I have several questions regarding running mobile bert model on Android.
- Does google mobile bert multilingual support Chinese as well (TensorFlow Hub)?
- Does this model have metadata? (Android studio shows there is not metadata, but archive has a separate file keras-metadata.pb)
- If this model should use metadata, how to open and add via Android Studio API?
- Default code shown by Android Studio after model import is not correct. It operates with DataType.INT32 when tensor flow lite does not support it. I assume IDE just does conversion what model by itself uses, but could we add a validator to show the model has not been supported yet?
- Code for the model is not quite clear either. It allocates several arrays for features, but for each feature it uses the same text input. If input is the same, why do we need to duplicate it several times? I expect just text for classification converted into a tensor
- Issue with unsupported model types seems to be possible to overcome via custom interpreter. However, I haven’t figured out yet how to properly build it for my aim. Especially when the initial usage sample of the model is misleading. Can you show how it should be used for Chinese (or any) text classification?
- What should be in the output array? I assume it should be a dictionary of words which the model can recognise. However, the initial usage sample of the model again shows something different.
update (14.08): some more investigation results and questions: https://stackoverflow.com/questions/76889244/if-mobile-bert-model-requires-datatype-int32-is-it-possible-to-run-on-android-a
Below is a sample code Android Studio shows after model has been imported:
val model = MobileBertMultilingual.newInstance(context)
// Creates inputs for reference.
val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 1), DataType.INT32)
inputFeature0.loadBuffer(byteBuffer)
val inputFeature1 = TensorBuffer.createFixedSize(intArrayOf(1, 1), DataType.INT32)
inputFeature1.loadBuffer(byteBuffer)
val inputFeature2 = TensorBuffer.createFixedSize(intArrayOf(1, 1), DataType.INT32)
inputFeature2.loadBuffer(byteBuffer)
// Runs model inference and gets result.
val outputs = model.process(inputFeature0, inputFeature1, inputFeature2)
val outputFeature0 = outputs.outputFeature0AsTensorBuffer
val outputFeature1 = outputs.outputFeature1AsTensorBuffer
val outputFeature2 = outputs.outputFeature2AsTensorBuffer
val outputFeature3 = outputs.outputFeature3AsTensorBuffer
val outputFeature4 = outputs.outputFeature4AsTensorBuffer
val outputFeature5 = outputs.outputFeature5AsTensorBuffer
val outputFeature6 = outputs.outputFeature6AsTensorBuffer
val outputFeature7 = outputs.outputFeature7AsTensorBuffer
val outputFeature8 = outputs.outputFeature8AsTensorBuffer
val outputFeature9 = outputs.outputFeature9AsTensorBuffer
val outputFeature10 = outputs.outputFeature10AsTensorBuffer
val outputFeature11 = outputs.outputFeature11AsTensorBuffer
val outputFeature12 = outputs.outputFeature12AsTensorBuffer
val outputFeature13 = outputs.outputFeature13AsTensorBuffer
val outputFeature14 = outputs.outputFeature14AsTensorBuffer
val outputFeature15 = outputs.outputFeature15AsTensorBuffer
val outputFeature16 = outputs.outputFeature16AsTensorBuffer
val outputFeature17 = outputs.outputFeature17AsTensorBuffer
val outputFeature18 = outputs.outputFeature18AsTensorBuffer
val outputFeature19 = outputs.outputFeature19AsTensorBuffer
val outputFeature20 = outputs.outputFeature20AsTensorBuffer
val outputFeature21 = outputs.outputFeature21AsTensorBuffer
val outputFeature22 = outputs.outputFeature22AsTensorBuffer
val outputFeature23 = outputs.outputFeature23AsTensorBuffer
val outputFeature24 = outputs.outputFeature24AsTensorBuffer
val outputFeature25 = outputs.outputFeature25AsTensorBuffer
val outputFeature26 = outputs.outputFeature26AsTensorBuffer
val outputFeature27 = outputs.outputFeature27AsTensorBuffer
val outputFeature28 = outputs.outputFeature28AsTensorBuffer
val outputFeature29 = outputs.outputFeature29AsTensorBuffer
val outputFeature30 = outputs.outputFeature30AsTensorBuffer
val outputFeature31 = outputs.outputFeature31AsTensorBuffer
val outputFeature32 = outputs.outputFeature32AsTensorBuffer
val outputFeature33 = outputs.outputFeature33AsTensorBuffer
val outputFeature34 = outputs.outputFeature34AsTensorBuffer
val outputFeature35 = outputs.outputFeature35AsTensorBuffer
val outputFeature36 = outputs.outputFeature36AsTensorBuffer
val outputFeature37 = outputs.outputFeature37AsTensorBuffer
val outputFeature38 = outputs.outputFeature38AsTensorBuffer
val outputFeature39 = outputs.outputFeature39AsTensorBuffer
val outputFeature40 = outputs.outputFeature40AsTensorBuffer
val outputFeature41 = outputs.outputFeature41AsTensorBuffer
val outputFeature42 = outputs.outputFeature42AsTensorBuffer
val outputFeature43 = outputs.outputFeature43AsTensorBuffer
val outputFeature44 = outputs.outputFeature44AsTensorBuffer
val outputFeature45 = outputs.outputFeature45AsTensorBuffer
val outputFeature46 = outputs.outputFeature46AsTensorBuffer
val outputFeature47 = outputs.outputFeature47AsTensorBuffer
val outputFeature48 = outputs.outputFeature48AsTensorBuffer
val outputFeature49 = outputs.outputFeature49AsTensorBuffer
val outputFeature50 = outputs.outputFeature50AsTensorBuffer
val outputFeature51 = outputs.outputFeature51AsTensorBuffer
// Releases model resources if no longer used.
model.close()
p.s.
A few words about context. I am extending my personal app which helps to me to learn Chinese. I want app to split just entered new sentence on Chinese into separate words and do translation to English for each of them. I am able to automatically translate Chinese text already with help of standard Google ml models, but for text classification seems BERT-like models works the best. The task is just word tokenisation, but because Chinese language doesn’t have spaces between separate words, the task become non-trivial.