VideoContentSearchDolphinScoringConfig

AI Overview😉

  • The potential purpose of this module is to configure and fine-tune the scoring of video content search results using a machine learning model called "Dolphin". This model appears to assess the descriptiveness and usefulness of video content, potentially to improve the relevance and ranking of search results.
  • This module could impact search results by influencing the ranking of videos based on their descriptiveness and usefulness. This could lead to more informative and engaging videos being promoted to the top of search results, while less descriptive or useful videos are demoted. The ensemble model and inference batch size settings may also affect the speed and accuracy of the scoring process.
  • To be more favorable for this function, a website could focus on creating high-quality, descriptive, and engaging video content that provides value to users. This could include using descriptive titles, tags, and descriptions, as well as optimizing video thumbnails and content to make them more informative and appealing. Additionally, websites could consider using structured data and schema markup to provide search engines with more context about their video content, which could improve its descriptiveness and usefulness scores.

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GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchDolphinScoringConfig (google_api_content_warehouse v0.4.0)

Attributes

  • descriptivenessOutputKey (type: String.t, default: nil) - The output put keys for Dolphin PredictResponse
  • ensembleModelNames (type: list(String.t), default: nil) - If the dolphin model is an ensemble model (e.g. Video QnA model which consists of 4 teacher models), stores each individual model name.
  • inferenceBatchSize (type: integer(), default: nil) - The inference batch size to use for inference methods that handle batching.
  • inferenceMethod (type: String.t, default: nil) - The method to use for inference. This must be set or inference will fail.
  • maxRpcRetries (type: integer(), default: nil) - Holds value of flag --max_rpc_retries.
  • modelName (type: String.t, default: nil) - Model name used for ModelSpec in PredictRequest used in the PredictionService API.
  • modelPath (type: String.t, default: nil) - Only used when using the bulk_inference API. See go/dolphin-models to learn about the different dolphin models.
  • outputKey (type: String.t, default: nil) - TODO(alexiaxu) To deprecate this field in the future Output key for Dolphin PredictResponse.
  • rpcDeadlineSeconds (type: float(), default: nil) - Holds value of flag --rpc_deadline (converted to seconds).
  • serviceBns (type: String.t, default: nil) - Tensorflow inference BNS address when using PredictionService API.
  • usefulnessOutputKey (type: String.t, default: nil) -

Summary

Types

t()

Functions

decode(value, options)

Unwrap a decoded JSON object into its complex fields.

Types

Link to this type

t()

@type t() ::
  %GoogleApi.ContentWarehouse.V1.Model.VideoContentSearchDolphinScoringConfig{
    descriptivenessOutputKey: String.t() | nil,
    ensembleModelNames: [String.t()] | nil,
    inferenceBatchSize: integer() | nil,
    inferenceMethod: String.t() | nil,
    maxRpcRetries: integer() | nil,
    modelName: String.t() | nil,
    modelPath: String.t() | nil,
    outputKey: String.t() | nil,
    rpcDeadlineSeconds: float() | nil,
    serviceBns: String.t() | nil,
    usefulnessOutputKey: String.t() | nil
  }

Functions

Link to this function

decode(value, options)

@spec decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.