How do I use sentence-transformers/all-MiniLM-L6-v2 tflite model in android studio (kotlin)

General Discussion
,
#1

I have a tflite version of the hugging face model sentence-transformers/all-MiniLM-L6-v2…
But i dont know how to shape the input and output tensor…basically the model gives encoding of a sentence but also does multiple other thigs…I just want to use it for encoding

When I added the model to ml folder of android studio it gave me the below code for running the model by analyzing it…

val model = Model.newInstance(context)

// Creates inputs for reference.
val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 128), DataType.INT64)
inputFeature0.loadBuffer(byteBuffer)
val inputFeature1 = TensorBuffer.createFixedSize(intArrayOf(1, 128), DataType.INT64)
inputFeature1.loadBuffer(byteBuffer)
val inputFeature2 = TensorBuffer.createFixedSize(intArrayOf(1, 128), DataType.INT64)
inputFeature2.loadBuffer(byteBuffer)

// Runs model inference and gets result.
val outputs = model.process(inputFeature0, inputFeature1, inputFeature2)
val outputFeature0 = outputs.outputFeature0AsTensorBuffer

// Releases model resources if no longer used.
model.close()

But when I run this code it gives me error r: tensorflow/lite/kernels/gather.cc:159 indices_has_only_positive_elements was not true.

I actually have no clue as to how should i use the model to get encodings…Any advice would help

Below is the code I am using…

  private val tflite: Interpreter

    init {
        // Initialize the TFLite interpreter with the model
        tflite = Interpreter(loadModelFile())

        // Get the output tensor
        val outputTensor = tflite.getOutputTensor(0)


// Get the output tensor type
//        val outputTensorType = tflite.getTensorInfo(outputTensor).dataType
    }

    private fun loadModelFile(): ByteBuffer {
        val modelPath = "model.tflite"
        val assetManager = c.assets
        val inputStream = assetManager.open(modelPath)

        val fileDescriptor = assetManager.openFd(modelPath)
        val startOffset = fileDescriptor.startOffset
        val declaredLength = fileDescriptor.declaredLength

        val fileChannel = FileInputStream(fileDescriptor.fileDescriptor).channel
        return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength)
    }
    fun computeCosineSimilarity(sentence1: String, sentence2: String): Double {
        // Convert the sentences to LongArrays
        val sentence1Embedding = computeEmbedding(sentence1)
        val sentence2Embedding = computeEmbedding(sentence2)

        // Compute the cosine similarity between the sentence embeddings
        val cosineSimilarity = cosineSimilarity(sentence1Embedding, sentence2Embedding)



        return cosineSimilarity

    }

    fun computeEmbedding(sentence: String): FloatArray {
        val model = Model.newInstance(c)
        val padded_sentence = sentence.padEnd(4 * sentence.length, ' ')
//        var  byteBuffer = ByteBuffer.allocate(1024)
//         byteBuffer = ByteBuffer.wrap(padded_sentence.toByteArray())

        val byteBuffer = ByteBuffer.allocate(1024).put(padded_sentence.toByteArray())
        // Creates inputs for reference.
        val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 256), DataType.FLOAT32)
        inputFeature0.loadBuffer(byteBuffer)
        val inputFeature1 = TensorBuffer.createFixedSize(intArrayOf(1, 256), DataType.FLOAT32)
        inputFeature1.loadBuffer(byteBuffer)
        val inputFeature2 = TensorBuffer.createFixedSize(intArrayOf(1, 256), DataType.FLOAT32)
        inputFeature2.loadBuffer(byteBuffer)


        // Runs model inference and gets result.
        val outputs = model.process(inputFeature0, inputFeature1, inputFeature2)
        val outputFeature0 = outputs.outputFeature0AsTensorBuffer

        // Extracts the embedding from the output.
        val embedding = outputFeature0.floatArray

        // Releases model resources if no longer used.
        model.close()

        return embedding
}}

here is the sentence_transformer model link : sentence-transformers/all-MiniLM-L6-v2 · Hugging Face

#3

can you give me more details about your code