  We present SNIascore, a deep-learning based method for spectroscopic
classification of thermonuclear supernovae (SNe Ia) based on very
low-resolution (R $\sim100$) data. The goal of SNIascore is fully automated
classification of SNe Ia with a very low false-positive rate (FPR) so that
human intervention can be greatly reduced in large-scale SN classification
efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF)
Bright Transient Survey (BTS). We utilize a recurrent neural network (RNN)
architecture with a combination of bidirectional long short-term memory and
gated recurrent unit layers. SNIascore achieves a $<0.6\%$ FPR while
classifying up to $90\%$ of the low-resolution SN Ia spectra obtained by the
BTS. SNIascore simultaneously performs binary classification and predicts the
redshifts of secure SNe Ia via regression (with a typical uncertainty of
$<0.005$ in the range from $z = 0.01$ to $z = 0.12$). For the magnitude-limited
ZTF BTS survey ($\approx70\%$ SNe Ia), deploying SNIascore reduces the amount
of spectra in need of human classification or confirmation by $\approx60\%$.
Furthermore, SNIascore allows SN Ia classifications to be automatically
announced in real-time to the public immediately following a finished
observation during the night.
