Token Classification
Flair
PyTorch
Spanish
sequence-tagger-model
jgrivolla commited on
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Update README.md

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@@ -60,14 +60,20 @@ for entity in sentence.get_spans('ner'):
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  print(entity)
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  ```
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- This yields the following output (**TODO: update**):
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  ```
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- Span [1,2]: "George Washington" [ Labels: PER (1.0)]
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- Span [5]: "Washington" [− Labels: LOC (1.0)]
 
 
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  ```
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- So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington fue a Washington*".
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-
 
 
 
 
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  ---
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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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+ Sentence[24]: "PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRAULICOS S.L., constituidos en UTE PODACESA-ECR realizan la siguiente oferta:" _ ["PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRAULICOS S.L."/UTE, "PODACESA-ECR"/UTE]
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+ The following NER tags are found:
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+ Span[0:14]: "PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRAULICOS S.L." _ UTE (0.995)
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+ Span[18:19]: "PODACESA-ECR" _ UTE (0.9955)
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  ```
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+ and with the sentence "PODACESA OBRAS Y SERVICIOS, S.A realiza la siguiente oferta:"
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+ ```
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+ Sentence[11]: "PODACESA OBRAS Y SERVICIOS, S.A realiza la siguiente oferta:" _ ["PODACESA OBRAS Y SERVICIOS, S.A"/SINGLE_COMPANY]
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+ The following NER tags are found:
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+ Span[0:6]: "PODACESA OBRAS Y SERVICIOS, S.A" _ SINGLE_COMPANY (1.0)
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+ ```
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  ---
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