Environment MacOC 26 Xcode Version 26.0 beta 7 (17A5305k) simulator: iPhone 16 pro iOS: iOS 26 Problem NLContextualEmbedding.load() fails with the following error In simulator Failed to load embedding from MIL representation: filesystem error: in create_directories: Permission denied ["/var/db/com.apple.naturallanguaged/com.apple.e5rt.e5bundlecache"] filesystem error: in create_directories: Permission denied ["/var/db/com.apple.naturallanguaged/com.apple.e5rt.e5bundlecache"] Failed to load embedding model 'mul_Latn' - '5C45D94E-BAB4-4927-94B6-8B5745C46289' assetRequestFailed(Optional(Error Domain=NLNaturalLanguageErrorDomain Code=7 "Embedding model requires compilation" UserInfo={NSLocalizedDescription=Embedding model requires compilation})) in #Playground I'm new to this embedding model. Not sure if it's caused by my code or environment. Code snippet import Foundation import NaturalLanguage import Playgrounds #Playground { // Prefer initializing by script for broader coverage; returns NLContextualEmbedding? guard let embeddingModel = NLContextualEmbedding(script: .latin) else { print("Failed to create NLContextualEmbedding") return } print(embeddingModel.hasAvailableAssets) do { try embeddingModel.load() print("Model loaded") } catch { print("Failed to load model: \(error)") } }

Hi, I wanted to know what is the best way to detect whether a part of string has an unavailable character, '□' (tofu box or last resort character). So far it seems to be that we will have to parse all the strings and individually check for each character and whether or not it is a part of the Unicode Scalar. And since we are a business application that deals with a lot of data as strings, this will be rather performance heavy. So wanted to know if there were any other better or more efficient ways to go about this?

When calling NLTagger.requestAssets with some languages, it hangs indefinitely both in the simulator and a device. This happens consistently for some languages like greek. An example call is NLTagger.requestAssets(for: .greek, tagScheme: .lemma). Other languages like french return immediately. I captured some logs from Console and found what looks like the repeated attempts to download the asset. I would expect the call to eventually terminate, either loading the asset or failing with an error.

In an under-development MacOS & iOS app, I need to identify various measurements from OCR'ed text: length, weight, counts per inch, area, percentage. The unit type (e.g. UnitLength) needs to be identified as well as the measurement's unit (e.g. .inches) in order to convert the measurement to the app's internal standard (e.g. centimetres), the value of which is stored the relevant CoreData entity. The use of NLTagger and NLTokenizer is problematic because of the various representations of the measurements: e.g. "50g.", "50 g", "50 grams", "1 3/4 oz." Currently, I use a bespoke algorithm based on String contains and step-wise evaluation of characters, which is reasonably accurate but requires frequent updating as further representations are detected. I'm aware of the Python SpaCy model being capable of NER Measurement recognition, but am reluctant to incorporate a Python-based solution into a production app. (ref [https://developer.apple.com/forums/thread/30092]) My preference is for an open-source NER Measurement model that can be used as, or converted to, some form of a Swift compatible Machine Learning model. Does anyone know of such a model?

Hello Apple, i've been using ios for many years and never had any issues with urdu language keyboard, but since the new 18.4 beta update some words are not working correctly as it should like a name of my friend who's name is "راعنیہ" but the new updated version cannot type is together and keep seperating like "راعنی ہ" its so frustrating to use like that and its not just one but so many other words that it just cannot do properly also the new font and no gap concept its hurting my eyes so much while reading or even typing.. i hope apple fixes that asap.. thankyou