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Hello there,  Does Scribble on iPadOS 14 support languages other than English? I wanted to have feature for Hindi. If not currently, does Apple plan on supporting other languages for Scribble in the future? I think that would be a great addition.  Thank you.

Hello, I am dabbing my feet in core ML and I am not so sure about the best way to tackle a language processing issue. I need to tokenize based on multiple words items. IE: "I like Ferrari Testarossa" In order to match "Ferrari Testarossa" I have tried two segmentation strategies: ["I","like","Ferrari Testarossa"] ["person", "like", "car"] and ["I","like","Ferrari", "Testarossa"] ["person", "like", "car", "car"] After generating the model, I realized that NSLinguisticTagger can only divide into words OR sentences (or bigger), nothing in between. So with either strategies, If I type "I love Lamborghini Contact" I get ["I","love","Lamborghini","Contact"] ["person", "like","car","car"] The first segmentation strategy (put tokens with 2 words in the same item) would be more suited to my UX, but I can deal with the second, it is quite trivial to join tags. I was just wondering, which is the best way to feed the model generator, Machine Learning-wise? Is there a difference in feeding the model "Ferrari Testarossa" and "Ferrari", "Testarossa" ?

After updating the iPad IOS 14.5 beta 2, logitech slim folio pro does not write in Latvian (before no problems). In the past sometimes wrote in "Notes" Latvian, but did not write in Safari and other places.

Is it possible to analyze and rate sentiment in a sentence with Create ML? How can I do this? Example: I like this game 👍 5/10 I love this game 👍 9/10

I wanted to find linguists here who may also be developers in order to get an app onto the market that allows language teachers to create and edit (syntax) parsing trees. I don't find anything professional enough and I think there would be a need for such an app. Who knows how I may go about it? (I myself don't have sufficient skills for app developing but I want to promote the idea).

When I try to get the sentence embedding with NLEmbedding.sentenceEmbedding(for: .english), I get nil returned and the message in the console is: "Unable to locate Asset for contextual word embedding model for local en." I've also tried different languages and also NLEmbedding.wordEmbedding(for: .english) doesn't work. This only occurs on my iPhone X after updating to iOS 14.4. Before it worked fine. It also works on the Simulator on iOS 14.4. Did somebody face a similar problem? Or does somebody know how to force iOS to download these embeddings again?

I got a wierd problem that Apple reject my app because the "Request permission modal dialog" language ( which base on the device language ) is difference from app language.It's only change after user terminate and enter app again. I was searching alot of solution but all of that do the same way as i did before. Only facebook have the "Change preference language" which worked but no document for that

Hi, I am trying to use the natural language tagger for Japanese. I used this sample code: var stringToRecognize = jpTextView.text &#9;&#9;let range = stringToRecognize!.startIndex ..< stringToRecognize!.endIndex &#9;&#9;let tagger = NLTagger(tagSchemes: [.lexicalClass]) &#9;&#9;tagger.string = stringToRecognize &#9;&#9;tagger.enumerateTags(in: range, unit: .word, scheme: .lexicalClass) { (tag, range) -> Bool in &#9;&#9;&#9;print("Word [\(stringToRecognize![range])] : \(tag!.rawValue)") &#9;&#9;&#9;return true &#9;&#9;} and applied it on some dummy data: 東京では１１月から、コロナウイルスの病気で入院する人が多くなっています。このため、お腹の中に赤ちゃんがいる看護師も仕事を続けています。家に小さな子どもがいる看護師は、子どもにウイルスがうつらないか心配しながら仕事をしています。 The output was: Word [東京] : OtherWord Word [で] : OtherWord Word [は] : OtherWord Word [１１] : OtherWord Word [月] : OtherWord Word [から] : OtherWord Word [、] : Punctuation Word [コロナ] : OtherWord Word [ウイルス] : OtherWord Word [の] : OtherWord Word [病気] : OtherWord Word [で] : OtherWord Word [入院] : OtherWord Word [する] : OtherWord Word [人] : OtherWord Word [が] : OtherWord Word [多く] : OtherWord Word [なっ] : OtherWord Word [て] : OtherWord Word [い] : OtherWord Word [ます] : OtherWord Word [。] : SentenceTerminator Word [この] : OtherWord Word [ため] : OtherWord Word [、] : Punctuation Word [お腹] : OtherWord Word [の] : OtherWord Word [中] : OtherWord Word [に] : OtherWord Word [赤ちゃん] : OtherWord Word [が] : OtherWord Word [いる] : OtherWord Word [看護] : OtherWord Word [師] : OtherWord Word [も] : OtherWord Word [仕事] : OtherWord Word [を] : OtherWord Word [続] : OtherWord Word [け] : OtherWord Word [て] : OtherWord Word [い] : OtherWord Word [ます] : OtherWord Word [。] : SentenceTerminator Word [家] : OtherWord Word [に] : OtherWord Word [小さな] : OtherWord Word [子ども] : OtherWord Word [が] : OtherWord Word [いる] : OtherWord Word [看護] : OtherWord Word [師] : OtherWord Word [は] : OtherWord Word [、] : Punctuation Word [子ども] : OtherWord Word [に] : OtherWord Word [ウイルス] : OtherWord Word [が] : OtherWord Word [うつら] : OtherWord Word [ない] : OtherWord Word [か] : OtherWord Word [心配] : OtherWord Word [し] : OtherWord Word [ながら] : OtherWord Word [仕事] : OtherWord Word [を] : OtherWord Word [し] : OtherWord Word [て] : OtherWord Word [い] : OtherWord Word [ます] : OtherWord Word [。] : SentenceTerminator It looks like every word is just being picked up as OtherWord, and it can detect some punctuation. Is this correct, or will there be an improvement to the Japanese tagger soon so we can differentiate between nouns, verbs, conjunctions, particles, etc. ?

As of IOS14 i started to notice a weird bug in my app. I have several UILabels that contains text in Hebrew and suppose to be displayed in RTL direction and until today it worked fine. But now running on IOS 14 I get this weird behaviour. First time app launches after installing from Xcode, all UILabels are being displayed correctly. But after closing the app and running again the direction of the UILabel׳s is changed to LTR. Each UILabel has leading and trailing constraints to it׳s superview and the text alignment is set to natural In app delegate i force app׳s direction to be RTL I am using Xcode version 12.0 Has anyone faced this kind of bug?

hello, everyone I currently encounter a problem with the installation of cocoapods, it tells me that it is a problem of native extension with ruby ​​... someone could help me please? is he also tells me a problem of access to the library I don't know what to do anymore ....

Hi, We understand that we need to have a D-U-N-S number to change the name of the personal account.But can we create a D-U-N-S number for a company located in the KOSOVO Republic. We do not have the KOSOVO Republic options in the address options. How should we act in such situations? Should we choose from the existing options? In order not to waste time without generating a DUNS number, can you give information on this subject? Thanks for Helping

When generating a MLWordEmbedding model, there seems to be some kind of compression happening with the original input vectors. Just take the example from the documentation: let vectors = [   "Hello"   : [0.0, 1.2, 5.0, 0.0],   "Goodbye" : [0.0, 1.3, -6.2, 0.1] ] let embedding = try! MLWordEmbedding(dictionary: vectors) embedding.vector(for: "Hello") == vectors["Hello"] // false embedding.vector(for: "Goodbye") == vectors["Goodbye"] // false // unexpectedly compressed to same vector embedding.vector(for: "Hello") == embedding.vector(for: "Goodbye") // true embedding.distance(between: "Hello", and: "Goodbye") // 0 Larger datasets, like word2vec, seem to work a bit better. But input vectors are still changed in unexpected ways and more vector collisions occur. I'm curious what spacial properties are expected to hold after compression? Is there some way to tune or disable this? Thanks!

It would be great to be able to use all the software in our mother language. I've spent so many years waiting for the S.W to be in basque. We are many people wating for it. I will love helping our dream come true.

I am wondering how well the "custom model" approach works for larger collections of documents. Assuming I have a corpus with a 100'000 sentences, would the custom model approach still be the way to go to look up the nearest neighbor for a query? Are there other mechanisms provided by the system for efficient vector similarity lookup? Is there a rule of thumb for the (key-)size for the custom model until it will have acceptable performance?

My app would significantly benefit from being able to identify sentences in text. So I'm trying NLTokenizer, since the api makes it looks like it could do that. I'm not able to obtain sentences as tokens. However, if I change the unit to words or paragraphs, I do get words and paragraphs respectively. Am I missing something or is this a bug? Here's some small example code: let source = "It was many and many a year ago, in a kingdom by the sea. \"Quiet\", said the raven." let tokenizer = NLTokenizer(unit: .sentence) tokenizer.string = source tokenizer.setLanguage(.english) print("begin") let tokens = tokenizer.tokens(for: source.startIndex..<source.endIndex).map({ range in return source[range] }) print(tokens) print("end") I expected to get: begin  ["It was many and many a year ago, in a kingdom by the sea.", "\"Quiet\", said the raven."] end But what I actually get is: begin [] end I found a blog where someone had claimed they had iterated the sentences using NLTokenizer, but when I examined his output, he had actually enumerated the words. macOS 10.15.6 beta 1