MACHINE LEARNING MODEL ARCHITECTURE AND USER INTERFACE TO INDICATE IMPACT OF TEXT NGRAMS

1 . A system, comprising:

a data processing system comprising one or more processors and memory to:

identify a plurality of n-grams at a plurality of locations in a first data set comprising text;

generate, via a model trained with machine learning, a first prediction for the first data set;

generate, via the model, a second prediction for a second data set, wherein the second data set lacks a first n-gram at a first location of the plurality of locations;

generate, by comparing a first prediction for the first data set with a second prediction for the second data set, an impact of the first n-gram at the first location; and

cause a user interface to present at least a portion of the first data set with a visual indication corresponding to the impact, the visual indication applied to a portion of the user interface corresponding to the first n-gram and positioned in the user interface at the first location.

2 . The system of claim 1 , comprising:

the data processing system to receive the first data set comprising data in a plurality of modalities.

3 . The system of claim 1 , comprising:

the data processing system to receive the first data set comprising the text, a numerical value, and an image.

4 . The system of claim 1 , comprising:

the data processing system to execute a tokenization technique to generate the plurality of n-grams.

5 . The system of claim 4 , wherein the tokenization technique comprises at least one of a deep learning tokenizer, a treebank tokenizer, or linguistic rules.

6 . The system of claim 1 , wherein the first n-gram comprises a word from the text.

7 . The system of claim 1 , wherein the first n-gram comprises a plurality of contiguous words from the text.

8 . The system of claim 1 , comprising the data processing system to:

generate a feature for the first data set based at least in part on the first n-gram at the first location; and

input the feature into the model to generate the first prediction.

9 . The system of claim 1 , comprising the data processing system to:

determine that a second n-gram of the plurality of n-grams is not used to generate a feature for input into the model to make the first prediction;

select, based on a map, a second visual indication different from the visual indication for the second n-gram; and

cause the user interface to present at least the portion of the first data set with the second visual indication applied to the second n-gram.

10 . The system of claim 1 , wherein the impact of the first n-gram is one of negative or positive on the first prediction.

11 . The system of claim 1 , comprising:

the data processing system to apply the visual indication to the first n-gram comprising a color selected from a color map that highlights the first n-gram in the user interface.

12 . A method, comprising:

receiving, by a data processing system, via a user interface and in communication with a client device, a data set comprising text;

identifying, by a data processing system comprising one or more processors and memory, a plurality of n-grams at a plurality of locations in a first data set;

removing, by the data processing system, a first n-gram of the plurality of n-grams from a first location of the plurality of locations to generate a second data set that lacks the first n-gram at the first location;

generating, by the data processing system, via a model trained with machine learning, a first prediction for the first data set;

generating, by the data processing system via the model, a second prediction for a second data set, wherein the second data set lacks a first n-gram at a first location of the plurality of locations;

generating, by the data processing system comparing a first prediction for the first data set with a second prediction for the second data set, an impact of the first n-gram at the first location; and

causing by the data processing system, a user interface to present at least a portion of the first data set with a visual indication corresponding to the impact, the visual indication applied to a portion of the user interface corresponding to the first n-gram and positioned in the user interface at the first location.

13 . The method of claim 12 , comprising:

receiving, by the data processing system, the first data set comprising data in a plurality of modalities, the plurality of modalities comprising the text, a numerical value, and an image.

14 . The method of claim 12 , comprising:

executing, by the data processing system, a tokenization technique to generate the plurality of n-grams, the tokenization technique comprising at least one of a deep learning tokenizer, a treebank tokenizer, or linguistic rules.

15 . The method of claim 12 , wherein the first n-gram comprises a plurality of contiguous words from the text.

16 . The method of claim 12 , comprising:

generating, by the data processing system, a feature for the first data set based at least in part on the first n-gram at the first location; and

inputting, by the data processing system, the feature into the model to generate the first prediction.

17 . The method of claim 12 , comprising:

determining, by the data processing system, that a second n-gram of the plurality of n-grams is not used to generate a feature for input into the model to make the first prediction;

selecting, by the data processing system based on a map, a second visual indication different from the visual indication for the second n-gram; and

causing, by the data processing system, the user interface to present at least the portion of the first data set with the second visual indication applied to the second n-gram.

18 . The method of claim 12 , comprising:

applying, by the data processing system, the visual indication to the first n-gram, the visual indication comprising a color selected from a color map that highlights the first n-gram in the user interface, wherein the impact of the first n-gram is one of negative or positive on the first prediction.

19 . A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

identify a plurality of n-grams at a plurality of locations in a first data set comprising text;

generate, via a model trained with machine learning, a first prediction for the first data set;

generate, via the model, a second prediction for the second data set, wherein the second data set lacks a first n-gram at a first location of the plurality of locations;

generate, by a comparison of a first prediction for the first data set with a second prediction for the second data set, an impact of the first n-gram at the first location; and

cause a user interface to present at least a portion of the first data set with a visual indication corresponding to the impact, the visual indication applied to the first n-gram and positioned in the user interface at the first location.

20 . The non-transitory computer readable medium of claim 19 , the computer readable medium further includes one or more instructions executable by the processor to:

apply the visual indication to the first n-gram, the visual indication comprising a color selected from a color map that highlights the first n-gram in the user interface, wherein the impact of the first n-gram is one of negative or positive on the first prediction.