Johannes commited on
Commit
54e9b45
1 Parent(s): a5bc7b6
Files changed (2) hide show
  1. README.md +1 -1
  2. app.py +16 -3
README.md CHANGED
@@ -1,5 +1,5 @@
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  ---
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- title: Borrowing Detection Es
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  emoji: 🔍
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  colorFrom: blue
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  colorTo: purple
 
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  ---
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+ title: Borrowing Detection Español
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  emoji: 🔍
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  colorFrom: blue
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  colorTo: purple
app.py CHANGED
@@ -18,6 +18,7 @@ diplacy_dict_template = {
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  def infer(input_text):
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  displacy_ents = []
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  borrowings = nlp(input_text)
 
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  for borrowing in borrowings:
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  displacy_ent_dict = {
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  "start": borrowing["start"],
@@ -28,9 +29,10 @@ def infer(input_text):
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  colors = {"B-ENG": "linear-gradient(90deg, #aa9cfc, #fc9ce7)",
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  "I-ENG": "linear-gradient(90deg, #99bfff, #a57cf0)",
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- "OTHER": "linear-gradient(90deg, #79d0a5, #f6e395)"}
 
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- options = {"ents": ["B-ENG", "I-ENG", "OTHER"], "colors": colors}
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  displacy_dict_template = {"text": input_text, "ents": displacy_ents, "title": None}
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  html = displacy.render(displacy_dict_template, style="ent", page=True, manual=True, options=options)
@@ -44,12 +46,23 @@ def infer(input_text):
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  return html
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  demo = gr.Interface(
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  title="Borrowing Detection Español",
 
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  fn=infer,
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  inputs=gr.Text(),
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  outputs=gr.HTML(),
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- examples=["Buscamos data scientist para proyecto de machine learning."],
 
 
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  )
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  demo.launch()
 
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  def infer(input_text):
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  displacy_ents = []
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  borrowings = nlp(input_text)
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+
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  for borrowing in borrowings:
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  displacy_ent_dict = {
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  "start": borrowing["start"],
 
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  colors = {"B-ENG": "linear-gradient(90deg, #aa9cfc, #fc9ce7)",
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  "I-ENG": "linear-gradient(90deg, #99bfff, #a57cf0)",
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+ "B-OTHER": "linear-gradient(90deg, #79d0a5, #f6e395)",
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+ "I-OTHER": "linear-gradient(90deg, #f79a76, #fb6d6d)"}
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+ options = {"ents": ["B-ENG", "I-ENG", "B-OTHER", "I-OTHER"], "colors": colors}
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  displacy_dict_template = {"text": input_text, "ents": displacy_ents, "title": None}
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  html = displacy.render(displacy_dict_template, style="ent", page=True, manual=True, options=options)
 
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  return html
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+ description="""This space is a demo for the paper Detecting Unassimilated Borrowings in Spanish:
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+ [An Annotated Corpus and Approaches to Modeling](https://arxiv.org/pdf/2203.16169.pdf)
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+
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+ The goal of the underlying model is to detect foreign words, e.g. anglicisms, in spanish texts.
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+ In general it has two types of tags for foreign words: *ENG* and *OTHER*. The authors used BIO-tagging,
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+ which is why in practice you will see a *B-* or *I-* in front of the tags.
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+ """
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+
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  demo = gr.Interface(
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  title="Borrowing Detection Español",
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+ description=description,
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  fn=infer,
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  inputs=gr.Text(),
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  outputs=gr.HTML(),
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+ examples=["Buscamos data scientist para proyecto de machine learning.",
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+ "Las fake news sobre la celebrity se reprodujeron por los 'mass media' en prime time.",
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+ "Me gusta el cine noir y el anime."],
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  )
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  demo.launch()