vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Understanding grammars 

The following examples introduce the IBM Watson® Speech to Text service's support for grammars. The examples create two simple ABNF grammars and show possible results when they are used for speech recognition. The examples illustrate the importance of examining the confidence score that the service includes with a transcript.

The examples provide only the results of speech recognition requests. For examples that show how to pass a grammar for speech recognition, see [Using a grammar for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUse). The examples are also very basic. For examples of more complex grammars, see [Example grammars](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarExamples).

For more information about the languages and models that support grammars and their level of support (generally available or beta), see [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support).



 Single-phrase matches: The yesno grammar 

The first example defines a very simple yesno grammar that accepts two valid single-word responses, yes and no. The grammar is useful in cases where the user must respond with only one of the two phrases.

ABNF 1.0 ISO-8859-1;
language en-US;
mode voice;
root $yesno;

$yesno = yes | no ;

When you apply this grammar to a speech recognition request, the service can return a transcript with a score that indicates its confidence in the match. It can also return no result if the input clearly does not match one of the two phrases.

For instance, if the user replies yes, the service likely returns a response that is very much like the following result. The score in the confidence field indicates a perfectly reliable match.

{
"result_index": 0,
"results": [
{
"alternatives":
{
"confidence": 1.0,
"transcript": "yes"
}
satisfied An affective response to perceived service quality 
 sympathetic An affective mode of understanding that involves emotional resonance 





* Example response:

{
"usage": {
"text_units": 1,
"text_characters": 60,
"features": 1
},
"language": "en",
"classifications": [
{
"confidence": 0.564849,
"class_name": "excited"
},
{
"confidence": 0.355816,
"class_name": "satisfied"
},
{
"confidence": 0.126127,
"class_name": "polite"
},
{
"confidence": 0.026995,
"class_name": "sympathetic"
},
{
"confidence": 0.012211,
"class_name": "frustrated"
},
{
"confidence": 0.011065,
"class_name": "sad"
},
{
"confidence": 0.000872,
"class_name": "impolite"
}
]
}
Show more









 Migrating from Watson Tone Analyzer Customer Engagement endpoint to Natural Language Understanding 

You can migrate your [Watson Tone Analyzer customer-engagement](https://cloud.ibm.com/docs/tone-analyzer?topic=tone-analyzer-utco) analysis requests to Natural Language Understanding. This can help you better understand your interactions with customers and improve your communications generally, or for specific customers.



 Reformatting your input data 

In Watson Tone Analyzer, you pass the /v3/tone_chat method a JSON ToneChatInput object consisting of utterances, text, and an optional user string fields. For Natural Language Understanding, you pass a JSON object that contains text to be analyzed, and a language-specific model classification ID, to the /v1/analyze method.

!
For example, if the metrics_filter section of a monitoring agent looks as follows:

metrics_filter:
- include: metricA.
- exclude: metricA.
- include: metricB.
- include: haproxy.backend.
- exclude: haproxy.
- exclude: metricC.



* You are configuring the monitoring agent to collect all data from metrics that start with metricA, metricB, and haproxy.backend.
* You are filtering out metrics that start with metricC and other metrics that start with haproxy.
* The entry exclude: metricA. is ignored.



4. Save the changes.



Changes are applied automatically.





 Filtering kubernetes objects and containers from which data is collected 

A Kubernetes monitoring agent automatically collects metrics from all containers that it detects in a cluster, including Prometheus, StatsD, JMX, app-checks, and built-in metrics.

You can customize the monitoring agent to exclude containers from metrics collection.

When you exclude containers, consider the following information:



* You reduce agent and backend load.
* You only collect data from containers that you want to monitor.
* You can control costs by reporting on important containers, and filtering out unnecessary or not critical containers.



To enable the feature where a monitoring agent filters containers, you must customize the sysdig-agent-configmap.yaml file. Set the use_container_filter entry in the containers section to true. Note: By default, this feature is turned off. Then, define the rules that include one or more conditions and that you want to apply.

The following table outlines the parameters that you can define to set the filtering rules in a cluster:



Table 1. Parameters to define conditions on containers

 Parameter Condition 

 container.image Container image name 
 container.name Container name 
 container.label.* Container label 
 kubernetes.object.* Kubernetes object. An object can be a pod, a namespace, etc. 
 kubernetes.object.annotation.* Kubernetes object annotation
The model learns from the fact that the word visit in the same example is not annotated.

![Visit unselected](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/oe-counter3.png)









 Enabling system entities 

Watson Assistant provides a number of system entities, which are common entities that you can use for any application. Enabling a system entity makes it possible to quickly populate your skill with training data that is common to many use cases.

System entities can be used to recognize a broad range of values for the object types they represent. For example, the @sys-number system entity matches any numerical value, including whole numbers, decimal fractions, or even numbers written out as words.

System entities are centrally maintained, so any updates are available automatically. You cannot modify system entities.



1. On the Entities page, click System entities.

![Screen capture of "System entities" tab](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/system-entities1.png)
2. Browse through the list of system entities to choose the ones that are useful for your application.



* To see more information about a system entity, including examples of matching input, click the entity in the list.
* For details about the available system entities, see [System entities](https://cloud.ibm.com/docs/assistant?topic=assistant-system-entities).



3. Set the switch for each system entity that you want to use to On.



After you enable system entities, Watson Assistant begins to retrain. After training is complete, you can use the entities.





 Entity limits 

The number of entities, entity values, and synonyms that you can create depends on your Watson Assistant service plan:



Plan details

 Plan Entities per skill Entity values per skill Entity synonyms per skill
Note that the Watson Assistant service learning models may have been updated as part of this enhancement, and when you retrain your model any changes will be applied.





 3 October 2017 

Pattern-defined entities (Beta)
: You can now define specific patterns for an entity, using regular expressions. This can help you identify entities that follow a defined pattern, for example SKU or part numbers, phone numbers, or email addresses. See [Pattern-defined entities](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-patterns) for additional details.



* You can add either synonyms or patterns for a single entity value; you cannot add both.
* For each entity value, there can be a maximum of up to 5 patterns.
* Each pattern (regular expression) is limited to 128 characters.
* Importing or exporting via a CSV file does not currently support patterns.
* The REST API does not support direct access to patterns, but you can retrieve or modify patterns using the /values endpoint.



Fuzzy matching filtered by dictionary (English only)
: An improved version of fuzzy matching for entities is now available, for English. This improvement prevents the capturing of some common, valid English words as fuzzy matches for a given entity. For example, fuzzy matching will not match the entity value like to hike or bike, which are valid English words, but will continue to match examples such as lkie or oike.





 27 September 2017 

Condition builder updates
: The control that is displayed to help you define a condition in a dialog node has been updated. Enhancements include support for listing available context variable names after you enter the $ to begin adding a context variable.





 31 August 2017 

Improve section rollback
: The median conversation time metric, and corresponding filters, are being temporarily removed from the Overview page of the Improve section. This removal will prevent the calculation of certain metrics from causing the median conversation time metric, and the conversations over time graph, to display inaccurate information. IBM regrets removing functionality from the tool, but is committed to ensuring that we are communicating accurate information to users.

Dialog node names
: You can now assign any name to a dialog node; it does not need to be unique.
Similarly, when someone with Manager service access to an instance sends a GET request to the v1 /logs API endpoint, logs from all instances that participate in log sharing are returned, regardless of the user's service level access to each instance.







 Making training data improvements 

Use insights from real user conversations to correct the model associated with your dialog skill.

If you use data from another data source, any improvements you make to the model are applied to the current dialog skill only. The Data source field shows the source of the messages you are using to improve this dialog skill, and the header of the page shows the dialog skill you are applying changes to.



 Correcting an intent 



1. To correct an intent, select the ![Edit](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/edit_icon.png) edit icon for the chosen #intent.
2. From the list provided, select the correct intent for this input.



* Begin typing in the entry field and the list of intents is filtered.
* You can also choose Mark as irrelevant from this menu. (For more information, see [Teaching your assistant about topics to ignore](https://cloud.ibm.com/docs/assistant?topic=assistant-logslogs-mark-irrelevant).) Or, you can choose Do not train on intent, which does not save this message as an example for training.



![Select intent](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/select_intent.png)
3. Select Save.

!
: The [categories stock model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-categories-hierarchy) has been updated to use the [IAB Tech Lab 2.0 taxonomy](https://iabtechlab.com/standards/content-taxonomy/).





 5 May 2021 

Advanced Rules beta feature deprecated
: The advanced rules beta feature is deprecated. As of June 10, 2021, you will not be able to deploy advanced rules models to Natural Language Understanding; existing models will keep running. After June 24, 2021, no advanced rules models will run in Natural Language Understanding.





 25 March 2021 

Custom classifications beta feature
: The [custom classifications (Beta)](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-classifications) feature allows you to train a multi-label text classifier (English-only) by using your own labeled data.





 18 February 2021 

Custom categories beta feature
: The [custom categories (Beta)](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-categories) feature allows you to train custom categories models (English only) with service instances deployed in the Dallas location.





 December 2020 



 9 December 2020 

Version 2 entity support for English
: Version 2 entity type support is now available, for all public and premium service instances, for English language. For details, see [Entity type systems](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-entity-type-systems).





 2 December 2020 

Version 2 entity support for Korean and Spanish
: Version 2 entity type support is now available, for all public and premium service instances, for the following languages: Korean and Spanish. For details, see [Entity type systems](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-entity-type-systems).







 November 2020 



 12 November 2020 

Entity support for Arabic, Chinese (Simplified), and Dutch
In the following example, you are verifying that the mean latency of your application is under 200.0 ms, the error rate is under 1% (you can make this 0.0), and the 95th percentile tail latency is under 500.0 ms.

docker exec ind helm install 
--set URL=<URL-OF-YOUR-APPLICATION> 
--set LimitMeanLatency=200.0 
--set LimitErrorRate=0.01 
--set Limit95thPercentileLatency=500.0 
codeengine /iter8/helm/conformance

Example output

NAME: codeengine
LAST DEPLOYED: Wed Jun 30 01:10:14 2021
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None





 Step 5: Getting the results of the SLO validation 

The following command outputs the result of the SLO validation for your Code Engine application.

docker exec ind 
bash -c "kubectl get experiment my-experiment -o yaml | iter8ctl describe -f -"

Example output

If you do not see output similar to the following example, you may need to wait a little longer and try the previous command again. The objectives section reports if your application is satisfying the specified SLOs. The metrics section reports the metrics observed for your application by Iter8.

 Overview 
Experiment name: my-experiment
Experiment namespace: default
Target: my-app
Testing pattern: Conformance
Deployment pattern: Progressive

 Progress Summary 
Experiment stage: Completed
Number of completed iterations: 1

 Winner Assessment 
> If the version being validated; i.e., the baseline version, satisfies the experiment objectives, it is the winner.
> Otherwise, there is no winner.
Winning version: my-app

 Objective Assessment 
> Identifies whether or not the experiment objectives are satisfied by the most recently observed metrics values for each version.
<metric category="config" context="host" device-id="" last-refresh="1607451781" refresh-interval="0" type="string" unit="none">
<name>Virtualization Solution</name>
<value>KVM</value>
</metric>
.
.
.
</metrics>



You might experience a delay before your data is available.





 Troubleshooting 

Use the following troubleshooting tips for IMCS.



 Uninstalling the Metrics Collector 



1. Run the following command to uninstall IMCS if you have any issues during the installation process. Then, reinstall it.

./uninstall-linux.sh

Removing IBM Metric Collector for SAP...
Successfully removed IBM Metric Collector for SAP.







 No metrics reported when you run the curl command 

No reported metrics message is often due to the port not assigned to SAP Metrics Collector. It needs port 18181 available for localhost. If you have any other applications that use the port, you must close the applications.



1. Use the following command to see whether the port is assigned to another application.

nmap -sT -O localhost

Starting Nmap 6.40 (http://nmap.org) at (date and time)
Nmap scan report for localhost (your localhost address)
Host is up (0.0s latency).
Other addresses for localhost (not scanned): (localhost addresses)
rDNS record for (localhost): sap-mc-redhat
Not shown: (number of) closed ports
PORT STATE SERVICE
(port)/tcp open ssh
(port)/tcp open smtp
Device type: general purpose
Running: Linux 3.X
OS CPE: cpe:/o:linux:linux_kernel:3
OS details: Linux 3.7 3.9
Network Distance: 0 hops







 nmap not found
As a part of the update, dialog skill error handling has been improved within the "Try it out" pane. This update was enabled on September 9, 2021 in the Tokyo and Seoul data centers. On September 13, 2021, the update was enabled in the London, Sydney, and Washington, D.C. data centers.

Disambiguation feature updates
: The dialog skill disambiguation feature now includes improved features:



* Increased control: The frequency and depth of disambiguation can now be controlled by using the sensitivity parameter in the [workspace API](https://cloud.ibm.com/apidocs/assistant/assistant-v1updateworkspace). There are 5 levels of sensitivity:



* high
* medium_high
* medium
* medium_low
* low



The default (auto) is medium_high if this option is not set.
* More predictable: The new disambiguation feature is more stable and predictable. The choices shown may sometimes vary slightly to enable learning and analytics, but the order and depth of disambiguation is largely stable.



These new features may affect various metrics, such as disambiguation rate and click rates, as well as influence conversation-level key performance indicators such as containment.

If the new disambiguation algorithm works differently than expected for your assistant, you can adjust it using the sensitivity parameter in the update workspace API. For more information, see [Update workspace](https://cloud.ibm.com/apidocs/assistant/assistant-v1updateworkspace).





 9 September 2021 

Actions skill improvements
: Actions skills now include these new features:



* Change conversation topic: In general, an action is designed to lead a customer through a particular process without any interruptions. In real life, however, conversations almost never follow such a simple flow. In the middle of a conversation, customers might get distracted, ask questions about related issues, misunderstand something, or just change their minds about what they want to do. The Change conversation topic feature enables your assistant to handle these digressions, dynamically responding to the user by changing the conversation topic as needed.
The National Institute of Neurological Disorders and Stroke NINDS conducts spinal cord research in its laboratories at the National Institutes of Health NIH.
NINDS also supports additional research through grants to major research institutions across the country.
Some of the more promising rehabilitation techniques are helping spinal cord injury patients become more mobile.
What is Osteogenesis imperfecta OI?
. . .
Show more

Speech recognition relies on statistical algorithms to analyze audio. Words from a custom model are in competition with words from the service's base vocabulary as well as other words of the model. (Factors such as audio noise and speaker accents also affect the quality of transcription.)

The accuracy of transcription can depend largely on how words are defined in a model and how speakers say them. To improve the service's accuracy, use corpora to provide as many examples as possible of how OOV words are used in the domain. Repeating the OOV words in corpora can improve the quality of the custom language model. How you duplicate the words in corpora depends on how you expect users to say them in the audio that is to be recognized. The more sentences that you add that represent the context in which speakers use words from the domain, the better the service's recognition accuracy.

The service does not apply a simple word-matching algorithm. Its transcription depends on the context in which words are used. When it parses a corpus, the service includes information about n-grams (bi-grams, tri-grams, and so on) from the sentences of the corpus in the custom model. This information helps the service transcribe audio with greater accuracy, and it explains why training a custom model on corpora is more valuable than training it on custom words alone.

For example, accountants adhere to a common set of standards and procedures that are known as Generally Accepted Accounting Principles (GAAP). When you create a custom model for a financial domain, provide sentences that use the term GAAP in context. The sentences help the service distinguish between general phrases such as "the gap between them is small" and domain-centric phrases such as "GAAP provides guidelines for measuring and disclosing financial information."

In general, it is better for corpora to use words in different contexts, which can improve how the service learns the words.
Classifications X* X 
 Concepts X 
 Emotion X 
 Entities X X 
 Keywords X 
 Metadata X 
 Relations X X 
 Semantic roles 
 Sentiment X 
 Syntax X 



* Indicates support for tone analysis; see [Tone analytics (Classifications)](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-tone_analytics) for more information.





 German 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories X 
 Classifications X 
 Concepts X 
 Emotion 
 Entities X X 
 Keywords X 
 Metadata X 
 Relations X X 
 Semantic roles X 
 Sentiment X 
 Syntax X 







 Hebrew 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories 
 Classifications 
 Concepts 
 Emotion 
 Entities X* 
 Keywords X 
 Metadata 
 Relations 
 Semantic roles 
 Sentiment X 
 Syntax X 



* Relevance ranking is not supported.





 Hindi 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories 
 Classifications 
 Concepts 
 Emotion 
 Entities X* 
 Keywords X 
 Metadata 
 Relations 
 Semantic roles 
 Sentiment X 
 Syntax X 



* Relevance ranking is not supported.





 Italian 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories X 
 Classifications X 
 Concepts X 
 Emotion 
 Entities X X 
 Keywords X 
 Metadata X 
 Relations X X 
 Semantic roles 
 Sentiment X 
 Syntax X 







 Japanese 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support
Host Port: 0/TCP
Command:
/metrics-server
--metric-resolution=45s
--secure-port=4443
--tls-cert-file=/etc/metrics-server-certs/tls.crt
--tls-private-key-file=/etc/metrics-server-certs/tls.key
State: Running
Started: Fri, 10 Sep 2021 17:31:39 +0000
Last State: Terminated
Reason: OOMKilled
Exit Code: 137
Started: Fri, 10 Sep 2021 05:59:51 +0000
Finished: Fri, 10 Sep 2021 17:31:37 +0000
Ready: True
Restart Count: 36

If the Last State shows a Reason of OOMKilled, increase the memory requests in the metrics-server-config ConfigMap in 100Mi increments or larger until the metrics-server is stable and runs for several hours or longer without being OOMkilled.

Last State: Terminated
Reason: OOMKilled
Exit Code: 137

If the Last state shows a shows a Reason of Error and Events such as those in the following example, increase the CPU requests in the metrics-server-config ConfigMap in 100m increments or larger until the metrics-server is stable and runs for several hours or longer without being killed due to probe timeouts.

Last State: Terminated
Reason: Error
Exit Code: 137

Events:
Warning Unhealthy 46m (x5 over 80m) kubelet Liveness probe failed: Get "https://198.18.68.236:4443/livez": context deadline exceeded (Client.Timeout exceeded while awaiting headers)
Warning Unhealthy 26m (x65 over 89m) kubelet Liveness probe failed: Get "https://198.18.68.236:4443/livez": net/http: TLS handshake timeout