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🎨 A short thread of some cool ways to visualize @spacy_io's output in your terminal! 
@spacy_io Explacy by @tylerneylon gives you a very nicely formatted dependency tree and a table including part-of-speech tags and lemmas.
github.com/tylerneylon/ex…
github.com/tylerneylon/ex…
This one by @weihuang is for #rstats and super new – it uses @quantedainit's spacyr package under the hood.
github.com/weihuangwong/d…
github.com/weihuangwong/d…
If you're using prodi.gy, you can also run the built-in `ner.print-stream` recipe, which takes a pre-trained model and an input text file. There's also an `ner.print-dataset` recipe for annotated datasets.
prodi.gy/docs/recipes
prodi.gy/docs/recipes
# Single tweet version
from wasabi import color as c
def print_ents(d):
o,r,t = 0,"",d.text
for e in d.ents:
r += t[o:e.start_char]+c(f" {t[e.start_char:e.end_char]} ",bg=3,fg="0")+c(f" {e.label_} ",bg=4,fg="0")
o = e.end_char
r += t[o:]
print(r)
from wasabi import color as c
def print_ents(d):
o,r,t = 0,"",d.text
for e in d.ents:
r += t[o:e.start_char]+c(f" {t[e.start_char:e.end_char]} ",bg=3,fg="0")+c(f" {e.label_} ",bg=4,fg="0")
o = e.end_char
r += t[o:]
print(r)
