From dbc89c8680c67af9b23b707ef5fb4c6f9e7476b5 Mon Sep 17 00:00:00 2001 From: Nate Bryant Date: Mon, 23 Jan 2023 21:46:03 -0500 Subject: [PATCH] =?UTF-8?q?style(models):=20Replaces=20non-ASCII=20charact?= =?UTF-8?q?es=20in=20pdl=20files=20with=20ASCII=20c=E2=80=A6=20(#7105)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../pegasus/com/linkedin/common/MLFeatureDataType.pdl | 8 ++++---- .../com/linkedin/ml/metadata/EthicalConsiderations.pdl | 2 +- .../pegasus/com/linkedin/ml/metadata/MLModelFactors.pdl | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/li-utils/src/main/pegasus/com/linkedin/common/MLFeatureDataType.pdl b/li-utils/src/main/pegasus/com/linkedin/common/MLFeatureDataType.pdl index 23ebc3aa35..9f4c4d3b2b 100644 --- a/li-utils/src/main/pegasus/com/linkedin/common/MLFeatureDataType.pdl +++ b/li-utils/src/main/pegasus/com/linkedin/common/MLFeatureDataType.pdl @@ -12,7 +12,7 @@ enum MLFeatureDataType { USELESS /** - * Nominal data is made of discrete values with no numerical relationship between the different categories — mean and median are meaningless. + * Nominal data is made of discrete values with no numerical relationship between the different categories - mean and median are meaningless. * Animal species is one example. For example, pig is not higher than bird and lower than fish. */ NOMINAL @@ -24,19 +24,19 @@ enum MLFeatureDataType { ORDINAL /** - * Binary data is discrete data that can be in only one of two categories — either yes or no, 1 or 0, off or on, etc + * Binary data is discrete data that can be in only one of two categories - either yes or no, 1 or 0, off or on, etc */ BINARY /** - * Count data is discrete whole number data — no negative numbers here. + * Count data is discrete whole number data - no negative numbers here. * Count data often has many small values, such as zero and one. */ COUNT /** * Time data is a cyclical, repeating continuous form of data. - * The relevant time features can be any period— daily, weekly, monthly, annual, etc. + * The relevant time features can be any period- daily, weekly, monthly, annual, etc. */ TIME diff --git a/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/EthicalConsiderations.pdl b/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/EthicalConsiderations.pdl index 8bb0b494d6..1e77af3ce9 100644 --- a/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/EthicalConsiderations.pdl +++ b/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/EthicalConsiderations.pdl @@ -14,7 +14,7 @@ record EthicalConsiderations { data: optional array[string] /** - * Is the MLModel intended to inform decisions about matters central to human life or flourishing – e.g., health or safety? Or could it be used in such a way? + * Is the MLModel intended to inform decisions about matters central to human life or flourishing - e.g., health or safety? Or could it be used in such a way? */ humanLife: optional array[string] diff --git a/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/MLModelFactors.pdl b/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/MLModelFactors.pdl index 5495ade1fd..0a506742e6 100644 --- a/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/MLModelFactors.pdl +++ b/metadata-models/src/main/pegasus/com/linkedin/ml/metadata/MLModelFactors.pdl @@ -13,7 +13,7 @@ record MLModelFactors { /** * The performance of a MLModel can vary depending on what instruments were used to capture the input to the MLModel. - * For example, a face detection model may perform differently depending on the camera’s hardware and software, + * For example, a face detection model may perform differently depending on the camera's hardware and software, * including lens, image stabilization, high dynamic range techniques, and background blurring for portrait mode. */ instrumentation: optional array[string]