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Update README.md (#2)

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- Update README.md (53498872645dd550fddf38253b670f4de78b2c31)


Co-authored-by: Conrad Dobberstein <[email protected]>

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  1. README.md +13 -14
README.md CHANGED
@@ -70,29 +70,28 @@ tagger = SequenceTagger.load("flair/upos-multi")
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  # make example sentence
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  sentence = Sentence("Ich liebe Berlin, as they say. ")
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- # predict NER tags
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  tagger.predict(sentence)
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  # print sentence
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  print(sentence)
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- # print predicted NER spans
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- print('The following NER tags are found:')
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- # iterate over entities and print
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- for entity in sentence.get_spans('pos'):
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- print(entity)
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  ```
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  This yields the following output:
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  ```
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- Span [1]: "Ich" [βˆ’ Labels: PRON (0.9999)]
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- Span [2]: "liebe" [βˆ’ Labels: VERB (0.9999)]
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- Span [3]: "Berlin" [βˆ’ Labels: PROPN (0.9997)]
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- Span [4]: "," [βˆ’ Labels: PUNCT (1.0)]
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- Span [5]: "as" [βˆ’ Labels: SCONJ (0.9991)]
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- Span [6]: "they" [βˆ’ Labels: PRON (0.9998)]
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- Span [7]: "say" [βˆ’ Labels: VERB (0.9998)]
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- Span [8]: "." [βˆ’ Labels: PUNCT (1.0)]
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  ```
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  So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*".
 
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  # make example sentence
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  sentence = Sentence("Ich liebe Berlin, as they say. ")
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+ # predict POS tags
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  tagger.predict(sentence)
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  # print sentence
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  print(sentence)
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+ # iterate over tokens and print the predicted POS label
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+ print("The following POS tags are found:")
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+ for token in sentence:
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+ print(token.get_label("upos"))
 
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  ```
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  This yields the following output:
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  ```
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+ Token[0]: "Ich" β†’ PRON (0.9999)
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+ Token[1]: "liebe" β†’ VERB (0.9999)
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+ Token[2]: "Berlin" β†’ PROPN (0.9997)
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+ Token[3]: "," β†’ PUNCT (1.0)
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+ Token[4]: "as" β†’ SCONJ (0.9991)
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+ Token[5]: "they" β†’ PRON (0.9998)
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+ Token[6]: "say" β†’ VERB (0.9998)
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+ Token[7]: "." β†’ PUNCT (1.0)
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  ```
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  So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*".