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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,2430 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:86268
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: market for residues MSWI waste polypropylene Switzerland
+  sentences:
+  - '# heat and power co-generation, biogas, gas engine | electricity, high voltage
+    | Cutoff, U
+
+
+    **UUID:** `4fb000d2-09ce-3406-9f90-a13f60f52c82`
+
+
+    **Reference Flow:** electricity, high voltage
+
+    **Amount:** 3.6
+
+
+    **Classification:** cogeneration
+
+
+    ## Description
+
+
+    This dataset represents the production of electricity and heat from a biogas mix
+    from different sources (biowaste, sewage sludge) when burning it in a cogeneration
+    unit with gas engine. It is intended to represent the production of grid-connected
+    electricity with biogas. The main product is then considered to be electricity
+    at high voltage, while heat is produced as a co-product. The cogeneration unit
+    has a capacity of 160 kWel; the degrees of efficiency are as follows: electricity:
+    0.37 and hea...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2007 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** US-RFC
+
+    Conditions of cogeneration in Switzerland.
+
+
+    ## Technology
+
+
+    Lambda 1-engine" with a catalytic converter.;Electrical efficiency: 0.32;Thermal
+    efficiency: 0.55
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Data Sources
+
+
+    **Sampling:** Literature data
+
+
+    ## Main Inputs
+
+
+    - biogas: 0.3444
+
+
+    ## Main Outputs
+
+
+    - Carbon dioxide, non-fossil: 0.6536
+
+
+    **Version:** 03.11.000'
+  - '# market for residues, MSWI[F], waste polypropylene | residues, MSWI[F], waste
+    polypropylene | Cutoff, U
+
+
+    **UUID:** `659d5ec0-598b-3a3e-a609-3dddc3a09023`
+
+
+    **Reference Flow:** residues, MSWI[F], waste polypropylene
+
+    **Amount:** -1.0
+
+
+    **Classification:** 3821:Treatment and disposal of non-hazardous waste
+
+
+    ## Description
+
+
+    This is a market activity, representing a treatment mix. In the case of products
+    needing treatment, market mixes are supplied by the activities treating the product
+    in the geography defined by the market, and they supply the activities needing
+    to treat the product, as they have generated it as a by-product in the Undefined
+    processes (present as a negative input in system models). Transport to the treating
+    facility or losses are also accounted in this type of markets, when relevant.
+    This is the m...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2010 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** CH
+
+    The disposal mix is country-specific for Switzerland.
+
+
+    ## Technology
+
+
+    The dataset supplies a disposal mix (technology mix) of ''residues, MSWI[F], waste
+    polypropylene'' to the respective waste producing activities.
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - transport, freight, lorry, diesel, unspecified: 0.02577
+
+    - transport, freight, train, fleet average: 0.00356
+
+
+    **Version:** 03.11.000'
+  - '# drying of lentils | drying of lentils | Cutoff, U
+
+
+    **UUID:** `682c8ff7-afc4-3ca1-92d1-77132ce6278d`
+
+
+    **Reference Flow:** drying of lentils
+
+    **Amount:** 0.001
+
+
+    **Classification:** work processes
+
+
+    ## Description
+
+
+    The functional unit is one liter of water evaporated. This module should be used
+    when evaluating drying of lentils from the fresh product to a final product with
+    less moisture content. Only electricity is consumed for this process.;The drying
+    capacity corresponds to the reduction of moisture from 20% to 18%. Drying capacity
+    is assumed to be 80 tonnes of lentils dried per hour.
+
+
+    The activity starts with the reseption of fresh lentils with a moisture content
+    of 20%.
+
+
+    This activity ends with the re...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2019 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** RoW
+
+
+    ## Technology
+
+
+    Data are based on smale scale of drying. Drying is taking place with electric
+    fans. Evaporation capacity is assumed to be 80 tonnes.hour-1 of legume.
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Data Sources
+
+
+    **Sampling:** The dataset is extrapolated from the "drying of maize grain" in
+    Brazil. Province-pecific electricity consumption data originate from primary data
+    collection.
+
+
+    ## Main Inputs
+
+
+    - electricity, low voltage: 0.2627
+
+
+    **Version:** 03.11.000'
+- source_sentence: wood chipping forwarder terrain chipper in forest LCA data
+  sentences:
+  - '# market for heat, central or small-scale, natural gas | heat, central or small-scale,
+    natural gas | Cutoff, U
+
+
+    **UUID:** `317ac404-2790-3750-b328-f954b5d73e5a`
+
+
+    **Reference Flow:** heat, central or small-scale, natural gas
+
+    **Amount:** 1.0
+
+
+    **Classification:** cogeneration
+
+
+    ## Description
+
+
+    This is a market activity. Each market represents the consumption mix of a product
+    in a given geography, connecting suppliers with consumers of the same product
+    in the same geographical area. Markets group the producers and also the imports
+    of the product (if relevant) within the same geographical area. They also account
+    for transport to the consumer and for the losses during that process, when relevant.;This
+    is the market for  ''heat, central or small-scale, natural gas'', in the Global
+    geography...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2011 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** RoW
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - heat, central or small-scale, natural gas: 1
+
+
+    **Version:** 03.11.000'
+  - '# market for wood chipping, forwarder with terrain chipper, in forest | wood
+    chipping, forwarder with terrain chipper, in forest | Cutoff, U
+
+
+    **UUID:** `b2036884-aea0-3154-bb46-53130b0e4285`
+
+
+    **Reference Flow:** wood chipping, forwarder with terrain chipper, in forest
+
+    **Amount:** 3600.0
+
+
+    **Classification:** machinery
+
+
+    ## Description
+
+
+    This is a market activity. Each market represents the consumption mix of a product
+    in a given geography, connecting suppliers with consumers of the same product
+    in the same geographical area. Markets group the producers and also the imports
+    of the product (if relevant) within the same geographical area. They also account
+    for transport to the consumer and for the losses during that process, when relevant.;This
+    is the market for  ''wood chipping, forwarder with terrain chipper, in forest'',
+    in the G...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2012 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** GLO
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - wood chipping, forwarder with terrain chipper, in forest: 3600
+
+
+    **Version:** 03.11.000'
+  - '# market for refrigeration machine, carbon dioxide, liquid as refrigerant | refrigeration
+    machine, carbon dioxide, liquid as refrigerant | Cutoff, U
+
+
+    **UUID:** `f48b87bf-529f-3042-9534-de14752ec005`
+
+
+    **Reference Flow:** refrigeration machine, carbon dioxide, liquid as refrigerant
+
+    **Amount:** 1.0
+
+
+    **Classification:** 2920:Manufacture of bodies (coachwork) for motor vehicles;
+    manufacture of traile
+
+
+    ## Description
+
+
+    This is a market activity. Each market represents the consumption mix of a product
+    in a given geography, connecting suppliers with consumers of the same product
+    in the same geographical area. Markets group the producers and also the imports
+    of the product (if relevant) within the same geographical area. They also account
+    for transport to the consumer and for the losses during that process, when relevant.;This
+    is the market for  ''refrigeration machine, carbon dioxide, liquid as refrigerant'',
+    in t...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2010 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** GLO
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - refrigeration machine, carbon dioxide, liquid as refrigerant: 1
+
+
+    **Version:** 03.11.000'
+- source_sentence: treatment of printer waste mechanical processing LCA database
+  sentences:
+  - '# market for residue from mechanical treatment, laser printer | residue from
+    mechanical treatment, laser printer | Cutoff, U
+
+
+    **UUID:** `90cd88b6-1e53-3abf-b1e1-49e34180e7ca`
+
+
+    **Reference Flow:** residue from mechanical treatment, laser printer
+
+    **Amount:** -1.0
+
+
+    **Classification:** 3821:Treatment and disposal of non-hazardous waste
+
+
+    ## Description
+
+
+    This is a market activity, representing a treatment mix. In the case of products
+    needing treatment, market mixes are supplied by the activities treating the product
+    in the geography defined by the market, and they supply the activities needing
+    to treat the product, as they have generated it as a by-product in the Undefined
+    processes (present as a negative input in system models). Transport to the treating
+    facility or losses are also accounted in this type of markets, when relevant.
+    This is the m...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2010 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** CH
+
+    The disposal mix is country-specific for Switzerland.
+
+
+    ## Technology
+
+
+    The dataset supplies a disposal mix (technology mix) of ''residue from mechanical
+    treatment, laser printer'' to the respective waste producing activities.
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - transport, freight, lorry, diesel, unspecified: 0.02577
+
+    - transport, freight, train, fleet average: 0.00356
+
+
+    **Version:** 03.11.000'
+  - '# market for municipal solid waste | municipal solid waste | Cutoff, U
+
+
+    **UUID:** `eca52ac4-6ea8-34bd-b1bc-4c81cb1ec2e3`
+
+
+    **Reference Flow:** municipal solid waste
+
+    **Amount:** -1.0
+
+
+    **Classification:** 3821:Treatment and disposal of non-hazardous waste
+
+
+    ## Description
+
+
+    This is a market activity, representing a treatment mix. In the case of products
+    needing treatment, market mixes are supplied by the activities treating the product
+    in the geography defined by the market, and they supply the activities needing
+    to treat the product, as they have generated it as a by-product in the Undefined
+    processes (present as a negative input in system models). Transport to the treating
+    facility or losses are also accounted in this type of markets, when relevant.
+    This is the m...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2010 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** TW
+
+    The disposal mix is country-specific for Taiwan, Province of China.
+
+
+    ## Technology
+
+
+    The dataset supplies a disposal mix (technology mix) of ''municipal solid waste''
+    to the respective waste producing activities.
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - transport, freight, lorry, diesel, unspecified: 0.077
+
+
+    **Version:** 03.11.000'
+  - '# grape production, conventional, hill region | grape | Cutoff, U
+
+
+    **UUID:** `d960eb47-84d0-3abe-b2f0-6102c7cc4171`
+
+
+    **Reference Flow:** grape
+
+    **Amount:** 1.0
+
+
+    **Classification:** 0121:Growing of grapes
+
+
+    ## Description
+
+
+    This dataset represents the production of 1 kg of grape calculated on a sectoral
+    average of the production of 1 ha with a yield of 6031 kg at a moisture content
+    of 81.0%. The yield is based on average yield from 2017-2021 in Agristat corrected
+    by region and production system differences in AGRIDEA (2021). Furthermore, the
+    establishing phase of the grape has been taken into account by accounting for
+    3 years of nonproductive phase and reducing the average yield per year accordingly.
+    Modelled crop ...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2017 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** CH
+
+    The inventory is modelled for Switzerland, hill region
+
+
+    ## Technology
+
+
+    Conventional production
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - application of plant protection product, by field sprayer: 26.53
+
+    - Energy, gross calorific value, in biomass: 19.59
+
+    - mulching: 11.61
+
+    - fertilising, by broadcaster: 1.658
+
+    - Occupation, permanent crop: 1.658
+
+    - Carbon dioxide, in air: 0.3066
+
+    - Transformation, from permanent crop: 0.06632
+
+    - trellis system, wooden poles, soft wood, tar impregnated: 0.06632
+
+    - Transformation, to permanent crop: 0.06632
+
+    - fruit tree seedling, for planting: 0.04642
+
+
+    ## Main Outputs
+
+
+    - Nitrate: 0.02173
+
+    - Sulfur: 0.001684
+
+
+    **Version:** 03.11.000'
+- source_sentence: LCA dataset ART cement concrete 40MPa South America
+  sentences:
+  - '# extrusion, plastic film | extrusion, plastic film | Cutoff, U
+
+
+    **UUID:** `89a22932-09a0-308f-94ca-95f355f77385`
+
+
+    **Reference Flow:** extrusion, plastic film
+
+    **Amount:** 1.0
+
+
+    **Classification:** processing
+
+
+    ## Description
+
+
+    1 kg of this process equals 0.976 kg of extruded plastic film.;[This dataset was
+    already contained in the ecoinvent database version 2. It was not individually
+    updated during the transfer to ecoinvent version 3. Life Cycle Impact Assessment
+    results may still have changed, as they are affected by changes in the supply
+    chain, i.e. in other datasets. This dataset was generated following the ecoinvent
+    quality guidelines for version 2. It may have been subject to central changes
+    described in the ecoi...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 1993 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** RER
+
+    information from different European and Swiss converting companies
+
+
+    ## Technology
+
+
+    present technologies
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Data Sources
+
+
+    **Sampling:** company data
+
+
+    ## Main Inputs
+
+
+    - electricity, medium voltage: 2.376
+
+    - heat, district or industrial, natural gas: 0.601
+
+    - heat, district or industrial, other than natural gas: 0.2091
+
+    - steam, in chemical industry: 0.058
+
+    - Water, cooling, unspecified natural origin: 0.0437
+
+    - core board: 0.00732
+
+    - polyethylene, low density, granulate: 0.00215
+
+    - EUR-flat pallet: 0.00144
+
+
+    ## Main Outputs
+
+
+    - Water: 0.0437
+
+
+    **Version:** 03.11.000'
+  - "# concrete production, 40MPa, for civil engineering, with cement, ART | concrete,\
+    \ 40MPa | Cutoff, U\n\n**UUID:** `e33b8446-e1bf-3ca4-8626-2405418fbe5a`\n\n**Reference\
+    \ Flow:** concrete, 40MPa\n**Amount:** 1.0\n\n**Classification:** 2395:Manufacture\
+    \ of articles of concrete, cement and plaster\n\n## Description\n\nThis dataset\
+    \ represents the production of Colombian 40MPa ready-mix concrete for buidling\
+    \ construction and gereral use. Density: 2'374 kg/m³ - w/c: 0.55. Ingredients\
+    \ (for 1 m³): Cement ART Type 330 kg, Water 182 kg, Gravel 1116 kg, Sand 744kg,\
+    \ Admixtures (retarder 0.3%cem and superplastisizers 0.5%cem) in total 2.37 tonne/m3.\n\
+    \nFrom reception of raw materials at the ready-mix plant gate.\n\nThis activity\
+    \ ends before the delivery of concrete at the construction site.\nThe dataset\
+    \ includes the whole m...\n\n## Time Coverage\n\nReference Year: 2014 | Valid\
+    \ Until: 2024\n\n## Geography\n\n**Location:** CO\nRepresentative production of\
+    \ ready-mix concrete 40 MPa with ART type cemen int Colombia. Data based from\
+    \ main cement producers and personal comunications with local experts.\n\n## Technology\n\
+    \nRepresent the current technology for ready-mix concrete production in Colombia.\n\
+    \n## Methodology\n\n**Data Set Type:** Unit process, single operation\n**LCI Method:**\
+    \ Other\n\n## Data Sources\n\n**Sampling:** Production data provides mostly from\
+    \ sustainability reports and general information of 3 principle cement and concrete\
+    \ producers of Colombia (Argos, Cemex and Holcim). Data has been checked, completed\
+    \ and confirmed by personal communication with local experts. \n\nUnit flows were\
+    \ calculated by dividing the amounts of inputs and outputs by the total concrete\
+    \ production in mass.\n\n## Main Inputs\n\n- gravel, crushed: 1149\n- sand: 766.3\n\
+    - cement, ART: 339.9\n- tap water: 248.7\n- electricity, medium voltage: 11.09\n\
+    - chemical, organic: 2.801\n- lubricating oil: 0.0499\n- synthetic rubber: 0.0345\n\
+    - steel, low-alloyed, hot rolled: 0.0238\n\n## Main Outputs\n\n- Water: 0.03708\n\
+    \n**Version:** 03.11.000"
+  - '# machine operation, diesel, >= 18.64 kW and < 74.57 kW, underground mining |
+    machine operation, diesel, >= 18.64 kW and < 74.57 kW, underground mining | Cutoff,
+    U
+
+
+    **UUID:** `4ece3e95-aa7d-32d4-9332-a3c4aa11371d`
+
+
+    **Reference Flow:** machine operation, diesel, >= 18.64 kW and < 74.57 kW, underground
+    mining
+
+    **Amount:** 3600.0
+
+
+    **Classification:** 4312:Site preparation
+
+
+    ## Description
+
+
+    This dataset represents the operation of underground mining machines. The U.S.
+    EPA NONROAD2008 model for off-road machines has been used as basis for this dataset,
+    it refers to engine power in units of horsepower (hp), the engine power range
+    for the dataset is then >= 25 kW and < 100 kW. The fuel consumption and emission
+    factors from the model have been kept in the original units of kg/hp.hour for
+    transparency reasons. However the engine power parameter used as input data for
+    the dataset is quan...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2014 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** GLO
+
+    Fuel flow and most emission flows are extrapolated from US conditions, some emission
+    flows and the other intermediate product flows (building machine, lubricating
+    oil) are extrapolated from RER and CH conditions. The uncertainty has been adjusted
+    accordingly.
+
+
+    ## Technology
+
+
+    Average current technology for one typical machine representing the category.
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Data Sources
+
+
+    **Sampling:** Most data from the U.S. EPA NONROAD2008 and NMIM models. Some data
+    from the LCA study Oekoinventare von Energiesystemen 1996 and emissions from the
+    Swiss "Offroad" database.
+
+
+    ## Main Inputs
+
+
+    - Oxygen: 8.566
+
+    - diesel: 3.685
+
+    - lubricating oil: 0.08106
+
+
+    ## Main Outputs
+
+
+    - Carbon dioxide, fossil: 11.61
+
+    - Carbon monoxide, fossil: 0.1028
+
+    - Nitrogen oxides: 0.08772
+
+    - NMVOC, non-methane volatile organic compounds: 0.01139
+
+    - Particulate Matter, < 2.5 um: 0.007783
+
+    - Formaldehyde: 0.00177
+
+
+    **Version:** 03.11.000'
+- source_sentence: high to medium voltage electricity loss factor IEA 2020
+  sentences:
+  - '# market for transport, freight, lorry with refrigeration machine, 3.5-7.5 ton,
+    diesel, EURO 3, CO2, liq. ref., freezing | transport, freight, lorry with refrigeration
+    machine, 3.5-7.5 ton, diesel, EURO 3, CO2, liq. ref., freezing | Cutoff, U
+
+
+    **UUID:** `f0162115-6ead-32f3-a01c-4921f273a9a0`
+
+
+    **Reference Flow:** transport, freight, lorry with refrigeration machine, 3.5-7.5
+    ton, diesel, EURO 3, CO2, liq. ref., freezing
+
+    **Amount:** 1.0
+
+
+    **Classification:** 4923:Freight transport by road
+
+
+    ## Description
+
+
+    This is a market activity. Each market represents the consumption mix of a product
+    in a given geography, connecting suppliers with consumers of the same product
+    in the same geographical area. Markets group the producers and also the imports
+    of the product (if relevant) within the same geographical area. They also account
+    for transport to the consumer and for the losses during that process, when relevant.;This
+    is the market for  ''transport, freight, lorry with refrigeration machine, 3.5-7.5
+    ton, ...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2010 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** GLO
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - transport, freight, lorry with refrigeration machine, 3.5-7.5 ton, diesel, EURO
+    3, CO2, liq. ref., freezing: 1
+
+
+    **Version:** 03.11.000'
+  - '# magnesium-alloy production, AZ91, diecasting | magnesium-alloy, AZ91, diecast
+    | Cutoff, U
+
+
+    **UUID:** `7a58ff3a-2802-3201-befd-948993f5862b`
+
+
+    **Reference Flow:** magnesium-alloy, AZ91, diecast
+
+    **Amount:** 1.0
+
+
+    **Classification:** 2420:Manufacture of basic precious and other non-ferrous metals
+
+
+    ## Description
+
+
+    Large uncertainties exist for data on energy use and SF6 emissions during magnesium
+    diecasting. This inventory describes an assumption for the average production.;[This
+    dataset was already contained in the ecoinvent database version 2. It was not
+    individually updated during the transfer to ecoinvent version 3. Life Cycle Impact
+    Assessment results may still have changed, as they are affected by changes in
+    the supply chain, i.e. in other datasets. This dataset was generated following
+    the ecoinvent...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 1998 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** RER
+
+    Data for 1 producer
+
+
+    ## Technology
+
+
+    Diecasting of AZ91, no recycling
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Data Sources
+
+
+    **Sampling:** Environmental data from an important producer
+
+
+    ## Main Inputs
+
+
+    - electricity, medium voltage: 7.992
+
+    - magnesium-alloy, AZ91: 1.67
+
+    - sulfur hexafluoride, liquid: 0.001
+
+
+    ## Main Outputs
+
+
+    - Sulfur hexafluoride: 0.001
+
+
+    **Version:** 03.11.000'
+  - '# electricity voltage transformation from high to medium voltage | electricity,
+    medium voltage | Cutoff, U
+
+
+    **UUID:** `7ea0a1a9-881c-301b-8520-3c13205bd721`
+
+
+    **Reference Flow:** electricity, medium voltage
+
+    **Amount:** 3.6
+
+
+    **Classification:** 3510:Electric power generation, transmission and distribution
+
+
+    ## Description
+
+
+    This dataset represents the transformation of electricity voltage from high to
+    medium voltage. The losses are based on electricity generation and trade statistics:
+    IEA World Energy Statistics and Balances, ISSN: 16834240, https://doi.org/10.1787/enestats-data-en
+    Definition of the voltage levels: - High voltage level above 24 kV (large scale
+    industry) - Medium voltage level between 1 kV and 24 kV (medium to small scale
+    industry, service sector and public buildings) - Low voltage level below 1 kV
+    ...
+
+
+    ## Time Coverage
+
+
+    Reference Year: 2020 | Valid Until: 2024
+
+
+    ## Geography
+
+
+    **Location:** AM
+
+
+    ## Technology
+
+
+    Transformers are essential components of the electricity grid. They connect the
+    different voltage levels.;Big power plants feed the electricity at a very high
+    voltage to the electricity grid. Transformers between 600 MVA and 80 MVA can be
+    found in substations, where the high transmission voltage is transformed to the
+    medium distribution voltage. In an additional step the medium voltage is transformed
+    to low voltage using transformers below 2.5 MVA. The small transformers below
+    2.5 MVA are allocated to the low voltage level, whereas all the other transformers
+    are allocated to the medium voltage level.
+
+
+    ## Methodology
+
+
+    **Data Set Type:** Unit process, single operation
+
+    **LCI Method:** Other
+
+
+    ## Main Inputs
+
+
+    - electricity, high voltage: 3.627
+
+
+    **Version:** 03.11.000'
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 1024 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/huggingface/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': 1024, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, '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': True, '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
+queries = [
+    "high to medium voltage electricity loss factor IEA 2020",
+]
+documents = [
+    '# electricity voltage transformation from high to medium voltage | electricity, medium voltage | Cutoff, U\n\n**UUID:** `7ea0a1a9-881c-301b-8520-3c13205bd721`\n\n**Reference Flow:** electricity, medium voltage\n**Amount:** 3.6\n\n**Classification:** 3510:Electric power generation, transmission and distribution\n\n## Description\n\nThis dataset represents the transformation of electricity voltage from high to medium voltage. The losses are based on electricity generation and trade statistics: IEA World Energy Statistics and Balances, ISSN: 16834240, https://doi.org/10.1787/enestats-data-en Definition of the voltage levels: - High voltage level above 24 kV (large scale industry) - Medium voltage level between 1 kV and 24 kV (medium to small scale industry, service sector and public buildings) - Low voltage level below 1 kV ...\n\n## Time Coverage\n\nReference Year: 2020 | Valid Until: 2024\n\n## Geography\n\n**Location:** AM\n\n## Technology\n\nTransformers are essential components of the electricity grid. They connect the different voltage levels.;Big power plants feed the electricity at a very high voltage to the electricity grid. Transformers between 600 MVA and 80 MVA can be found in substations, where the high transmission voltage is transformed to the medium distribution voltage. In an additional step the medium voltage is transformed to low voltage using transformers below 2.5 MVA. The small transformers below 2.5 MVA are allocated to the low voltage level, whereas all the other transformers are allocated to the medium voltage level.\n\n## Methodology\n\n**Data Set Type:** Unit process, single operation\n**LCI Method:** Other\n\n## Main Inputs\n\n- electricity, high voltage: 3.627\n\n**Version:** 03.11.000',
+    "# market for transport, freight, lorry with refrigeration machine, 3.5-7.5 ton, diesel, EURO 3, CO2, liq. ref., freezing | transport, freight, lorry with refrigeration machine, 3.5-7.5 ton, diesel, EURO 3, CO2, liq. ref., freezing | Cutoff, U\n\n**UUID:** `f0162115-6ead-32f3-a01c-4921f273a9a0`\n\n**Reference Flow:** transport, freight, lorry with refrigeration machine, 3.5-7.5 ton, diesel, EURO 3, CO2, liq. ref., freezing\n**Amount:** 1.0\n\n**Classification:** 4923:Freight transport by road\n\n## Description\n\nThis is a market activity. Each market represents the consumption mix of a product in a given geography, connecting suppliers with consumers of the same product in the same geographical area. Markets group the producers and also the imports of the product (if relevant) within the same geographical area. They also account for transport to the consumer and for the losses during that process, when relevant.;This is the market for  'transport, freight, lorry with refrigeration machine, 3.5-7.5 ton, ...\n\n## Time Coverage\n\nReference Year: 2010 | Valid Until: 2024\n\n## Geography\n\n**Location:** GLO\n\n## Methodology\n\n**Data Set Type:** Unit process, single operation\n**LCI Method:** Other\n\n## Main Inputs\n\n- transport, freight, lorry with refrigeration machine, 3.5-7.5 ton, diesel, EURO 3, CO2, liq. ref., freezing: 1\n\n**Version:** 03.11.000",
+    '# magnesium-alloy production, AZ91, diecasting | magnesium-alloy, AZ91, diecast | Cutoff, U\n\n**UUID:** `7a58ff3a-2802-3201-befd-948993f5862b`\n\n**Reference Flow:** magnesium-alloy, AZ91, diecast\n**Amount:** 1.0\n\n**Classification:** 2420:Manufacture of basic precious and other non-ferrous metals\n\n## Description\n\nLarge uncertainties exist for data on energy use and SF6 emissions during magnesium diecasting. This inventory describes an assumption for the average production.;[This dataset was already contained in the ecoinvent database version 2. It was not individually updated during the transfer to ecoinvent version 3. Life Cycle Impact Assessment results may still have changed, as they are affected by changes in the supply chain, i.e. in other datasets. This dataset was generated following the ecoinvent...\n\n## Time Coverage\n\nReference Year: 1998 | Valid Until: 2024\n\n## Geography\n\n**Location:** RER\nData for 1 producer\n\n## Technology\n\nDiecasting of AZ91, no recycling\n\n## Methodology\n\n**Data Set Type:** Unit process, single operation\n**LCI Method:** Other\n\n## Data Sources\n\n**Sampling:** Environmental data from an important producer\n\n## Main Inputs\n\n- electricity, medium voltage: 7.992\n- magnesium-alloy, AZ91: 1.67\n- sulfur hexafluoride, liquid: 0.001\n\n## Main Outputs\n\n- Sulfur hexafluoride: 0.001\n\n**Version:** 03.11.000',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 1024] [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.5645, -0.0200,  0.0264]])
+```
+
+<!--
+### 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
+
+#### Unnamed Dataset
+
+* Size: 86,268 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                            | positive                                                                               |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                                 |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 12.72 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 135 tokens</li><li>mean: 487.69 tokens</li><li>max: 1024 tokens</li></ul> |
+* Samples:
+  | anchor                                                             | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  |:-------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>sewage sludge treatment LCA data 97% water</code>            | <code># market for sewage sludge, 97% water, WWT-SLF, waste tin sheet \| sewage sludge, 97% water, WWT-SLF, waste tin sheet \| Cutoff, U<br><br>**UUID:** `558102aa-45b5-3b05-a8d8-a87cb2afe51d`<br><br>**Reference Flow:** sewage sludge, 97% water, WWT-SLF, waste tin sheet<br>**Amount:** -1.0<br><br>**Classification:** 3821:Treatment and disposal of non-hazardous waste<br><br>## Description<br><br>This is a market activity, representing a treatment mix. In the case of products needing treatment, market mixes are supplied by the activities treating the product in the geography defined by the market, and they supply the activities needing to treat the product, as they have generated it as a by-product in the Undefined processes (present as a negative input in system models). Transport to the treating facility or losses are also accounted in this type of markets, when relevant. This is the m...<br><br>## Time Coverage<br><br>Reference Year: 2010 \| Valid Until: 2024<br><br>## Geography<br><br>**Location:** GLO<br>The disposal mix is country-specific for Globa...</code>                         |
+  | <code>market for waste paperboard treatment mix India 2018</code>  | <code># market for waste paperboard \| waste paperboard \| Cutoff, U<br><br>**UUID:** `d924c6f6-85f7-3d7c-aad9-5b038d69d492`<br><br>**Reference Flow:** waste paperboard<br>**Amount:** -1.0<br><br>**Classification:** 3821:Treatment and disposal of non-hazardous waste<br><br>## Description<br><br>This is a market activity, representing a treatment mix. In the case of products needing treatment, market mixes are supplied by the activities treating the product in the geography defined by the market, and they supply the activities needing to treat the product, as they have generated it as a by-product in the Undefined processes (present as a negative input in system models). Transport to the treating facility or losses are also accounted in this type of markets, when relevant.;This market d...<br><br>## Time Coverage<br><br>Reference Year: 2018 \| Valid Until: 2024<br><br>## Geography<br><br>**Location:** IN<br>The disposal mix is country-specific for India.<br><br>## Technology<br><br>The dataset supplies a disposal mix (technology mix) of waste paperboard  to the resp...</code>             |
+  | <code>pellet boiler production emissions CO2 Central Europe</code> | <code># furnace production, pellets, with silo, 300kW \| furnace, pellets, with silo, 300kW \| Cutoff, U<br><br>**UUID:** `4a13b02f-8889-3ce6-b081-fd4e93d86745`<br><br>**Reference Flow:** furnace, pellets, with silo, 300kW<br>**Amount:** 1.0<br><br>**Classification:** 2815:Manufacture of ovens, furnaces and furnace burners<br><br>## Description<br><br>This dataset represent the production and the disposal of a wood-pellets burning furnace for local heating networks, with a storage silo. Its assumed lifetime is 20 years.<br><br>From reception of furnace components at the factory gate<br><br>This activity ends with the disposal of the device in the end of its lifetime. The dataset includes the most important materials used for the fabrication of the furnace, the pellets storage silo, the automatic screw conveyor system and chimney flue pipes: steel, steel ...<br><br>## Time Coverage<br><br>Reference Year: 2010 \| Valid Until: 2024<br><br>## Geography<br><br>**Location:** CH<br>Could be used for Central European conditions<br><br>## Technology<br><br>Boiler of modern technology a...</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### 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`: 8
+- `per_device_eval_batch_size`: 8
+- `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`: 1e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 1
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `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}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0002 | 1     | 0.1742        |
+| 0.0019 | 10    | 0.1517        |
+| 0.0037 | 20    | 0.0885        |
+| 0.0056 | 30    | 0.0962        |
+| 0.0074 | 40    | 0.1077        |
+| 0.0093 | 50    | 0.0848        |
+| 0.0111 | 60    | 0.151         |
+| 0.0130 | 70    | 0.1112        |
+| 0.0148 | 80    | 0.1011        |
+| 0.0167 | 90    | 0.116         |
+| 0.0185 | 100   | 0.1088        |
+| 0.0204 | 110   | 0.1053        |
+| 0.0223 | 120   | 0.135         |
+| 0.0241 | 130   | 0.0784        |
+| 0.0260 | 140   | 0.1017        |
+| 0.0278 | 150   | 0.0769        |
+| 0.0297 | 160   | 0.0984        |
+| 0.0315 | 170   | 0.0978        |
+| 0.0334 | 180   | 0.0537        |
+| 0.0352 | 190   | 0.1063        |
+| 0.0371 | 200   | 0.0727        |
+| 0.0390 | 210   | 0.059         |
+| 0.0408 | 220   | 0.0703        |
+| 0.0427 | 230   | 0.0818        |
+| 0.0445 | 240   | 0.0565        |
+| 0.0464 | 250   | 0.0425        |
+| 0.0482 | 260   | 0.0442        |
+| 0.0501 | 270   | 0.0357        |
+| 0.0519 | 280   | 0.031         |
+| 0.0538 | 290   | 0.0455        |
+| 0.0556 | 300   | 0.0381        |
+| 0.0575 | 310   | 0.02          |
+| 0.0594 | 320   | 0.0451        |
+| 0.0612 | 330   | 0.0329        |
+| 0.0631 | 340   | 0.0253        |
+| 0.0649 | 350   | 0.064         |
+| 0.0668 | 360   | 0.0273        |
+| 0.0686 | 370   | 0.0159        |
+| 0.0705 | 380   | 0.0316        |
+| 0.0723 | 390   | 0.0202        |
+| 0.0742 | 400   | 0.0231        |
+| 0.0761 | 410   | 0.0481        |
+| 0.0779 | 420   | 0.0259        |
+| 0.0798 | 430   | 0.0313        |
+| 0.0816 | 440   | 0.0277        |
+| 0.0835 | 450   | 0.0306        |
+| 0.0853 | 460   | 0.0153        |
+| 0.0872 | 470   | 0.0125        |
+| 0.0890 | 480   | 0.025         |
+| 0.0909 | 490   | 0.0192        |
+| 0.0927 | 500   | 0.0191        |
+| 0.0946 | 510   | 0.0065        |
+| 0.0965 | 520   | 0.0379        |
+| 0.0983 | 530   | 0.0431        |
+| 0.1002 | 540   | 0.0202        |
+| 0.1020 | 550   | 0.0172        |
+| 0.1039 | 560   | 0.0136        |
+| 0.1057 | 570   | 0.084         |
+| 0.1076 | 580   | 0.0362        |
+| 0.1094 | 590   | 0.0232        |
+| 0.1113 | 600   | 0.0412        |
+| 0.1132 | 610   | 0.0312        |
+| 0.1150 | 620   | 0.0292        |
+| 0.1169 | 630   | 0.0177        |
+| 0.1187 | 640   | 0.0234        |
+| 0.1206 | 650   | 0.0075        |
+| 0.1224 | 660   | 0.0116        |
+| 0.1243 | 670   | 0.0163        |
+| 0.1261 | 680   | 0.0457        |
+| 0.1280 | 690   | 0.0088        |
+| 0.1298 | 700   | 0.0237        |
+| 0.1317 | 710   | 0.0274        |
+| 0.1336 | 720   | 0.0182        |
+| 0.1354 | 730   | 0.0152        |
+| 0.1373 | 740   | 0.0093        |
+| 0.1391 | 750   | 0.0088        |
+| 0.1410 | 760   | 0.0079        |
+| 0.1428 | 770   | 0.0167        |
+| 0.1447 | 780   | 0.0186        |
+| 0.1465 | 790   | 0.0122        |
+| 0.1484 | 800   | 0.0259        |
+| 0.1503 | 810   | 0.0607        |
+| 0.1521 | 820   | 0.0124        |
+| 0.1540 | 830   | 0.0125        |
+| 0.1558 | 840   | 0.009         |
+| 0.1577 | 850   | 0.0102        |
+| 0.1595 | 860   | 0.0224        |
+| 0.1614 | 870   | 0.0156        |
+| 0.1632 | 880   | 0.0296        |
+| 0.1651 | 890   | 0.0253        |
+| 0.1669 | 900   | 0.0283        |
+| 0.1688 | 910   | 0.0128        |
+| 0.1707 | 920   | 0.0128        |
+| 0.1725 | 930   | 0.0045        |
+| 0.1744 | 940   | 0.0312        |
+| 0.1762 | 950   | 0.0019        |
+| 0.1781 | 960   | 0.0164        |
+| 0.1799 | 970   | 0.0156        |
+| 0.1818 | 980   | 0.019         |
+| 0.1836 | 990   | 0.0069        |
+| 0.1855 | 1000  | 0.0172        |
+| 0.1873 | 1010  | 0.0027        |
+| 0.1892 | 1020  | 0.0158        |
+| 0.1911 | 1030  | 0.0086        |
+| 0.1929 | 1040  | 0.0162        |
+| 0.1948 | 1050  | 0.0067        |
+| 0.1966 | 1060  | 0.0063        |
+| 0.1985 | 1070  | 0.008         |
+| 0.2003 | 1080  | 0.0206        |
+| 0.2022 | 1090  | 0.034         |
+| 0.2040 | 1100  | 0.0262        |
+| 0.2059 | 1110  | 0.0192        |
+| 0.2078 | 1120  | 0.0042        |
+| 0.2096 | 1130  | 0.0218        |
+| 0.2115 | 1140  | 0.0185        |
+| 0.2133 | 1150  | 0.0318        |
+| 0.2152 | 1160  | 0.0049        |
+| 0.2170 | 1170  | 0.0056        |
+| 0.2189 | 1180  | 0.0068        |
+| 0.2207 | 1190  | 0.0099        |
+| 0.2226 | 1200  | 0.0079        |
+| 0.2244 | 1210  | 0.0154        |
+| 0.2263 | 1220  | 0.0159        |
+| 0.2282 | 1230  | 0.0033        |
+| 0.2300 | 1240  | 0.0288        |
+| 0.2319 | 1250  | 0.0056        |
+| 0.2337 | 1260  | 0.0127        |
+| 0.2356 | 1270  | 0.0073        |
+| 0.2374 | 1280  | 0.0301        |
+| 0.2393 | 1290  | 0.0334        |
+| 0.2411 | 1300  | 0.0076        |
+| 0.2430 | 1310  | 0.024         |
+| 0.2449 | 1320  | 0.0092        |
+| 0.2467 | 1330  | 0.022         |
+| 0.2486 | 1340  | 0.0219        |
+| 0.2504 | 1350  | 0.021         |
+| 0.2523 | 1360  | 0.0317        |
+| 0.2541 | 1370  | 0.0091        |
+| 0.2560 | 1380  | 0.0136        |
+| 0.2578 | 1390  | 0.007         |
+| 0.2597 | 1400  | 0.0027        |
+| 0.2615 | 1410  | 0.0192        |
+| 0.2634 | 1420  | 0.0004        |
+| 0.2653 | 1430  | 0.0075        |
+| 0.2671 | 1440  | 0.01          |
+| 0.2690 | 1450  | 0.0039        |
+| 0.2708 | 1460  | 0.0136        |
+| 0.2727 | 1470  | 0.012         |
+| 0.2745 | 1480  | 0.0152        |
+| 0.2764 | 1490  | 0.0054        |
+| 0.2782 | 1500  | 0.0087        |
+| 0.2801 | 1510  | 0.0193        |
+| 0.2820 | 1520  | 0.0192        |
+| 0.2838 | 1530  | 0.0035        |
+| 0.2857 | 1540  | 0.0173        |
+| 0.2875 | 1550  | 0.0082        |
+| 0.2894 | 1560  | 0.0127        |
+| 0.2912 | 1570  | 0.0025        |
+| 0.2931 | 1580  | 0.0111        |
+| 0.2949 | 1590  | 0.0227        |
+| 0.2968 | 1600  | 0.0125        |
+| 0.2986 | 1610  | 0.0054        |
+| 0.3005 | 1620  | 0.0073        |
+| 0.3024 | 1630  | 0.0051        |
+| 0.3042 | 1640  | 0.0138        |
+| 0.3061 | 1650  | 0.0108        |
+| 0.3079 | 1660  | 0.0321        |
+| 0.3098 | 1670  | 0.0119        |
+| 0.3116 | 1680  | 0.0181        |
+| 0.3135 | 1690  | 0.0084        |
+| 0.3153 | 1700  | 0.0131        |
+| 0.3172 | 1710  | 0.0039        |
+| 0.3191 | 1720  | 0.0114        |
+| 0.3209 | 1730  | 0.0082        |
+| 0.3228 | 1740  | 0.0144        |
+| 0.3246 | 1750  | 0.0052        |
+| 0.3265 | 1760  | 0.008         |
+| 0.3283 | 1770  | 0.0069        |
+| 0.3302 | 1780  | 0.0018        |
+| 0.3320 | 1790  | 0.0149        |
+| 0.3339 | 1800  | 0.0152        |
+| 0.3357 | 1810  | 0.0295        |
+| 0.3376 | 1820  | 0.0115        |
+| 0.3395 | 1830  | 0.0007        |
+| 0.3413 | 1840  | 0.0219        |
+| 0.3432 | 1850  | 0.002         |
+| 0.3450 | 1860  | 0.0021        |
+| 0.3469 | 1870  | 0.0255        |
+| 0.3487 | 1880  | 0.0284        |
+| 0.3506 | 1890  | 0.0191        |
+| 0.3524 | 1900  | 0.0092        |
+| 0.3543 | 1910  | 0.0068        |
+| 0.3561 | 1920  | 0.0112        |
+| 0.3580 | 1930  | 0.0107        |
+| 0.3599 | 1940  | 0.0068        |
+| 0.3617 | 1950  | 0.0052        |
+| 0.3636 | 1960  | 0.0093        |
+| 0.3654 | 1970  | 0.0276        |
+| 0.3673 | 1980  | 0.0116        |
+| 0.3691 | 1990  | 0.0271        |
+| 0.3710 | 2000  | 0.0045        |
+| 0.3728 | 2010  | 0.0023        |
+| 0.3747 | 2020  | 0.0121        |
+| 0.3766 | 2030  | 0.028         |
+| 0.3784 | 2040  | 0.0218        |
+| 0.3803 | 2050  | 0.0236        |
+| 0.3821 | 2060  | 0.0153        |
+| 0.3840 | 2070  | 0.003         |
+| 0.3858 | 2080  | 0.0268        |
+| 0.3877 | 2090  | 0.0038        |
+| 0.3895 | 2100  | 0.0017        |
+| 0.3914 | 2110  | 0.0046        |
+| 0.3932 | 2120  | 0.0087        |
+| 0.3951 | 2130  | 0.0193        |
+| 0.3970 | 2140  | 0.0414        |
+| 0.3988 | 2150  | 0.0145        |
+| 0.4007 | 2160  | 0.0367        |
+| 0.4025 | 2170  | 0.0383        |
+| 0.4044 | 2180  | 0.0023        |
+| 0.4062 | 2190  | 0.0013        |
+| 0.4081 | 2200  | 0.0114        |
+| 0.4099 | 2210  | 0.0187        |
+| 0.4118 | 2220  | 0.0143        |
+| 0.4137 | 2230  | 0.0077        |
+| 0.4155 | 2240  | 0.002         |
+| 0.4174 | 2250  | 0.0115        |
+| 0.4192 | 2260  | 0.0085        |
+| 0.4211 | 2270  | 0.0037        |
+| 0.4229 | 2280  | 0.0021        |
+| 0.4248 | 2290  | 0.0076        |
+| 0.4266 | 2300  | 0.0109        |
+| 0.4285 | 2310  | 0.0062        |
+| 0.4303 | 2320  | 0.0055        |
+| 0.4322 | 2330  | 0.0091        |
+| 0.4341 | 2340  | 0.0149        |
+| 0.4359 | 2350  | 0.0048        |
+| 0.4378 | 2360  | 0.0222        |
+| 0.4396 | 2370  | 0.0156        |
+| 0.4415 | 2380  | 0.0164        |
+| 0.4433 | 2390  | 0.0055        |
+| 0.4452 | 2400  | 0.0062        |
+| 0.4470 | 2410  | 0.0246        |
+| 0.4489 | 2420  | 0.0022        |
+| 0.4508 | 2430  | 0.0172        |
+| 0.4526 | 2440  | 0.0125        |
+| 0.4545 | 2450  | 0.0201        |
+| 0.4563 | 2460  | 0.0047        |
+| 0.4582 | 2470  | 0.0104        |
+| 0.4600 | 2480  | 0.0235        |
+| 0.4619 | 2490  | 0.0041        |
+| 0.4637 | 2500  | 0.023         |
+| 0.4656 | 2510  | 0.0069        |
+| 0.4674 | 2520  | 0.0035        |
+| 0.4693 | 2530  | 0.0113        |
+| 0.4712 | 2540  | 0.0017        |
+| 0.4730 | 2550  | 0.0019        |
+| 0.4749 | 2560  | 0.0174        |
+| 0.4767 | 2570  | 0.003         |
+| 0.4786 | 2580  | 0.0031        |
+| 0.4804 | 2590  | 0.0065        |
+| 0.4823 | 2600  | 0.0144        |
+| 0.4841 | 2610  | 0.0059        |
+| 0.4860 | 2620  | 0.0162        |
+| 0.4879 | 2630  | 0.0072        |
+| 0.4897 | 2640  | 0.0043        |
+| 0.4916 | 2650  | 0.0008        |
+| 0.4934 | 2660  | 0.0251        |
+| 0.4953 | 2670  | 0.0098        |
+| 0.4971 | 2680  | 0.0049        |
+| 0.4990 | 2690  | 0.0043        |
+| 0.5008 | 2700  | 0.0035        |
+| 0.5027 | 2710  | 0.0295        |
+| 0.5045 | 2720  | 0.0049        |
+| 0.5064 | 2730  | 0.0429        |
+| 0.5083 | 2740  | 0.0032        |
+| 0.5101 | 2750  | 0.0125        |
+| 0.5120 | 2760  | 0.0113        |
+| 0.5138 | 2770  | 0.013         |
+| 0.5157 | 2780  | 0.0285        |
+| 0.5175 | 2790  | 0.0193        |
+| 0.5194 | 2800  | 0.0168        |
+| 0.5212 | 2810  | 0.0154        |
+| 0.5231 | 2820  | 0.007         |
+| 0.5249 | 2830  | 0.0161        |
+| 0.5268 | 2840  | 0.0027        |
+| 0.5287 | 2850  | 0.0067        |
+| 0.5305 | 2860  | 0.0097        |
+| 0.5324 | 2870  | 0.0081        |
+| 0.5342 | 2880  | 0.0106        |
+| 0.5361 | 2890  | 0.0064        |
+| 0.5379 | 2900  | 0.0195        |
+| 0.5398 | 2910  | 0.0179        |
+| 0.5416 | 2920  | 0.0193        |
+| 0.5435 | 2930  | 0.0128        |
+| 0.5454 | 2940  | 0.0232        |
+| 0.5472 | 2950  | 0.0107        |
+| 0.5491 | 2960  | 0.0088        |
+| 0.5509 | 2970  | 0.0115        |
+| 0.5528 | 2980  | 0.014         |
+| 0.5546 | 2990  | 0.0111        |
+| 0.5565 | 3000  | 0.0138        |
+| 0.5583 | 3010  | 0.0093        |
+| 0.5602 | 3020  | 0.0016        |
+| 0.5620 | 3030  | 0.0169        |
+| 0.5639 | 3040  | 0.0032        |
+| 0.5658 | 3050  | 0.0014        |
+| 0.5676 | 3060  | 0.01          |
+| 0.5695 | 3070  | 0.0095        |
+| 0.5713 | 3080  | 0.0158        |
+| 0.5732 | 3090  | 0.002         |
+| 0.5750 | 3100  | 0.0077        |
+| 0.5769 | 3110  | 0.0153        |
+| 0.5787 | 3120  | 0.005         |
+| 0.5806 | 3130  | 0.0046        |
+| 0.5825 | 3140  | 0.0118        |
+| 0.5843 | 3150  | 0.0008        |
+| 0.5862 | 3160  | 0.0052        |
+| 0.5880 | 3170  | 0.017         |
+| 0.5899 | 3180  | 0.0179        |
+| 0.5917 | 3190  | 0.0061        |
+| 0.5936 | 3200  | 0.0143        |
+| 0.5954 | 3210  | 0.0111        |
+| 0.5973 | 3220  | 0.0154        |
+| 0.5991 | 3230  | 0.0151        |
+| 0.6010 | 3240  | 0.017         |
+| 0.6029 | 3250  | 0.0084        |
+| 0.6047 | 3260  | 0.0026        |
+| 0.6066 | 3270  | 0.0019        |
+| 0.6084 | 3280  | 0.0143        |
+| 0.6103 | 3290  | 0.0125        |
+| 0.6121 | 3300  | 0.0486        |
+| 0.6140 | 3310  | 0.0143        |
+| 0.6158 | 3320  | 0.0018        |
+| 0.6177 | 3330  | 0.0056        |
+| 0.6196 | 3340  | 0.0064        |
+| 0.6214 | 3350  | 0.0121        |
+| 0.6233 | 3360  | 0.0085        |
+| 0.6251 | 3370  | 0.0155        |
+| 0.6270 | 3380  | 0.0143        |
+| 0.6288 | 3390  | 0.0126        |
+| 0.6307 | 3400  | 0.0067        |
+| 0.6325 | 3410  | 0.0079        |
+| 0.6344 | 3420  | 0.0075        |
+| 0.6362 | 3430  | 0.007         |
+| 0.6381 | 3440  | 0.0173        |
+| 0.6400 | 3450  | 0.0168        |
+| 0.6418 | 3460  | 0.0069        |
+| 0.6437 | 3470  | 0.0013        |
+| 0.6455 | 3480  | 0.0147        |
+| 0.6474 | 3490  | 0.0024        |
+| 0.6492 | 3500  | 0.0056        |
+| 0.6511 | 3510  | 0.032         |
+| 0.6529 | 3520  | 0.0082        |
+| 0.6548 | 3530  | 0.01          |
+| 0.6566 | 3540  | 0.061         |
+| 0.6585 | 3550  | 0.0143        |
+| 0.6604 | 3560  | 0.0173        |
+| 0.6622 | 3570  | 0.008         |
+| 0.6641 | 3580  | 0.0037        |
+| 0.6659 | 3590  | 0.0066        |
+| 0.6678 | 3600  | 0.0282        |
+| 0.6696 | 3610  | 0.0546        |
+| 0.6715 | 3620  | 0.0009        |
+| 0.6733 | 3630  | 0.0051        |
+| 0.6752 | 3640  | 0.004         |
+| 0.6771 | 3650  | 0.0009        |
+| 0.6789 | 3660  | 0.0081        |
+| 0.6808 | 3670  | 0.0139        |
+| 0.6826 | 3680  | 0.0134        |
+| 0.6845 | 3690  | 0.008         |
+| 0.6863 | 3700  | 0.0035        |
+| 0.6882 | 3710  | 0.0184        |
+| 0.6900 | 3720  | 0.0019        |
+| 0.6919 | 3730  | 0.028         |
+| 0.6937 | 3740  | 0.0109        |
+| 0.6956 | 3750  | 0.0036        |
+| 0.6975 | 3760  | 0.008         |
+| 0.6993 | 3770  | 0.0017        |
+| 0.7012 | 3780  | 0.0142        |
+| 0.7030 | 3790  | 0.0014        |
+| 0.7049 | 3800  | 0.01          |
+| 0.7067 | 3810  | 0.0008        |
+| 0.7086 | 3820  | 0.0263        |
+| 0.7104 | 3830  | 0.0051        |
+| 0.7123 | 3840  | 0.0083        |
+| 0.7142 | 3850  | 0.0101        |
+| 0.7160 | 3860  | 0.0062        |
+| 0.7179 | 3870  | 0.0043        |
+| 0.7197 | 3880  | 0.0072        |
+| 0.7216 | 3890  | 0.005         |
+| 0.7234 | 3900  | 0.0012        |
+| 0.7253 | 3910  | 0.0154        |
+| 0.7271 | 3920  | 0.0121        |
+| 0.7290 | 3930  | 0.0111        |
+| 0.7308 | 3940  | 0.0123        |
+| 0.7327 | 3950  | 0.0074        |
+| 0.7346 | 3960  | 0.0219        |
+| 0.7364 | 3970  | 0.0074        |
+| 0.7383 | 3980  | 0.0072        |
+| 0.7401 | 3990  | 0.0111        |
+| 0.7420 | 4000  | 0.0186        |
+| 0.7438 | 4010  | 0.0311        |
+| 0.7457 | 4020  | 0.0113        |
+| 0.7475 | 4030  | 0.0186        |
+| 0.7494 | 4040  | 0.0083        |
+| 0.7513 | 4050  | 0.0138        |
+| 0.7531 | 4060  | 0.0154        |
+| 0.7550 | 4070  | 0.0082        |
+| 0.7568 | 4080  | 0.0064        |
+| 0.7587 | 4090  | 0.0377        |
+| 0.7605 | 4100  | 0.0027        |
+| 0.7624 | 4110  | 0.0095        |
+| 0.7642 | 4120  | 0.0241        |
+| 0.7661 | 4130  | 0.0023        |
+| 0.7679 | 4140  | 0.0123        |
+| 0.7698 | 4150  | 0.0104        |
+| 0.7717 | 4160  | 0.0464        |
+| 0.7735 | 4170  | 0.0049        |
+| 0.7754 | 4180  | 0.0021        |
+| 0.7772 | 4190  | 0.0423        |
+| 0.7791 | 4200  | 0.0108        |
+| 0.7809 | 4210  | 0.0166        |
+| 0.7828 | 4220  | 0.0039        |
+| 0.7846 | 4230  | 0.0229        |
+| 0.7865 | 4240  | 0.0218        |
+| 0.7884 | 4250  | 0.0155        |
+| 0.7902 | 4260  | 0.0058        |
+| 0.7921 | 4270  | 0.0057        |
+| 0.7939 | 4280  | 0.0104        |
+| 0.7958 | 4290  | 0.0111        |
+| 0.7976 | 4300  | 0.0039        |
+| 0.7995 | 4310  | 0.0062        |
+| 0.8013 | 4320  | 0.0011        |
+| 0.8032 | 4330  | 0.0046        |
+| 0.8050 | 4340  | 0.0154        |
+| 0.8069 | 4350  | 0.0078        |
+| 0.8088 | 4360  | 0.0227        |
+| 0.8106 | 4370  | 0.0126        |
+| 0.8125 | 4380  | 0.003         |
+| 0.8143 | 4390  | 0.0169        |
+| 0.8162 | 4400  | 0.008         |
+| 0.8180 | 4410  | 0.0082        |
+| 0.8199 | 4420  | 0.0067        |
+| 0.8217 | 4430  | 0.0109        |
+| 0.8236 | 4440  | 0.0094        |
+| 0.8254 | 4450  | 0.0095        |
+| 0.8273 | 4460  | 0.0017        |
+| 0.8292 | 4470  | 0.0065        |
+| 0.8310 | 4480  | 0.0104        |
+| 0.8329 | 4490  | 0.0121        |
+| 0.8347 | 4500  | 0.0172        |
+| 0.8366 | 4510  | 0.0065        |
+| 0.8384 | 4520  | 0.0131        |
+| 0.8403 | 4530  | 0.0069        |
+| 0.8421 | 4540  | 0.0019        |
+| 0.8440 | 4550  | 0.011         |
+| 0.8459 | 4560  | 0.0087        |
+| 0.8477 | 4570  | 0.0151        |
+| 0.8496 | 4580  | 0.0048        |
+| 0.8514 | 4590  | 0.0024        |
+| 0.8533 | 4600  | 0.0398        |
+| 0.8551 | 4610  | 0.0142        |
+| 0.8570 | 4620  | 0.0058        |
+| 0.8588 | 4630  | 0.0071        |
+| 0.8607 | 4640  | 0.0137        |
+| 0.8625 | 4650  | 0.0172        |
+| 0.8644 | 4660  | 0.0066        |
+| 0.8663 | 4670  | 0.0294        |
+| 0.8681 | 4680  | 0.013         |
+| 0.8700 | 4690  | 0.0061        |
+| 0.8718 | 4700  | 0.0176        |
+| 0.8737 | 4710  | 0.0105        |
+| 0.8755 | 4720  | 0.0032        |
+| 0.8774 | 4730  | 0.0035        |
+| 0.8792 | 4740  | 0.0115        |
+| 0.8811 | 4750  | 0.0034        |
+| 0.8830 | 4760  | 0.0019        |
+| 0.8848 | 4770  | 0.021         |
+| 0.8867 | 4780  | 0.0105        |
+| 0.8885 | 4790  | 0.0096        |
+| 0.8904 | 4800  | 0.0127        |
+| 0.8922 | 4810  | 0.0093        |
+| 0.8941 | 4820  | 0.0307        |
+| 0.8959 | 4830  | 0.011         |
+| 0.8978 | 4840  | 0.0107        |
+| 0.8996 | 4850  | 0.0161        |
+| 0.9015 | 4860  | 0.0073        |
+| 0.9034 | 4870  | 0.0098        |
+| 0.9052 | 4880  | 0.0085        |
+| 0.9071 | 4890  | 0.0153        |
+| 0.9089 | 4900  | 0.0241        |
+| 0.9108 | 4910  | 0.01          |
+| 0.9126 | 4920  | 0.0072        |
+| 0.9145 | 4930  | 0.0093        |
+| 0.9163 | 4940  | 0.0282        |
+| 0.9182 | 4950  | 0.0249        |
+| 0.9201 | 4960  | 0.0005        |
+| 0.9219 | 4970  | 0.0075        |
+| 0.9238 | 4980  | 0.0053        |
+| 0.9256 | 4990  | 0.019         |
+| 0.9275 | 5000  | 0.005         |
+| 0.9293 | 5010  | 0.0071        |
+| 0.9312 | 5020  | 0.0083        |
+| 0.9330 | 5030  | 0.0097        |
+| 0.9349 | 5040  | 0.0089        |
+| 0.9367 | 5050  | 0.0107        |
+| 0.9386 | 5060  | 0.0135        |
+| 0.9405 | 5070  | 0.0145        |
+| 0.9423 | 5080  | 0.001         |
+| 0.9442 | 5090  | 0.014         |
+| 0.9460 | 5100  | 0.018         |
+| 0.9479 | 5110  | 0.0007        |
+| 0.9497 | 5120  | 0.0076        |
+| 0.9516 | 5130  | 0.0066        |
+| 0.9534 | 5140  | 0.0057        |
+| 0.9553 | 5150  | 0.0282        |
+| 0.9572 | 5160  | 0.0006        |
+| 0.9590 | 5170  | 0.012         |
+| 0.9609 | 5180  | 0.0145        |
+| 0.9627 | 5190  | 0.0085        |
+| 0.9646 | 5200  | 0.004         |
+| 0.9664 | 5210  | 0.023         |
+| 0.9683 | 5220  | 0.0074        |
+| 0.9701 | 5230  | 0.0187        |
+| 0.9720 | 5240  | 0.0207        |
+| 0.9738 | 5250  | 0.0126        |
+| 0.9757 | 5260  | 0.004         |
+| 0.9776 | 5270  | 0.0034        |
+| 0.9794 | 5280  | 0.0036        |
+| 0.9813 | 5290  | 0.0084        |
+| 0.9831 | 5300  | 0.0008        |
+| 0.9850 | 5310  | 0.0038        |
+| 0.9868 | 5320  | 0.0268        |
+| 0.9887 | 5330  | 0.0033        |
+| 0.9905 | 5340  | 0.0055        |
+| 0.9924 | 5350  | 0.0069        |
+| 0.9942 | 5360  | 0.0017        |
+| 0.9961 | 5370  | 0.002         |
+| 0.9980 | 5380  | 0.0171        |
+| 0.9998 | 5390  | 0.0083        |
+| 1.0017 | 5400  | 0.0258        |
+| 1.0035 | 5410  | 0.0069        |
+| 1.0054 | 5420  | 0.025         |
+| 1.0072 | 5430  | 0.0067        |
+| 1.0091 | 5440  | 0.0023        |
+| 1.0109 | 5450  | 0.013         |
+| 1.0128 | 5460  | 0.0127        |
+| 1.0147 | 5470  | 0.0089        |
+| 1.0165 | 5480  | 0.0071        |
+| 1.0184 | 5490  | 0.0266        |
+| 1.0202 | 5500  | 0.0202        |
+| 1.0221 | 5510  | 0.036         |
+| 1.0239 | 5520  | 0.001         |
+| 1.0258 | 5530  | 0.0175        |
+| 1.0276 | 5540  | 0.0288        |
+| 1.0295 | 5550  | 0.0283        |
+| 1.0313 | 5560  | 0.0171        |
+| 1.0332 | 5570  | 0.0046        |
+| 1.0351 | 5580  | 0.036         |
+| 1.0369 | 5590  | 0.0073        |
+| 1.0388 | 5600  | 0.0115        |
+| 1.0406 | 5610  | 0.0351        |
+| 1.0425 | 5620  | 0.0148        |
+| 1.0443 | 5630  | 0.0092        |
+| 1.0462 | 5640  | 0.0072        |
+| 1.0480 | 5650  | 0.0049        |
+| 1.0499 | 5660  | 0.005         |
+| 1.0518 | 5670  | 0.0056        |
+| 1.0536 | 5680  | 0.0064        |
+| 1.0555 | 5690  | 0.003         |
+| 1.0573 | 5700  | 0.009         |
+| 1.0592 | 5710  | 0.0094        |
+| 1.0610 | 5720  | 0.0041        |
+| 1.0629 | 5730  | 0.0038        |
+| 1.0647 | 5740  | 0.0032        |
+| 1.0666 | 5750  | 0.0179        |
+| 1.0684 | 5760  | 0.0036        |
+| 1.0703 | 5770  | 0.0109        |
+| 1.0722 | 5780  | 0.0041        |
+| 1.0740 | 5790  | 0.0119        |
+| 1.0759 | 5800  | 0.0178        |
+| 1.0777 | 5810  | 0.007         |
+| 1.0796 | 5820  | 0.0028        |
+| 1.0814 | 5830  | 0.0075        |
+| 1.0833 | 5840  | 0.0058        |
+| 1.0851 | 5850  | 0.0076        |
+| 1.0870 | 5860  | 0.0043        |
+| 1.0889 | 5870  | 0.0026        |
+| 1.0907 | 5880  | 0.0074        |
+| 1.0926 | 5890  | 0.0087        |
+| 1.0944 | 5900  | 0.0041        |
+| 1.0963 | 5910  | 0.0066        |
+| 1.0981 | 5920  | 0.0116        |
+| 1.1000 | 5930  | 0.0069        |
+| 1.1018 | 5940  | 0.012         |
+| 1.1037 | 5950  | 0.0085        |
+| 1.1055 | 5960  | 0.0353        |
+| 1.1074 | 5970  | 0.0136        |
+| 1.1093 | 5980  | 0.0288        |
+| 1.1111 | 5990  | 0.0187        |
+| 1.1130 | 6000  | 0.0237        |
+| 1.1148 | 6010  | 0.0074        |
+| 1.1167 | 6020  | 0.0118        |
+| 1.1185 | 6030  | 0.0378        |
+| 1.1204 | 6040  | 0.0012        |
+| 1.1222 | 6050  | 0.0009        |
+| 1.1241 | 6060  | 0.0007        |
+| 1.1260 | 6070  | 0.0141        |
+| 1.1278 | 6080  | 0.0089        |
+| 1.1297 | 6090  | 0.0104        |
+| 1.1315 | 6100  | 0.0018        |
+| 1.1334 | 6110  | 0.0102        |
+| 1.1352 | 6120  | 0.0039        |
+| 1.1371 | 6130  | 0.0014        |
+| 1.1389 | 6140  | 0.002         |
+| 1.1408 | 6150  | 0.0049        |
+| 1.1426 | 6160  | 0.0074        |
+| 1.1445 | 6170  | 0.0052        |
+| 1.1464 | 6180  | 0.0034        |
+| 1.1482 | 6190  | 0.0116        |
+| 1.1501 | 6200  | 0.0276        |
+| 1.1519 | 6210  | 0.0026        |
+| 1.1538 | 6220  | 0.006         |
+| 1.1556 | 6230  | 0.0026        |
+| 1.1575 | 6240  | 0.0063        |
+| 1.1593 | 6250  | 0.0107        |
+| 1.1612 | 6260  | 0.0053        |
+| 1.1630 | 6270  | 0.0144        |
+| 1.1649 | 6280  | 0.0113        |
+| 1.1668 | 6290  | 0.0143        |
+| 1.1686 | 6300  | 0.0039        |
+| 1.1705 | 6310  | 0.0066        |
+| 1.1723 | 6320  | 0.001         |
+| 1.1742 | 6330  | 0.0127        |
+| 1.1760 | 6340  | 0.0004        |
+| 1.1779 | 6350  | 0.0046        |
+| 1.1797 | 6360  | 0.0102        |
+| 1.1816 | 6370  | 0.0066        |
+| 1.1835 | 6380  | 0.0009        |
+| 1.1853 | 6390  | 0.006         |
+| 1.1872 | 6400  | 0.0005        |
+| 1.1890 | 6410  | 0.0043        |
+| 1.1909 | 6420  | 0.005         |
+| 1.1927 | 6430  | 0.0048        |
+| 1.1946 | 6440  | 0.002         |
+| 1.1964 | 6450  | 0.0033        |
+| 1.1983 | 6460  | 0.0043        |
+| 1.2001 | 6470  | 0.004         |
+| 1.2020 | 6480  | 0.0147        |
+| 1.2039 | 6490  | 0.0105        |
+| 1.2057 | 6500  | 0.0078        |
+| 1.2076 | 6510  | 0.001         |
+| 1.2094 | 6520  | 0.007         |
+| 1.2113 | 6530  | 0.0052        |
+| 1.2131 | 6540  | 0.0112        |
+| 1.2150 | 6550  | 0.001         |
+| 1.2168 | 6560  | 0.0032        |
+| 1.2187 | 6570  | 0.0009        |
+| 1.2206 | 6580  | 0.0026        |
+| 1.2224 | 6590  | 0.0021        |
+| 1.2243 | 6600  | 0.0159        |
+| 1.2261 | 6610  | 0.0044        |
+| 1.2280 | 6620  | 0.001         |
+| 1.2298 | 6630  | 0.0137        |
+| 1.2317 | 6640  | 0.0024        |
+| 1.2335 | 6650  | 0.0085        |
+| 1.2354 | 6660  | 0.0012        |
+| 1.2372 | 6670  | 0.0149        |
+| 1.2391 | 6680  | 0.0214        |
+| 1.2410 | 6690  | 0.0017        |
+| 1.2428 | 6700  | 0.0049        |
+| 1.2447 | 6710  | 0.0069        |
+| 1.2465 | 6720  | 0.0178        |
+| 1.2484 | 6730  | 0.0137        |
+| 1.2502 | 6740  | 0.0075        |
+| 1.2521 | 6750  | 0.0168        |
+| 1.2539 | 6760  | 0.0056        |
+| 1.2558 | 6770  | 0.0074        |
+| 1.2577 | 6780  | 0.0028        |
+| 1.2595 | 6790  | 0.0011        |
+| 1.2614 | 6800  | 0.0086        |
+| 1.2632 | 6810  | 0.0008        |
+| 1.2651 | 6820  | 0.0023        |
+| 1.2669 | 6830  | 0.0047        |
+| 1.2688 | 6840  | 0.0008        |
+| 1.2706 | 6850  | 0.0086        |
+| 1.2725 | 6860  | 0.0044        |
+| 1.2743 | 6870  | 0.013         |
+| 1.2762 | 6880  | 0.0015        |
+| 1.2781 | 6890  | 0.0034        |
+| 1.2799 | 6900  | 0.0144        |
+| 1.2818 | 6910  | 0.0114        |
+| 1.2836 | 6920  | 0.0006        |
+| 1.2855 | 6930  | 0.0124        |
+| 1.2873 | 6940  | 0.002         |
+| 1.2892 | 6950  | 0.0063        |
+| 1.2910 | 6960  | 0.0006        |
+| 1.2929 | 6970  | 0.0048        |
+| 1.2948 | 6980  | 0.0117        |
+| 1.2966 | 6990  | 0.0071        |
+| 1.2985 | 7000  | 0.0015        |
+| 1.3003 | 7010  | 0.0045        |
+| 1.3022 | 7020  | 0.0018        |
+| 1.3040 | 7030  | 0.0079        |
+| 1.3059 | 7040  | 0.0071        |
+| 1.3077 | 7050  | 0.0202        |
+| 1.3096 | 7060  | 0.0031        |
+| 1.3114 | 7070  | 0.0096        |
+| 1.3133 | 7080  | 0.0019        |
+| 1.3152 | 7090  | 0.0016        |
+| 1.3170 | 7100  | 0.0092        |
+| 1.3189 | 7110  | 0.0046        |
+| 1.3207 | 7120  | 0.0025        |
+| 1.3226 | 7130  | 0.0107        |
+| 1.3244 | 7140  | 0.0011        |
+| 1.3263 | 7150  | 0.0036        |
+| 1.3281 | 7160  | 0.0019        |
+| 1.3300 | 7170  | 0.0012        |
+| 1.3318 | 7180  | 0.0073        |
+| 1.3337 | 7190  | 0.0093        |
+| 1.3356 | 7200  | 0.0191        |
+| 1.3374 | 7210  | 0.0055        |
+| 1.3393 | 7220  | 0.0009        |
+| 1.3411 | 7230  | 0.0005        |
+| 1.3430 | 7240  | 0.0142        |
+| 1.3448 | 7250  | 0.0007        |
+| 1.3467 | 7260  | 0.018         |
+| 1.3485 | 7270  | 0.0117        |
+| 1.3504 | 7280  | 0.0139        |
+| 1.3523 | 7290  | 0.0054        |
+| 1.3541 | 7300  | 0.0034        |
+| 1.3560 | 7310  | 0.0068        |
+| 1.3578 | 7320  | 0.0076        |
+| 1.3597 | 7330  | 0.0026        |
+| 1.3615 | 7340  | 0.0022        |
+| 1.3634 | 7350  | 0.0045        |
+| 1.3652 | 7360  | 0.0097        |
+| 1.3671 | 7370  | 0.0072        |
+| 1.3689 | 7380  | 0.0185        |
+| 1.3708 | 7390  | 0.0024        |
+| 1.3727 | 7400  | 0.0007        |
+| 1.3745 | 7410  | 0.0058        |
+| 1.3764 | 7420  | 0.0175        |
+| 1.3782 | 7430  | 0.0078        |
+| 1.3801 | 7440  | 0.0124        |
+| 1.3819 | 7450  | 0.0014        |
+| 1.3838 | 7460  | 0.0083        |
+| 1.3856 | 7470  | 0.0187        |
+| 1.3875 | 7480  | 0.0016        |
+| 1.3894 | 7490  | 0.001         |
+| 1.3912 | 7500  | 0.0016        |
+| 1.3931 | 7510  | 0.0032        |
+| 1.3949 | 7520  | 0.0082        |
+| 1.3968 | 7530  | 0.0309        |
+| 1.3986 | 7540  | 0.0081        |
+| 1.4005 | 7550  | 0.0213        |
+| 1.4023 | 7560  | 0.0228        |
+| 1.4042 | 7570  | 0.0007        |
+| 1.4060 | 7580  | 0.0008        |
+| 1.4079 | 7590  | 0.0037        |
+| 1.4098 | 7600  | 0.0077        |
+| 1.4116 | 7610  | 0.0034        |
+| 1.4135 | 7620  | 0.005         |
+| 1.4153 | 7630  | 0.0007        |
+| 1.4172 | 7640  | 0.0044        |
+| 1.4190 | 7650  | 0.0046        |
+| 1.4209 | 7660  | 0.0038        |
+| 1.4227 | 7670  | 0.0012        |
+| 1.4246 | 7680  | 0.0045        |
+| 1.4265 | 7690  | 0.0031        |
+| 1.4283 | 7700  | 0.0027        |
+| 1.4302 | 7710  | 0.0029        |
+| 1.4320 | 7720  | 0.0027        |
+| 1.4339 | 7730  | 0.0109        |
+| 1.4357 | 7740  | 0.0021        |
+| 1.4376 | 7750  | 0.0152        |
+| 1.4394 | 7760  | 0.002         |
+| 1.4413 | 7770  | 0.0157        |
+| 1.4431 | 7780  | 0.0045        |
+| 1.4450 | 7790  | 0.0022        |
+| 1.4469 | 7800  | 0.0121        |
+| 1.4487 | 7810  | 0.0013        |
+| 1.4506 | 7820  | 0.0064        |
+| 1.4524 | 7830  | 0.0046        |
+| 1.4543 | 7840  | 0.0152        |
+| 1.4561 | 7850  | 0.0028        |
+| 1.4580 | 7860  | 0.0065        |
+| 1.4598 | 7870  | 0.0098        |
+| 1.4617 | 7880  | 0.0016        |
+| 1.4636 | 7890  | 0.0126        |
+| 1.4654 | 7900  | 0.0054        |
+| 1.4673 | 7910  | 0.0019        |
+| 1.4691 | 7920  | 0.0039        |
+| 1.4710 | 7930  | 0.0024        |
+| 1.4728 | 7940  | 0.0012        |
+| 1.4747 | 7950  | 0.0015        |
+| 1.4765 | 7960  | 0.0089        |
+| 1.4784 | 7970  | 0.0016        |
+| 1.4802 | 7980  | 0.001         |
+| 1.4821 | 7990  | 0.0119        |
+| 1.4840 | 8000  | 0.0022        |
+| 1.4858 | 8010  | 0.0121        |
+| 1.4877 | 8020  | 0.0045        |
+| 1.4895 | 8030  | 0.0016        |
+| 1.4914 | 8040  | 0.0006        |
+| 1.4932 | 8050  | 0.0165        |
+| 1.4951 | 8060  | 0.0081        |
+| 1.4969 | 8070  | 0.0017        |
+| 1.4988 | 8080  | 0.0014        |
+| 1.5006 | 8090  | 0.0023        |
+| 1.5025 | 8100  | 0.0104        |
+| 1.5044 | 8110  | 0.0112        |
+| 1.5062 | 8120  | 0.026         |
+| 1.5081 | 8130  | 0.0086        |
+| 1.5099 | 8140  | 0.0057        |
+| 1.5118 | 8150  | 0.0051        |
+| 1.5136 | 8160  | 0.0104        |
+| 1.5155 | 8170  | 0.0268        |
+| 1.5173 | 8180  | 0.0083        |
+| 1.5192 | 8190  | 0.0075        |
+| 1.5211 | 8200  | 0.0062        |
+| 1.5229 | 8210  | 0.0095        |
+| 1.5248 | 8220  | 0.0144        |
+| 1.5266 | 8230  | 0.0018        |
+| 1.5285 | 8240  | 0.0047        |
+| 1.5303 | 8250  | 0.0042        |
+| 1.5322 | 8260  | 0.0038        |
+| 1.5340 | 8270  | 0.0081        |
+| 1.5359 | 8280  | 0.0044        |
+| 1.5377 | 8290  | 0.0091        |
+| 1.5396 | 8300  | 0.01          |
+| 1.5415 | 8310  | 0.0171        |
+| 1.5433 | 8320  | 0.01          |
+| 1.5452 | 8330  | 0.015         |
+| 1.5470 | 8340  | 0.0007        |
+| 1.5489 | 8350  | 0.0117        |
+| 1.5507 | 8360  | 0.008         |
+| 1.5526 | 8370  | 0.0089        |
+| 1.5544 | 8380  | 0.0028        |
+| 1.5563 | 8390  | 0.0106        |
+| 1.5582 | 8400  | 0.0084        |
+| 1.5600 | 8410  | 0.0009        |
+| 1.5619 | 8420  | 0.0084        |
+| 1.5637 | 8430  | 0.007         |
+| 1.5656 | 8440  | 0.0009        |
+| 1.5674 | 8450  | 0.0013        |
+| 1.5693 | 8460  | 0.0117        |
+| 1.5711 | 8470  | 0.0089        |
+| 1.5730 | 8480  | 0.0041        |
+| 1.5748 | 8490  | 0.0032        |
+| 1.5767 | 8500  | 0.0132        |
+| 1.5786 | 8510  | 0.0009        |
+| 1.5804 | 8520  | 0.0055        |
+| 1.5823 | 8530  | 0.0075        |
+| 1.5841 | 8540  | 0.0006        |
+| 1.5860 | 8550  | 0.0031        |
+| 1.5878 | 8560  | 0.0095        |
+| 1.5897 | 8570  | 0.0188        |
+| 1.5915 | 8580  | 0.0082        |
+| 1.5934 | 8590  | 0.0059        |
+| 1.5953 | 8600  | 0.014         |
+| 1.5971 | 8610  | 0.0082        |
+| 1.5990 | 8620  | 0.01          |
+| 1.6008 | 8630  | 0.013         |
+| 1.6027 | 8640  | 0.0066        |
+| 1.6045 | 8650  | 0.0015        |
+| 1.6064 | 8660  | 0.001         |
+| 1.6082 | 8670  | 0.0084        |
+| 1.6101 | 8680  | 0.0047        |
+| 1.6119 | 8690  | 0.0422        |
+| 1.6138 | 8700  | 0.0094        |
+| 1.6157 | 8710  | 0.0014        |
+| 1.6175 | 8720  | 0.0022        |
+| 1.6194 | 8730  | 0.005         |
+| 1.6212 | 8740  | 0.0085        |
+| 1.6231 | 8750  | 0.0054        |
+| 1.6249 | 8760  | 0.0118        |
+| 1.6268 | 8770  | 0.0085        |
+| 1.6286 | 8780  | 0.0105        |
+| 1.6305 | 8790  | 0.0063        |
+| 1.6324 | 8800  | 0.0054        |
+| 1.6342 | 8810  | 0.0024        |
+| 1.6361 | 8820  | 0.0055        |
+| 1.6379 | 8830  | 0.0151        |
+| 1.6398 | 8840  | 0.0157        |
+| 1.6416 | 8850  | 0.0032        |
+| 1.6435 | 8860  | 0.0014        |
+| 1.6453 | 8870  | 0.0115        |
+| 1.6472 | 8880  | 0.0011        |
+| 1.6490 | 8890  | 0.0013        |
+| 1.6509 | 8900  | 0.0321        |
+| 1.6528 | 8910  | 0.0076        |
+| 1.6546 | 8920  | 0.0048        |
+| 1.6565 | 8930  | 0.0402        |
+| 1.6583 | 8940  | 0.0152        |
+| 1.6602 | 8950  | 0.0096        |
+| 1.6620 | 8960  | 0.0063        |
+| 1.6639 | 8970  | 0.0009        |
+| 1.6657 | 8980  | 0.0032        |
+| 1.6676 | 8990  | 0.0282        |
+| 1.6694 | 9000  | 0.0457        |
+| 1.6713 | 9010  | 0.0008        |
+| 1.6732 | 9020  | 0.003         |
+| 1.6750 | 9030  | 0.0019        |
+| 1.6769 | 9040  | 0.0013        |
+| 1.6787 | 9050  | 0.0048        |
+| 1.6806 | 9060  | 0.0081        |
+| 1.6824 | 9070  | 0.0094        |
+| 1.6843 | 9080  | 0.0043        |
+| 1.6861 | 9090  | 0.0032        |
+| 1.6880 | 9100  | 0.0045        |
+| 1.6899 | 9110  | 0.0091        |
+| 1.6917 | 9120  | 0.0047        |
+| 1.6936 | 9130  | 0.0306        |
+| 1.6954 | 9140  | 0.0031        |
+| 1.6973 | 9150  | 0.0068        |
+| 1.6991 | 9160  | 0.0008        |
+| 1.7010 | 9170  | 0.0093        |
+| 1.7028 | 9180  | 0.0039        |
+| 1.7047 | 9190  | 0.0074        |
+| 1.7065 | 9200  | 0.0006        |
+| 1.7084 | 9210  | 0.0204        |
+| 1.7103 | 9220  | 0.0036        |
+| 1.7121 | 9230  | 0.0043        |
+| 1.7140 | 9240  | 0.0068        |
+| 1.7158 | 9250  | 0.0061        |
+| 1.7177 | 9260  | 0.0027        |
+| 1.7195 | 9270  | 0.0061        |
+| 1.7214 | 9280  | 0.0045        |
+| 1.7232 | 9290  | 0.001         |
+| 1.7251 | 9300  | 0.0131        |
+| 1.7270 | 9310  | 0.009         |
+| 1.7288 | 9320  | 0.0074        |
+| 1.7307 | 9330  | 0.0101        |
+| 1.7325 | 9340  | 0.0052        |
+| 1.7344 | 9350  | 0.0174        |
+| 1.7362 | 9360  | 0.005         |
+| 1.7381 | 9370  | 0.0052        |
+| 1.7399 | 9380  | 0.0076        |
+| 1.7418 | 9390  | 0.0143        |
+| 1.7436 | 9400  | 0.0234        |
+| 1.7455 | 9410  | 0.0085        |
+| 1.7474 | 9420  | 0.0145        |
+| 1.7492 | 9430  | 0.0063        |
+| 1.7511 | 9440  | 0.0114        |
+| 1.7529 | 9450  | 0.0139        |
+| 1.7548 | 9460  | 0.0037        |
+| 1.7566 | 9470  | 0.0056        |
+| 1.7585 | 9480  | 0.0323        |
+| 1.7603 | 9490  | 0.0021        |
+| 1.7622 | 9500  | 0.0069        |
+| 1.7641 | 9510  | 0.0221        |
+| 1.7659 | 9520  | 0.0013        |
+| 1.7678 | 9530  | 0.002         |
+| 1.7696 | 9540  | 0.0185        |
+| 1.7715 | 9550  | 0.0375        |
+| 1.7733 | 9560  | 0.004         |
+| 1.7752 | 9570  | 0.0014        |
+| 1.7770 | 9580  | 0.0357        |
+| 1.7789 | 9590  | 0.0077        |
+| 1.7807 | 9600  | 0.0066        |
+| 1.7826 | 9610  | 0.008         |
+| 1.7845 | 9620  | 0.0205        |
+| 1.7863 | 9630  | 0.0182        |
+| 1.7882 | 9640  | 0.0119        |
+| 1.7900 | 9650  | 0.0044        |
+| 1.7919 | 9660  | 0.0043        |
+| 1.7937 | 9670  | 0.0089        |
+| 1.7956 | 9680  | 0.0086        |
+| 1.7974 | 9690  | 0.0033        |
+| 1.7993 | 9700  | 0.0049        |
+| 1.8012 | 9710  | 0.0008        |
+| 1.8030 | 9720  | 0.0038        |
+| 1.8049 | 9730  | 0.014         |
+| 1.8067 | 9740  | 0.0049        |
+| 1.8086 | 9750  | 0.018         |
+| 1.8104 | 9760  | 0.0147        |
+| 1.8123 | 9770  | 0.0021        |
+| 1.8141 | 9780  | 0.0163        |
+| 1.8160 | 9790  | 0.007         |
+| 1.8178 | 9800  | 0.0062        |
+| 1.8197 | 9810  | 0.0049        |
+| 1.8216 | 9820  | 0.0086        |
+| 1.8234 | 9830  | 0.0074        |
+| 1.8253 | 9840  | 0.0082        |
+| 1.8271 | 9850  | 0.0019        |
+| 1.8290 | 9860  | 0.0059        |
+| 1.8308 | 9870  | 0.0057        |
+| 1.8327 | 9880  | 0.0115        |
+| 1.8345 | 9890  | 0.0163        |
+| 1.8364 | 9900  | 0.001         |
+| 1.8382 | 9910  | 0.0084        |
+| 1.8401 | 9920  | 0.0105        |
+| 1.8420 | 9930  | 0.0014        |
+| 1.8438 | 9940  | 0.009         |
+| 1.8457 | 9950  | 0.0076        |
+| 1.8475 | 9960  | 0.009         |
+| 1.8494 | 9970  | 0.0068        |
+| 1.8512 | 9980  | 0.002         |
+| 1.8531 | 9990  | 0.0229        |
+| 1.8549 | 10000 | 0.0252        |
+| 1.8568 | 10010 | 0.0032        |
+| 1.8587 | 10020 | 0.0066        |
+| 1.8605 | 10030 | 0.0056        |
+| 1.8624 | 10040 | 0.0156        |
+| 1.8642 | 10050 | 0.0073        |
+| 1.8661 | 10060 | 0.022         |
+| 1.8679 | 10070 | 0.0147        |
+| 1.8698 | 10080 | 0.0052        |
+| 1.8716 | 10090 | 0.0166        |
+| 1.8735 | 10100 | 0.0093        |
+| 1.8753 | 10110 | 0.003         |
+| 1.8772 | 10120 | 0.0023        |
+| 1.8791 | 10130 | 0.0114        |
+| 1.8809 | 10140 | 0.0031        |
+| 1.8828 | 10150 | 0.0014        |
+| 1.8846 | 10160 | 0.0155        |
+| 1.8865 | 10170 | 0.0117        |
+| 1.8883 | 10180 | 0.0059        |
+| 1.8902 | 10190 | 0.0074        |
+| 1.8920 | 10200 | 0.0057        |
+| 1.8939 | 10210 | 0.0279        |
+| 1.8958 | 10220 | 0.0117        |
+| 1.8976 | 10230 | 0.0082        |
+| 1.8995 | 10240 | 0.0123        |
+| 1.9013 | 10250 | 0.0065        |
+| 1.9032 | 10260 | 0.0086        |
+| 1.9050 | 10270 | 0.0078        |
+| 1.9069 | 10280 | 0.0135        |
+| 1.9087 | 10290 | 0.0237        |
+| 1.9106 | 10300 | 0.0086        |
+| 1.9124 | 10310 | 0.006         |
+| 1.9143 | 10320 | 0.0071        |
+| 1.9162 | 10330 | 0.0179        |
+| 1.9180 | 10340 | 0.0158        |
+| 1.9199 | 10350 | 0.0104        |
+| 1.9217 | 10360 | 0.0056        |
+| 1.9236 | 10370 | 0.004         |
+| 1.9254 | 10380 | 0.0173        |
+| 1.9273 | 10390 | 0.0019        |
+| 1.9291 | 10400 | 0.0068        |
+| 1.9310 | 10410 | 0.0071        |
+| 1.9329 | 10420 | 0.0092        |
+| 1.9347 | 10430 | 0.0085        |
+| 1.9366 | 10440 | 0.0062        |
+| 1.9384 | 10450 | 0.0111        |
+| 1.9403 | 10460 | 0.0143        |
+| 1.9421 | 10470 | 0.0008        |
+| 1.9440 | 10480 | 0.0115        |
+| 1.9458 | 10490 | 0.0167        |
+| 1.9477 | 10500 | 0.0007        |
+| 1.9495 | 10510 | 0.0062        |
+| 1.9514 | 10520 | 0.0024        |
+| 1.9533 | 10530 | 0.0071        |
+| 1.9551 | 10540 | 0.0139        |
+| 1.9570 | 10550 | 0.0108        |
+| 1.9588 | 10560 | 0.0102        |
+| 1.9607 | 10570 | 0.012         |
+| 1.9625 | 10580 | 0.0073        |
+| 1.9644 | 10590 | 0.0039        |
+| 1.9662 | 10600 | 0.021         |
+| 1.9681 | 10610 | 0.0022        |
+| 1.9699 | 10620 | 0.0058        |
+| 1.9718 | 10630 | 0.0341        |
+| 1.9737 | 10640 | 0.0097        |
+| 1.9755 | 10650 | 0.0038        |
+| 1.9774 | 10660 | 0.003         |
+| 1.9792 | 10670 | 0.003         |
+| 1.9811 | 10680 | 0.0061        |
+| 1.9829 | 10690 | 0.0006        |
+| 1.9848 | 10700 | 0.0012        |
+| 1.9866 | 10710 | 0.0154        |
+| 1.9885 | 10720 | 0.0115        |
+| 1.9904 | 10730 | 0.005         |
+| 1.9922 | 10740 | 0.0061        |
+| 1.9941 | 10750 | 0.0018        |
+| 1.9959 | 10760 | 0.0011        |
+| 1.9978 | 10770 | 0.0173        |
+| 1.9996 | 10780 | 0.0066        |
+
+</details>
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.1.2
+- Transformers: 4.51.3
+- PyTorch: 2.9.1+cu128
+- Accelerate: 1.12.0
+- Datasets: 4.4.1
+- Tokenizers: 0.21.4
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "gradient_checkpointing": false,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151669
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "prompts": {
+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine",
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.51.3",
+    "pytorch": "2.9.1+cu128"
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70076e9713aab2fbb2bd4fb9fee88c9b84f22dd2df1d433803893c5546483a6a
+size 1191586416

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": 1024,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b982a210810d72da18b6d33f34ee4621cc6daa7b981ff99fcf1be9268d5223d
+size 11423972

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    "151646": {
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+      "rstrip": false,
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+    },
+    "151647": {
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+    },
+    "151657": {
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+      "lstrip": false,
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+    },
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+      "content": "<|repo_name|>",
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+      "normalized": false,
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+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
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+    },
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+      "rstrip": false,
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+    },
+    "151666": {
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+      "normalized": false,
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+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
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+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}