victordibia

<script src="https://cdn.jsdelivr.net/npm/handtrackjs/dist/handtrack.min.js"> </script>
const img = document.getElementById('img');  

// Load the model. 
const img = document.getElementById('img'); 
	handTrack.load().then(model => {
		model.detect(img).then(predictions => {
			console.log('Predictions: ', predictions) // bbox predictions  
		});
});

victordibia

 \\ npm install --save handtrackjs 
import * as handTrack from 'handtrackjs'; 

const img = document.getElementById('img');  

// Load the model. 
const img = document.getElementById('img'); 
	handTrack.load().then(model => {
		model.detect(img).then(predictions => {

victordibia

def get_gradient(question, context, model, tokenizer):  
    """Return gradient of input (question) wrt to model output span prediction

    Args:
        question (str): text of input question
        context (str): text of question context/passage
        model (QA model): Hugging Face BERT model for QA transformers.modeling_tf_distilbert.TFDistilBertForQuestionAnswering, transformers.modeling_tf_bert.TFBertForQuestionAnswering
        tokenizer (tokenizer): transformers.tokenization_bert.BertTokenizerFast 

    Returns: