><br>tab of a results workspace shows how the variables in the results workspace depend on each other.<br>If the results workspace contains sample output, the Analyzer shows these calculated results.</code> | <code>Analyzer dependency diagram<br><br>The dependency diagram of the<br><br>Analyzer<br><br>tab of a results workspace shows which variable you are currently analyzing with the variables that it depends on and the variables that depend upon it.<br>You can double-click another variable in the dependency diagram to analyze that variable.<br>The dependency diagram shows the value of each variable if this is available in sample output.<br><br>The dependency diagram is divided into three strips of variables:<br><br>* The top strip shows variables whose value depends on the value of the current variable (its dependants).<br>* The middle strip contains the variable currently being analyzed.<br>* The bottom strip shows variables on which the value of the current variable depends (its precedents).<br><br>Each variable has a box that shows:<br><br>* An icon representing the data type of the variable<br>* A name bar that shows the name of the variable<br>* A value box that shows the value of the variable<br><br>The variable boxes are linked by arrows that show the ...</code> |
+* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
+      "triplet_margin": 5
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `gradient_accumulation_steps`: 2
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.05
+- `bf16`: True
+- `dataloader_num_workers`: 2
+- `remove_unused_columns`: False
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 2
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `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`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 2
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0946 | 50   | 9.7648        |
+| 0.1892 | 100  | 9.3037        |
+| 0.2838 | 150  | 9.1803        |
+| 0.3784 | 200  | 9.2374        |
+| 0.4730 | 250  | 9.1815        |
+| 0.5676 | 300  | 9.2019        |
+| 0.6623 | 350  | 9.2085        |
+| 0.7569 | 400  | 9.0603        |
+| 0.8515 | 450  | 9.1276        |
+| 0.9461 | 500  | 9.1794        |
+| 1.0397 | 550  | 9.0348        |
+| 1.1343 | 600  | 9.1246        |
+| 1.2289 | 650  | 9.1251        |
+| 1.3236 | 700  | 9.1681        |
+| 1.4182 | 750  | 8.907         |
+| 1.5128 | 800  | 9.0067        |
+| 1.6074 | 850  | 9.1056        |
+| 1.7020 | 900  | 9.0715        |
+| 1.7966 | 950  | 8.9425        |
+| 1.8912 | 1000 | 9.0148        |
+| 1.9858 | 1050 | 9.0477        |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 5.1.1
+- Transformers: 4.49.0
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.10.1
+- Datasets: 4.1.1
+- 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",
+}
+```
+
+#### TripletLoss
+```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 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-large-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.49.0",
+    "pytorch": "2.5.1+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "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:a66b161f3952fa42bedb0340befa9c77fc545482a110ceeb0269e1ae1c7786c1
+size 1340612432

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 384,
+    "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}