{
  "public": {
    "netWorkError": "Network or backend service error. Please check.",
    "browserWarning": "Your browser may cause some functions to become invalid or unavailable. You are advised to use Chrome 65 or later.",
    "timeout": "Timeout. Try again.",
    "noData": "No data",
    "reset": "Reset",
    "tagFilterPlaceHolder": "Enter tag (regular expression supported)",
    "dataError": "The obtained data is abnormal.",
    "regIllegal": "Enter a correct search criterion.",
    "stayTuned": "Coming soon...",
    "select": "Select",
    "search": "Search",
    "enter": "Enter",
    "remark": "Remarks",
    "zhLanguage": "中",
    "enLanguage": "EN",
    "sure": "OK",
    "clear": "Clear",
    "cancel": "Cancel",
    "selectAll": "All",
    "deselectAll": "Clear",
    "dataLoading": "Loading data...",
    "notice": "Information"
  },
  "symbols": {
    "leftbracket": "(",
    "rightbracket": ")",
    "point": "·",
    "slashes": "/",
    "colon": ":",
    "fullStop": "."
  },
  "header": {
    "refreshData": "Refresh Data",
    "refreshingData": "Refreshing data...",
    "timeReload": "Auto Refresh",
    "timeSecond": "Seconds",
    "timeReloadScope": "The automatic refresh interval ranges from 3 to 300 seconds."
  },
  "summaryManage": {
    "summaryList": "Summary List",
    "currentFolder": "Current folder:",
    "sorting": "No.",
    "summaryPath": "Summary Path",
    "createTime": "Creation Time",
    "updateTime": "Update Time",
    "operation": "Operation",
    "viewDashboard": "Training Dashboard",
    "viewProfiler": "Profiling",
    "modelTraceback": "Model Lineage",
    "dataTraceback": "Dataset Lineage",
    "comparePlate": "Comparison Dashboard",
    "disableProfilerTip": "Failed to view profiling because no profiler log is available.",
    "disableDashboardTip": "Failed to view training dashboard because no summary log or pb files are available.",
    "disableParameterTip": "Failed to view parameter details because no lineage log is available.",
    "openNewTab": "Open Link in New Tab",
    "paramDetails": "Parameter Details",
    "trainingParamDetails": "Training Parameter Details",
    "tracebackAnalysis": "Lineage Analysis",
    "compareAnalysis": "Comparison Analysis",
    "guidTipFirst": "In the training script, add ",
    "guidTipSecond": "the function of collecting summary data ",
    "guidTipThird": "and save ",
    "guidTipForth": "data.",
    "scalarTipFirst": "scalar ",
    "histogramGuidTipFirst": "parameter distribution ",
    "tensorTipFirst": "tensor ",
    "graphTipFirst": "computational graph ",
    "graphTipSecond": "structure data.",
    "dataProcessTipFirst": "data processing process",
    "imageTipFirst": "specified image",
    "guidUrl": "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/summary_record.html",
    "scalarUrl": "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/dashboard.html#scalar-visualization",
    "histogramUrl": "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/dashboard.html#parameter-distribution-visualization",
    "tensorUrl": "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/dashboard.html#tensor-visualization",
    "graphUrl": "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/dashboard.html#computational-graph-visualization",
    "dataProcessUrl": "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/dashboard.html#dataset-graph-visualization",
    "imageUrl": "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/dashboard.html#image-visualization"
  },
  "modelTraceback": {
    "summaryPath": "Summary Path",
    "trainSetPath": "Training Set Path",
    "testSetPath": "Test Set Path",
    "trainingSampleNum": "Training Samples",
    "testSampleNum": "Testing Samples",
    "showAllData": "Show All",
    "showSelected": "Show Selected",
    "hideSelected": "Hide Selected",
    "network": "Network",
    "optimizer": "Optimizer",
    "lossFunc": "Loss Function",
    "learningRate": "Learning Rate",
    "modelSize": "Model Size",
    "dataProcess": "Data Processing",
    "noDataFound": "No data found.",
    "allHide": "All data is hidden.",
    "userDefined": "User-defined Data",
    "metric": "Metrics",
    "deviceNum": "Devices",
    "mixedItemMessage": "This parameter contains multiple types of data and cannot be filtered.",
    "notSupportSelected": "Filtering is not supported for this parameter type.",
    "displayColumn": "Displayed columns",
    "mustExist": "Mandatory",
    "remarkValidation": "Remarks can contain 1 to 128 characters, including letters, digits, underscores (_), hyphens (-), and periods (.).",
    "changeSuccess": "Modified successfully.",
    "metricLabel": "Metric",
    "userDefinedLabel": "User Defined",
    "hyperLabel": "Hyper",
    "otherLabel": "Other",
    "remarkTips": "Note: Once the service is terminated, remarks and tags will be cleared.",
    "optimizationObject": "Optimization Objective",
    "targetDistribution": "Optimization Objective Distribution",
    "parameterImportance": "Parameter Importance",
    "optimizationTitle": "Parameter and Optimization Objective",
    "viewBigImage": "Zoom In",
    "mustOptions": "Mandatory",
    "customOptions": "Custom",
    "targetTips": "Click a bar from the preceding chart to display a scatter chart of corresponding parameter and optimization objective.",
    "reasonCode": {
      "1": "The parameter contains non-numeric values.",
      "2": "The number of valid items of the parameter and optimization objective is less than or equal to 2. The importance cannot be calculated.",
      "3": "The importance is NaN."
    },
    "explan": "Note"
  },
  "dataTraceback": {
    "details": "Details",
    "key": "KEY",
    "value": "VALUE",
    "dataTraceTips": "The data involves combination.",
    "noDataFound": "No data found."
  },
  "trainingDashboard": {
    "trainingDashboardTitle": "Training Dashboard",
    "calculationChart": "Computational Graph",
    "dataMap": "Data Graph",
    "trainingScalar": "Training Scalar Information",
    "samplingData": "Data Sampling",
    "imagesampleSwitch": "Switch Tag",
    "invalidId": "Invalid job.",
    "summaryDirPath": "Summary path:",
    "loadingTip": "Loading...",
    "waitLoading": "waiting to be loaded",
    "switchTitle": "The Computational Graph and Data Graph modules do not support auto refresh."
  },
  "scalar": {
    "titleText": "Scalar",
    "tagSelectTitle": "Tag Selection",
    "xAxisTitle": "Horizontal axis",
    "smoothness": "Smoothness",
    "step": "Step",
    "selectAll": "All",
    "relativeTime": "Relative time",
    "absoluteTime": "Absolute time",
    "fullScreen": "Full Screen",
    "stepBack": "Rollback by Step",
    "openOrCloseSelection": "Enable/Disable Box Selection",
    "toggleYaxisScale": "Switch Y-axis Ratio",
    "charTipHeadName": "Name",
    "charTipTagName": "Tag",
    "charTipHeadValue": "Value",
    "charSmoothedValue": "Smoothness",
    "comparison": "Scalar Synthesis",
    "compareCancel": "Cancel Synthesis",
    "open": "More",
    "close": "Less",
    "invalidData": "Invalid data exists.",
    "restore": "Restore",
    "currentThreshold": "Current threshold",
    "deleteThreshold": "Delete Threshold",
    "setThreshold": "Set Threshold",
    "currentTag": "Current Tag",
    "filterCriteria": "Filter",
    "placeHolderThreshold": "Threshold",
    "or": "Or",
    "and": "And",
    "greaterThan": "Greater than",
    "lessThan": "Less than",
    "applyAllSelectTag": "Apply to Selected Tags",
    "placeHolderNumber": "Enter a number",
    "noSpace": "Do not enter spaces.",
    "sameCompare": "The comparison operator must be unique.",
    "unreasonable": "The logic is improper.",
    "info": "Information",
    "isDelete": "Are you sure you want to delete the current threshold?"
  },
  "images": {
    "titleText": "Image",
    "tagSelectTitle": "Tag Selection",
    "selectAll": "All",
    "open": "More",
    "close": "Less",
    "step": "Step:",
    "setBright": "Brightness",
    "setContrast": "Contrast"
  },
  "histogram": {
    "titleText": "Parameter Distribution",
    "xAxisTitle": "Vertical axis",
    "viewType": "Angle of View",
    "centerValue": "Center Value",
    "step": "Step",
    "relativeTime": "Relative Time",
    "absoluteTime": "Absolute Time",
    "overlay": "Front",
    "offset": "Top",
    "fullScreen": "Full Screen"
  },
  "dataMap": {
    "titleText": "Data Graph"
  },
  "tensors": {
    "titleText": "Tensor",
    "dimension": "Shape:",
    "tensorType": "Data type:",
    "viewTypeTitle": "View",
    "chartViewType": "Table",
    "histogramViewType": "Histogram",
    "tensorDashboardLimitErrorMsg": "The requested data is too large. Go to the tensor page and try another dimension."
  },
  "graph": {
    "titleText": "Computational Graph",
    "downloadPic": "Download",
    "fitScreen": "Fit to Screen",
    "nodeInfo": "Node Information",
    "legend": "Legend",
    "nameSpace": "Namespace",
    "operatorNode": "Operator Node",
    "virtualNode": "Virtual Node",
    "constantNode": "Constant Node",
    "polymetric": "Aggregation Node",
    "dataFlowEdge": "Data Flow Edge",
    "controllDepEdge": "Control Dependency Edge",
    "name": "Name",
    "count": "Subnodes",
    "type": "Type",
    "attr": "Attribute",
    "inputs": "Input",
    "outputs": "Output",
    "outputs_i": "Outputs_i",
    "controlDependencies": "Control Edge",
    "searchLoading": "Locating nodes... Please wait. The locating speed depends on the number of nodes. A large number of nodes will slow down the speed.",
    "queryLoading": "Loading... Please wait.",
    "fullScreen": "Full Screen",
    "tooManyNodes": "Too many nodes to open.",
    "inputNodeName": "Enter node name",
    "guide": "User Guide",
    "guideTitle1": "Introduction 1 of 3: Main Functions",
    "guideTitle2": "Introduction 2 of 3: Node Types",
    "guideTitle3": "Introduction 3 of 3: Edges",
    "guideContent11": "1. In a computational graph display area, you can view a computational graph, zoom in or out a computational graph by scrolling the mouse wheel, and drag a computational graph.",
    "guideContent12": "2. A computational graph can be displayed in full screen or saved as an SVG file.",
    "guideContent13": "3. In the function area on the right, you can switch to view computational graphs of different files or search for nodes in a computational graph.",
    "guideContent14": "4. In the node information, you can click an input or output node to go to the selected node.",
    "guideContent2": "Node types of a computational graph include namespace node, operator node, virtual node,   aggregation node, and constant node. \"Default\" indicates forward propagation, and \"Gradients\" indicates backward propagation. ",
    "guideContent3": "Data edges and control edges exist in a computational graph. A data edge indicates the data input, and a control edge indicates the execution dependency between node.",
    "next": "Next",
    "finish": "Complete",
    "dataTooLarge": "Failed to open the graph because of too many nodes and edges.",
    "tooManyChain": "The direct subnode depth exceeds 70 and cannot be expanded."
  },
  "operator": {
    "titleText": "Profiling",
    "currentCard": "Number of cards",
    "pie": "Pie",
    "bar": "Bar",
    "allOperator": "All",
    "classificationOperator": "Type",
    "card": " ",
    "searchByType": "Enter operator type",
    "searchByName": "Enter operator name",
    "operatorInfo": "Operator",
    "kernelInfo": "Kernel",
    "searchByCoreName": "Enter kernel name",
    "searchByCoreFullName": "Enter operator full name"
  },
  "profiling": {
    "profilingDashboard": "Profiling Dashboard",
    "showAverage": "Average value",
    "iterationGapTime": "Step interval",
    "fpBpTime": "Forward and Backward Propagation",
    "tailTime": "Step Tail",
    "time": "Time",
    "operatorTimeConAnalysis": "Operator Time Consumption Analysis",
    "avgCost": "Average total consumed time:",
    "getCost": "Average data obtaining time:",
    "pushCost": "Average data push time:",
    "lterationGap": "Step Interval",
    "lterationTail": "Step Tail",
    "minddataTitle": "Data Preparation Details",
    "dataQueue": "Data Queues",
    "smartHelper": "Helper",
    "suggestions": "Suggestions",
    "common-profiler_tutorial": {
      "desc": "How Do I Use Profiler for Profiling?",
      "anchor": ["desc"],
      "url": [
        "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/performance_profiling.html"
      ],
      "gpuUrl": [
        "https://www.mindspore.cn/tutorial/training/en/master/advanced_use/performance_profiling_gpu.html"
      ]
    },
    "step_trace-proposer_type_label": {
      "desc": "Step trace performance optimization"
    },
    "step_trace-iter_interval": {
      "desc": "After the praph mode and dataset sink mode are enabled, if the average step interval is greater than {n1} ms, the process from data processing to computational graph execution can be optimized."
    },
    "common-proposer_type_label": {
      "desc": "Profiling and optimization guide"
    },
    "minddata_pipeline-proposer_type_label": {
      "desc": "Data processing performance optimization"
    },
    "minddata_pipeline-general": {
      "desc": "The {n1} operator in the pipeline may have performance bottlenecks."
    },
    "minddata_pipeline-dataset_op": {
      "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter."
    },
    "minddata_pipeline-generator_op": {
      "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter or optimize the training script. If the performance is not optimized, you can replace the operator with the MindRecordDataset operator."
    },
    "minddata_pipeline-map_op": {
      "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter. If the Python operator is used, you can optimize the training script."
    },
    "minddata_pipeline-batch_op": {
      "desc": "For operator {n1}, you can increase the prefetch_size value."
    },
    "minddata_warning_op": {
      "desc": "Based on the preceding determination, the operator {n1} can be optimized."
    },
    "minddata-proposer_type_label": {
      "desc": "Step interval profiling"
    },
    "minddata_device_queue": {
      "desc": "The ratio of empty queues on a host is {n1}/{n2}, and the ratio of full queues is {n3}/{n4}."
    },
    "minddata_get_next_queue": {
      "desc": "The ratio of empty queues on a chip is {n1}/{n2}."
    },
    "millisecond": "ms",
    "curCard": "Number of cards",
    "stepTrace": "Step Trace",
    "mindData": "Data Preparation",
    "timeLine": "Timeline",
    "rankOfOperator": "Operator Time Consumption Ranking",
    "stepTraceDetail": "Step Trace Details",
    "viewDetail": "Details",
    "stepNum": "Steps",
    "iterGapTimeLabel": "Time",
    "iterGapRateLabel": "Ratio",
    "fpBpTimeLabel": "Time",
    "fpBpRateLabel": "Ratio",
    "tailTimeLabel": "Time",
    "tailRateLabel": "Ratio",
    "operatorDetail": "Operator Details",
    "times": "times",
    "queueStep": "Queue Step Distribution",
    "queueInfo": "Step Interval",
    "pipeline": "Data Processing",
    "pipelineTopTitle": "Average usage of queues between operators",
    "pipelineMiddleTitle": "Queue relationship between operators",
    "deviceQueueOp": "Data Transmission",
    "deviceQueueOpTip": "Forward and Backward Propagation",
    "getNext": "Data Obtaining Operator",
    "connectorQuene": "Host Queues",
    "getData": "Data Obtaining",
    "opTotalTime": "Total operator execution time:",
    "streamNum": "Number of executed flows:",
    "opNum": "Number of operators:",
    "opTimes": "Total operator execution times:",
    "features": "Functions:",
    "iterationInfo": "The step trace displays the duration of each step from the start of the previous iteration to the end of the step. The main time is divided into three parts: step interval, forward and backward propagation, and step tail.",
    "iterationGapInfo": "Reads data from data queues. If this part takes a long time, you are advised to check the data processing for further analysis.",
    "fpbpTitle": "Forward and Backward Propagation",
    "fpbpInfo": "Executes the forward and backward operators on the network, which carry the main calculation work of a step. If this part takes a long time, you are advised to check the operator statistics or timeline for further analysis.",
    "iterativeTailingTitle": "Step Tail",
    "iterativeTailingInfo": "Performs parameter aggregation and update operations in multi-card scenarios. If the operations take a long time,you are advised to check the time consumed by all_reduce and the parallel status.",
    "statistics": "Statistics:",
    "totalTime": "Total consumed time:",
    "totalSteps": "Total steps:",
    "fpbpTimeRatio": "Ratio of time consumed by forward and backward propagation:",
    "iterationGapTimeRatio": "Ratio of time consumed by step interval:",
    "iterativeTailingTimeRatio": "Ratio of time consumed by step tail:",
    "dataProcess": "This shows the data processing. Data is stored in the host queue during data processing, and then stored in the data queue on a chip during data transmission. Finally, the forward and backward propagation get_next transmits the data to forward propagation.",
    "dataProcessInfo": "By determining the empty host and data queues, you can preliminarily determine the stage where the performance is abnormal.",
    "analysisOne": "1. If the step interval is long and some batches of the data queue on a chip are empty, the performance is abnormal during data processing and transmission. Otherwise, locate the internal problem of the forward and backward propagation get_next.",
    "analysisTwo": "2. If the performance is abnormal during data processing and transmission, check the host queue. If the host queue is empty at a high probability, the exception may occur during data transmission.",
    "higherAnalysis": "Note: You can perform advanced analysis based on the time consumed by operators.",
    "chipInfo": "Ratio of empty data queues on a chip:",
    "hostIsEmpty": "Ratio of empty queues on a host:",
    "hostIsFull": "Ratio of full queues on a host:",
    "operatorInfo": "Operator information of {msg1} and {msg2}",
    "workersNum": "Number of threads",
    "queueDeepChartTitle": "{msg} Depth Line Chart",
    "sampleInterval": "Sampling interval",
    "queueTip1": "Ratio of full queues:",
    "queueTip2": "Ratio of empty queues:",
    "totalCapacity": "Total capacity",
    "averageCapacity": "Average used capacity",
    "FPMessage": "FP start operator:",
    "BPMessage": "BP termination operator:",
    "approximateTime": "Total duration ≈ ",
    "stepInputTip": "Steps (an integer ranging from 1 to {max})",
    "inputError": "Input parameter error. Please enter a positive integer ranging from 1 to {max}",
    "defaultTip": "Average value (default)",
    "downloadTimeline": "Download",
    "timelineTips": {
      "title1": "The timeline function helps you analyze the training process and displays the following information:",
      "content11": "- Device(AI CPU or AI core) to which an operator is allocated for execution.",
      "content12": "- Flow tiling policy of MindSpore on the network.",
      "content13": "- Execution sequence and duration of an operator on a device.",
      "title2": "How to view the timeline details?",
      "content21": {
        "part1": "Click ",
        "part2": "Download",
        "part3": " to save a file containing the timeline information to a local host."
      },
      "content22": "View the information using either Google plug-in (chrome://tracing) or Perfetto (https://ui.perfetto.dev/#!/viewer).",
      "content23": {
        "part1": "Select one of the preceding two tools, enter its address in an address box of a browser, and press ",
        "part2": "Enter",
        "part3": ". On the page that is displayed, click ",
        "part4": "Load",
        "part5": " in the upper left corner of the tracing tool or click ",
        "part6": "Open trace file",
        "part7": " in the left pane of the Perfetto tool."
      },
      "title3": "How to use the timeline?",
      "content31": "You can analyze whether the flow tiling policy is proper and whether the step interval and tail time are too long based on the timeline information.",
      "content32": "You can also locate an operator and view and analyze its execution time."
    },
    "countUnit": "times",
    "unit": "ms/time",
    "gpuunit": "us/time",
    "chartTitle": "Average Time Consumption Ranking"
  },
  "profilingGPU": {
    "minddata_get_next_queue": {
      "desc": "The ratio of empty data queues is {n1}/{n2}."
    },
    "dataProcess": "This shows the data processing. Data is stored in the host queue during data processing, and then stored in the data queue during data transmission. Finally, the forward and backward propagation get_next transmits the data to forward propagation.",
    "dataProcessInfo": "By determining the empty host and data queues, you can preliminarily determine the stage where the performance is abnormal.",
    "analysisOne": "1. If the step interval is long and some batches of the data queue are empty, the performance is abnormal during data processing and transmission. Otherwise, locate the internal problem of the forward and backward propagation get_next.",
    "analysisTwo": "2. If the performance is abnormal during data processing and transmission, check the host queue. If the host queue is empty at a high probability, the exception may occur during data transmission.",
    "chipInfo": "Ratio of empty data queues:"
  },
  "components": {
    "summaryTitle": "Training Selection",
    "tagSelectTitle": "Tag Selection",
    "selectAll": "All",
    "tagFilterPlaceHolder": "Enter tag (regular expression supported)",
    "open": "More",
    "close": "Less",
    "gridIncorrectDataError": "A maximum of two-dimensionalarrays can be displayed.",
    "gridAccuracy": "Decimal places are reserved.",
    "inCorrectInput": "Invalid input.",
    "gridTableNoData": "No data in the table.",
    "value": "Value",
    "dimsFilterInputTitle": "Dimension Selection",
    "dimsFilterInputTip": "The dimension value can be a specific index (consistent with the Python index meaning and supporting negative signs) or a colon (:) that indicates all values of the current dimension."
  },
  "debugger": {
    "debugger": "Debugger",
    "nodeList": "Node List",
    "watchList": "Watch Point List",
    "watchPoint": "Watch Point",
    "continue": "CONTINUE",
    "pause": "PAUSE",
    "terminate": "TERMINATE",
    "selectCondition": "Select a condition",
    "inputStep": "Enter a step value (a positive integer)",
    "curHitNode": "Watch Point Hit List",
    "backstageStatus": "The backend running status is ",
    "view": "View",
    "deleteWatchpointConfirm": "Are you sure you want to delete the watchpoint?",
    "ternimateConfirm": "Are you sure you want to end the status?",
    "successCreateWP": "The watchpoint is created.",
    "successDeleteWP": "The watchpoint is deleted.",
    "pendingTips": "Waiting for training connection...",
    "nextNodeTip": "This is the last node.",
    "previousNodeTip": "This is the first node.",
    "serviceError": "Failed to connect to the backend service. Check and refresh the page.",
    "debuggerError": "Failed to connect to the backend service. Check whether the service is properly started.",
    "tolerance": "Tolerance",
    "curValue": "Current Value",
    "compareToPre": "Compare with Previous Step",
    "stepTip": "(The value 0 indicates the initial training state.)",
    "toSummeryList": "Go to Summary List",
    "clientIp": "Client IP",
    "deviceId": "Device ID",
    "currentStep": "Step",
    "step": "Step",
    "currentNode": "Current Node",
    "previousNode": "Previous Node",
    "nextNode": "Next Node",
    "tensorMsg": "Tensor Value Overview",
    "dType": "DType",
    "shape": "Shape",
    "value": "Value",
    "largeDataTip": "The requested data is too large. Try another dimension.",
    "continueTo": "Continue to"
  },
  "explain": {
    "explain": "Model Explanation",
    "explainSummary": "Explanation List",
    "explainSummaryCurrentFolder": "Root path of the explanation log:",
    "summaryPath": "Explanation Log Path",
    "title": "Saliency Map Visualization",
    "explainMethod": "Explanation Methods",
    "viewScore": "View Score",
    "fetch": "Filter",
    "minConfidence": "Probability Threshold",
    "imgSort": "Sort Images By",
    "default": "Default",
    "byProbability": "Probabilities in descending order",
    "superposeImg": "Overlay on Original Image",
    "originalPicture": "Original Image",
    "forecastTag": "Prediction Tag",
    "tag": "Tag",
    "confidence": "Probability",
    "forecastTagTip": "When the inference image has the correct tag, the following four flags are displayed in the tag row",
    "TP": "TP: indicates true positive. The tag is a positive sample, and the classification is a positive sample;",
    "FN": "FN: indicates false negative. The tag is a positive sample, and the classification is a negative sample;",
    "FP": "FP: indicates false positive. The tag is a negative sample, and the classification is a positive sample;",
    "TN": "TN: indicates true negative. The tag is a negative sample, and the classification is a negative sample;",
    "mainTipTitle": "Function description:",
    "mainTipPartOne": "This function visualizes the basis for model classification. After the to-be-explained model, the image, and the tag are selected, a contribution degree of each pixel in the original image to the selected tag is calculated by using an explanation method, and visualization is performed by using a saliency map similar to a heatmap. A brighter color indicates that the corresponding area contributes more to the selected tag of the model prediction. The darker the color, the smaller the contribution of the area to the selected tag.",
    "mainTipPartTwo": "A saliency map helps you understand the features related to the specified tag during deep neural network inference. When the inference basis and expectation of a model are different, you can debug the model by referring to the saliency map so that the model can perform inference based on proper features.",
    "mainTipPartThree": "For details about how to generate saliency maps, see section 3.2 'Local Methods' in",
    "mainTipPartFour": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9050829",
    "noExplainer": "Select Explanation Method"
  },
  "metric": {
    "scoreSystem": "Scoring System",
    "comprehensive": "Comprehensive Assessment",
    "classify": "Classification Assessment",
    "singleMethod": "Single method and multiple indicators",
    "multiMethod": "Single indicator and multiple methods",
    "interpretation": "Explanation",
    "measurement": "Measurement",
    "seeInterpretation": "You are viewing scores of the explanation method ",
    "seeMeasurement": "You are viewing scores of the measurement method ",
    "showGrade": " of different tag types",
    "evaluationScore": "Assessment Score",
    "weightAllocatgion": "Weight Configuration",
    "compositeScore": "Comprehensive Score",
    "metric": "Dimension",
    "inputWeightScore": "Enter a weight score between 0 and 1",
    "weightSumNotNull": "The sum of weights cannot be empty",
    "weightError": "Weight calculation error",
    "weightSum": "The sum of weights must be 1",
    "radarChart": "Explanation Method Comparison Radar Chart",
    "comprehensiveTooltip": "On the comprehensive assessment page, you can configure weights for different scoring dimensions to calculate the comprehensive scores of explanation methods to sort and filter the methods. Each explanation method has advantages in different scoring dimensions. You can refer to the description of each dimension and adjust weights based on the actual requirements to assess the explanation method. By default, the same weight is set for each dimension.",
    "classifyTooltip": "Classification assessment groups datasets by tag and measures the explanation methods based on data with different tags.",
    "scoreSystemtooltipOne": "Function description:",
    "scoreSystemtooltiptwo": "The scoring system provides multiple dimensions for scoring explanation methods.",
    "scoreSystemtooltipthree": "Scoring dimensions:",
    "scoreSystemtooltipfour": " - Faithfulness: evaluates the faithfulness of the explanation result to the model, that is, whether the saliency map correctly reflects the model classification basis. Modify the corresponding positions in the original image according to a certain rule (for example, from bright to dark) and transfer the modified image to the model for inference in sequence. If the brighter the area of the saliency map is, the greater the impact on the selected tag is, the higher the faithfulness of the interpretation result is. Conversely, the explanation result may not reflect the inference basis of the model. Currently, there are three mainstream solutions: naive faithfulness, insertion AUC, and deletion AUC.",
    "scoreSystemtooltipfive": "  - Localization: uses the bounding box of the dataset to assess the positioning accuracy of the highlighted area in the saliency map. Select an objective and related tag. If the explanation method accurately highlights the area related to the selected tag in the image, the highlighted area should overlap the bounding box of the selected objective. Localization assesses the location capability of the explanation result by assessing the overlapped area of the highlighted area and bounding box."
  },
  "error": {
    "50540000": "System error.",
    "50540001": "Incorrect parameter type. Check whether the request parameter types meet the requirements.",
    "50540002": "Incorrect parameter value. Check whether the request parameter values meet the requirements.",
    "50540003": "Mandatory parameters are missing. Check whether all mandatory parameters meet the requirements.",
    "50545001": "The API route resource does not exist.",
    "50545002": "Incorrect HTTP method for requesting the API.",
    "50545005": "The training job does not exist.",
    "50545007": "Loading summary data... Please wait.",
    "50545009": "The queried node is not in the graph. Please refresh.",
    "5054500A": "Failed to decode the URL of the training job ID.",
    "5054500C": "The computational graph does not exist. Please refresh.",
    "5054500D": "The image data does not exist. Please refresh.",
    "5054500E": "The scalar data does not exist. Please refresh.",
    "5054500F": "The parameter distribution data does not exist. Please refresh.",
    "50545010": "The requested data is not in the cache. Refresh.",
    "50542082": "The model name is missing.",
    "50542085": "Invalid model name.",
    "50542215": "Incorrect query parameters.",
    "50542216": "The summary log file is not found.",
    "50542217": "Incorrect summary log path.",
    "50542218": "Incorrect filtering parameter.",
    "50545012": "The tensor data does not exist. Please refresh.",
    "50545013": "The requested data is too large. Try another dimension.",
    "50545014": "The queried tensor data has been replaced by new data. Please refresh.",
    "50546083": "The profiler directory does not exist.",
    "50548001": "Ascend AI Processor information query timed out.",
    "5054B080": "Incorrect parameter type. Please check the input parameter type.",
    "5054B081": "Incorrect parameter value. Please check the input parameter.",
    "5054B180": "Failed to create the watchpoint. Please stop training and try again.",
    "5054B181": "Failed to update the watchpoint. Please stop training and try again.",
    "5054B182": "Failed to delete the watchpoint. Please stop training and try again.",
    "5054B183": "Backend training is in progress or has ended. Please try again later",
    "5054B184": "The operation is too fast, the backend service has been suspended."
  }
}