The Task Library BertQuestionAnswerer
API loads a Bert model and answers
questions based on the content of a given passage. For more information, see the
example for the Question-Answer model.
Key features of the BertQuestionAnswerer API
Takes two text inputs as question and context and outputs a list of possible answers.
Performs out-of-graph Wordpiece or Sentencepiece tokenizations on input text.
Supported BertQuestionAnswerer models
The following models are compatible with the BertNLClassifier
API.
Models created by TensorFlow Lite Model Maker for BERT Question Answer.
Custom models that meet the model compatibility requirements.
Run inference in Java
Step 1: Import Gradle dependency and other settings
Copy the .tflite
model file to the assets directory of the Android module
where the model will be run. Specify that the file should not be compressed, and
add the TensorFlow Lite library to the module’s build.gradle
file:
android {
// Other settings
// Specify tflite file should not be compressed for the app apk
aaptOptions {
noCompress "tflite"
}
}
dependencies {
// Other dependencies
// Import the Task Text Library dependency
implementation 'org.tensorflow:tensorflow-lite-task-text:0.4.4'
}
Step 2: Run inference using the API
// Initialization
BertQuestionAnswererOptions options =
BertQuestionAnswererOptions.builder()
.setBaseOptions(BaseOptions.builder().setNumThreads(4).build())
.build();
BertQuestionAnswerer answerer =
BertQuestionAnswerer.createFromFileAndOptions(
androidContext, modelFile, options);
// Run inference
List<QaAnswer> answers = answerer.answer(contextOfTheQuestion, questionToAsk);
See the source code for more details.
Run inference in Swift
Step 1: Import CocoaPods
Add the TensorFlowLiteTaskText pod in Podfile
target 'MySwiftAppWithTaskAPI' do
use_frameworks!
pod 'TensorFlowLiteTaskText', '~> 0.4.4'
end
Step 2: Run inference using the API
// Initialization
let mobileBertAnswerer = TFLBertQuestionAnswerer.questionAnswerer(
modelPath: mobileBertModelPath)
// Run inference
let answers = mobileBertAnswerer.answer(
context: context, question: question)
See the source code for more details.
Run inference in C++
// Initialization
BertQuestionAnswererOptions options;
options.mutable_base_options()->mutable_model_file()->set_file_name(model_path);
std::unique_ptr<BertQuestionAnswerer> answerer = BertQuestionAnswerer::CreateFromOptions(options).value();
// Run inference with your inputs, `context_of_question` and `question_to_ask`.
std::vector<QaAnswer> positive_results = answerer->Answer(context_of_question, question_to_ask);
See the source code for more details.
Run inference in Python
Step 1: Install the pip package
pip install tflite-support
Step 2: Using the model
# Imports
from tflite_support.task import text
# Initialization
answerer = text.BertQuestionAnswerer.create_from_file(model_path)
# Run inference
bert_qa_result = answerer.answer(context, question)
See the
source code
for more options to configure BertQuestionAnswerer
.
Example results
Here is an example of the answer results of ALBERT model.
Context: "The Amazon rainforest, alternatively, the Amazon Jungle, also known in English as Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 km2 (2,700,000 sq mi), of which 5,500,000 km2 (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations."
Question: "Where is Amazon rainforest?"
Answers:
answer[0]: 'South America.'
logit: 1.84847, start_index: 39, end_index: 40
answer[1]: 'most of the Amazon basin of South America.'
logit: 1.2921, start_index: 34, end_index: 40
answer[2]: 'the Amazon basin of South America.'
logit: -0.0959535, start_index: 36, end_index: 40
answer[3]: 'the Amazon biome that covers most of the Amazon basin of South America.'
logit: -0.498558, start_index: 28, end_index: 40
answer[4]: 'Amazon basin of South America.'
logit: -0.774266, start_index: 37, end_index: 40
Try out the simple CLI demo tool for BertQuestionAnswerer with your own model and test data.
Model compatibility requirements
The BertQuestionAnswerer
API expects a TFLite model with mandatory
TFLite Model Metadata.
The Metadata should meet the following requirements:
input_process_units
for Wordpiece/Sentencepiece Tokenizer3 input tensors with names "ids", "mask" and "segment_ids" for the output of the tokenizer
2 output tensors with names "end_logits" and "start_logits" to indicate the answer's relative position in the context