Merge pull request #3934 from MicrosoftDocs/main

04/04/2025 AM Publishing

Author: v-alje

articles/ai-services/language-service/conversational-language-understanding/how-to/view-model-evaluation.md

@@ -16,7 +16,7 @@ ms.custom: language-service-custom-classification
 After model training is completed, you can view your model details and see how well it performs against the test set. 
 
 > [!NOTE]
-> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaulation is calcualted on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
+> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaluation is calculated on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
 
 ## Prerequisites
 

articles/ai-services/language-service/custom-named-entity-recognition/faq.md

@@ -42,7 +42,7 @@ When you're ready to start [using your model to make predictions](#how-do-i-use-
 
 ## What is the recommended CI/CD process?
 
-You can train multiple models on the same dataset within the same project. After you have trained your model successfully, you can [view its performance](how-to/view-model-evaluation.md). You can [deploy and test](quickstart.md#deploy-your-model) your model within [Language studio](https://aka.ms/languageStudio). You can add or remove labels from your data and train a **new** model and test it as well. View [service limits](service-limits.md)to learn about maximum number of trained models with the same project. When you [train a model](how-to/train-model.md), you can determine how your dataset is split into training and testing sets. You can also have your data split randomly into training and testing set where there is no guarantee that the reflected model evaluation is about the same test set, and the results are not comparable. It's recommended that you develop your own test set and use it to evaluate both models so you can measure improvement.
+You can train multiple models on the same dataset within the same project. After you have trained your model successfully, you can [view its performance](how-to/view-model-evaluation.md). You can [deploy and test](quickstart.md#deploy-your-model) your model within [Language studio](https://aka.ms/languageStudio). You can add or remove labels from your data and train a **new** model and test it as well. View [service limits](service-limits.md) to learn about maximum number of trained models with the same project. When you [train a model](how-to/train-model.md), you can determine how your dataset is split into training and testing sets. You can also have your data split randomly into training and testing set where there is no guarantee that the reflected model evaluation is about the same test set, and the results are not comparable. It's recommended that you develop your own test set and use it to evaluate both models so you can measure improvement.
 
 ## Does a low or high model score guarantee bad or good performance in production?
 

articles/ai-services/language-service/custom-named-entity-recognition/how-to/view-model-evaluation.md

@@ -17,7 +17,7 @@ ms.custom: language-service-custom-ner
 After your model has finished training, you can view the model performance and see the extracted entities for the documents in the test set. 
 
 > [!NOTE]
-> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from the data. To make sure that the evaulation is calcualted on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Test** documents when [labeling data](tag-data.md).
+> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from the data. To make sure that the evaluation is calculated on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Test** documents when [labeling data](tag-data.md).
 
 ## Prerequisites
 

articles/ai-services/language-service/custom-text-classification/how-to/tag-data.md

@@ -80,7 +80,7 @@ Use the following steps to label your data:
 7. In the bottom section of the right side pane you can add the current file you are viewing to the training set or the testing set. By default all the documents are added to your training set. Learn more about [training and testing sets](train-model.md#data-splitting) and how they are used for model training and evaluation.
 
     > [!TIP]
-    > If you are planning on using **Automatic** data spliting use the default option of assigning all the documents into your training set.
+    > If you are planning on using **Automatic** data splitting use the default option of assigning all the documents into your training set.
 
 8. Under the **Distribution** pivot you can view the distribution across training and testing sets. You have two options for viewing:
    * *Total instances* where you can view count of all labeled instances of a specific class.

articles/ai-services/language-service/orchestration-workflow/how-to/view-model-evaluation.md

@@ -16,7 +16,7 @@ ms.custom: language-service-custom-classification
 After model training is completed, you can view your model details and see how well it performs against the test set. Observing how well your model performed is called evaluation. The test set consists of data that wasn't introduced to the model during the training process.
 
 > [!NOTE]
-> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaulation is calcualted on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
+> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaluation is calculated on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
 
 
 ## Prerequisites

articles/ai-services/language-service/personally-identifiable-information/language-support.md

@@ -190,7 +190,7 @@ Use this article to learn which natural languages are supported by the text PII,
 
 ## PII language support
 
-The Generally Available Conversational PII serivce currently supports English. Preview model version `2023-04-15-preview` supports English, German, Spanish, and French. 
+The Generally Available Conversational PII service currently supports English. Preview model version `2023-04-15-preview` supports English, German, Spanish, and French. 
 
 
 ---

articles/ai-services/metrics-advisor/how-tos/configure-metrics.md

@@ -107,7 +107,7 @@ There are additional parameters like **Direction**, and **Valid anomaly** that c
 
 ### Tune the configuration for a specific series or group
 
-Select **Advanced configuration** below the metric level configuration options to see the group level configuration.You can add a configuration for an individual series, or group of series by clicking the **+** icon in this window. The parameters are similar to the metric-level configuration parameters, but you might need to specify at least one dimension value for a group-level configuration to identify a group of series. And specify all dimension values for series-level configuration to identify a specific series. 
+Select **Advanced configuration** below the metric level configuration options to see the group level configuration. You can add a configuration for an individual series, or group of series by clicking the **+** icon in this window. The parameters are similar to the metric-level configuration parameters, but you might need to specify at least one dimension value for a group-level configuration to identify a group of series. And specify all dimension values for series-level configuration to identify a specific series. 
 
 This configuration will be applied to the group of series or specific series instead of the metric level configuration. After setting the conditions for this group, save it.
 

articles/ai-services/openai/concepts/gpt-4-v-prompt-engineering.md

@@ -67,7 +67,7 @@ When you receive a refusal output from a model, there are several ways you can t
 
 |Basic prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/border-collie.jpg" lightbox="../media/concepts/gpt-v/border-collie.jpg" alt-text="Photograph of a border collie dog laying in a grass field."::: What is this dog breed and what is its average weight and height? | `The dog in the image appears to be a Border Collie. Border Collies are known for their intelligence, agility, and herding abilities.The average weight for a Border Collie is typically around 30-45 pounds (14-20 kilograms). As for height, Border Collies generally stand about 18-22 inches (46-56 centimeters) tall at the shoulder. These figures can vary based on the individual dog's genetics, diet, and level of activity. Border Collies are medium-sized dogs with a well-proportioned body and a high level of energy.` |
+| :::image type="content" source="../media/concepts/gpt-v/border-collie.jpg" lightbox="../media/concepts/gpt-v/border-collie.jpg" alt-text="Photograph of a border collie dog laying in a grass field."::: What is this dog breed and what is its average weight and height? | `The dog in the image appears to be a Border Collie. Border Collies are known for their intelligence, agility, and herding abilities. The average weight for a Border Collie is typically around 30-45 pounds (14-20 kilograms). As for height, Border Collies generally stand about 18-22 inches (46-56 centimeters) tall at the shoulder. These figures can vary based on the individual dog's genetics, diet, and level of activity. Border Collies are medium-sized dogs with a well-proportioned body and a high level of energy.` |
 
 |Updated prompt|Model output|
 |--------|-----------|

articles/ai-services/openai/how-to/chat-markup-language.md

@@ -140,7 +140,7 @@ You can also include relevant data or information in the system message to give
 
 ```
 <|im_start|>system
-Assistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Serivce. Only answer questions using the context below and if you're not sure of an answer, you can say "I don't know".
+Assistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Service. Only answer questions using the context below and if you're not sure of an answer, you can say "I don't know".
 
 Context:
 - Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series.

articles/ai-services/openai/includes/chat-completion.md

@@ -187,7 +187,7 @@ Instructions:
 You can also include relevant data or information in the system message to give the model extra context for the conversation. If you need to include only a small amount of information, you can hard code it in the system message. If you have a large amount of data that the model should be aware of, you can use [embeddings](../tutorials/embeddings.md?tabs=command-line) or a product like [Azure AI Search](https://techcommunity.microsoft.com/t5/ai-applied-ai-blog/revolutionize-your-enterprise-data-with-chatgpt-next-gen-apps-w/ba-p/3762087) to retrieve the most relevant information at query time.
 
 ```
-{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Serivce. Only answer questions using the context below and if you're not sure of an answer, you can say 'I don't know'.
+{"role": "system", "content": "Assistant is an intelligent chatbot designed to help users answer technical questions about Azure OpenAI Service. Only answer questions using the context below and if you're not sure of an answer, you can say 'I don't know'.
 
 Context:
 - Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series.