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The demo examples are wrong or don't make much sense.

"Donald Trump's administration" is not a person.

In the following example, "The currency" is not a subject and "India" is not an object.

I don't know how much useful information is extracted by this system.

That example is a tweet, which the syntax and NER models haven't been trained on. You can make calls to `nlp.update()` to improve it on your own data. We also have an annotation tool, https://prodi.gy , to more quickly create training data.

(I'm the author of spaCy, not this Docker container.)

SpaCy is wonderful, I've used it a lot over the years and I have high confidence in its output.

I just wish the author of this docker container chose demo sentences that advertised it better.

> "The currency" is not a subject and "India" is not an object.

But "subject" and "object" is for indicating the Subject-Verb-Predicate (object) of the sentence and not as in literal object ?

"India" is neither the predicate nor the object of the sentence.

You are correct. That is clearly a wrong example. Will change that.

Also that issue is in my code (poor naming choice). Will put up the code on github soon. Hope that will help.

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