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README.md

Question Answering with DistilBERT

The app contains a demo of the DistilBERT model (97% of BERT’s performance on GLUE) fine-tuned for Question answering on the SQuAD dataset. It provides 48 passages from the dataset for users to choose from.

demo gif

Available models:

  • "original" converted DistilBERT (254MB)
  • FP16 post-training-quantized DistilBERT (131MB)
  • "hybrid" (8-bits precision weights) post-training-quantized DistilBERT (64MB)

Build the demo app using Android Studio

Prerequisites

  • If you don't have already, install Android Studio, following the instructions on the website.
  • Android Studio 3.2 or later.
  • You need an Android device or Android emulator and Android development environment with minimum API 26.
  • The libs directory contains a custom build of TensorFlow Lite with TensorFlow ops built-in, which is used by the app. It results in a bigger binary than the "normal" build but allows compatibility with DistilBERT.

Building

  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, select the directory where you cloned this repo.
  • You may also need to install various platforms and tools according to error messages.
  • If it asks you to use Instant Run, click Proceed Without Instant Run.

Running

  • You need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.
  • If you already have Android emulator installed in Android Studio, select a virtual device with minimum API 26.
  • Be sure the bert configuration is selected
  • Click Run to run the demo app on your Android device.

Build the demo using gradle (command line)

From the repository root location:

  • Use the following command to build a demo apk:
./gradlew :bert:build
  • Use the following command to install the apk onto your connected device:
adb install bert/build/outputs/apk/debug/bert-debug.apk

Change the model

To choose which model to use in the app:

  • Remove/rename the current model.tflite file in src/main/assets
  • Comment/uncomment the model to download in the download.gradle config file:
"https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-384.tflite": "model.tflite", // <- "original" converted DistilBERT (default)
// "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-384-fp16.tflite": "model.tflite", // <- fp16 quantized version of DistilBERT
// "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-384-8bits.tflite": "model.tflite", // <- hybrid quantized version of DistilBERT

Credits

The Bert QA app is forked from the bertqa example in the tensorflow/examples repository and uses the same tokenizer with DistilBERT.