Multilabel Metrics TFX about model-analysis HOT 7 CLOSED

model = tf.saved_model.load(export_dir='/tmp/tfx-interactive-model/6/serving_model_dir/')
predict_fn = model.signatures["serving_default"]
text = "some random text"
example = tf.train.Example(features = tf.train.Features(feature ={
    "Headline":_string_feature('headline'),
    "InternalId":_int64_feature(1111),
    "Text":_string_feature(text),
    "content_id":_string_feature("2222"),
}))
serialized_example = example.SerializeToString()
predict_fn(tf.constant([serialized_example]))

{'outputs': <tf.Tensor: shape=(1, 10), dtype=float32, numpy=
 array([[0.6034294 , 0.40879828, 0.4462878 , 0.3196895 , 0.31108657,
         0.30687535, 0.27371922, 0.29507416, 0.2872882 , 0.26999897]],
       dtype=float32)>}

{
 'Text': array([b'Football fans looking forward to seeing the renewal of the rivalry between Cristiano Ronaldo and Lionel Messi were made to wait a while longer after the Portuguese forward was forced to miss Juventus' Champions League tie against Barcelona on Wednesday.'],dtype=object),
 'Headline': array([b"Lionel Messi scores as Cristiano Ronaldo misses Barcelona's victory over Juventus."], dtype=object),
 'categories': array([1., 0., 0., 0., 0., 0., 0., 0., 0.,0.], dtype=object), 
}

{
 'Text_xf': array([b'COVID-19 has changed fan behavior and accelerated three to five years of technology adoption into six months'],dtype=object),
 'Headline_xf': array([b"How Technology Is Improving Fan Transactions at Sports Venues"], dtype=object),
 'categories_xf': array([1., 1., 0., 0., 0., 0., 0., 0., 0.,0.'], dtype=object), 
}