lorenzoscottb commited on
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Update app.py

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Files changed (1) hide show
  1. app.py +6 -4
app.py CHANGED
@@ -18,7 +18,7 @@ Two main tasks are available:
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  - Sentiment Analysis (SA), with two English-only models (one for classification, one for generation) and a large multilingual model for classification.
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- - Relation Extraction (RE), with an English-only model that identifies relevant characters and existing relations between them following the Activity feature of the the Hall and Van de Castle framework.
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  - Name Entity Recognition (NER), with an English-only model that generates the identified characters.
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@@ -34,7 +34,7 @@ This model is an XLM-R tuned model, pre-trained with 94 languages available, and
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  """
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  description_S = """
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- A BERT-base-cased model pre-trained on Eglish-only text and tuned on annotated DreamBank English data.
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  """
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  description_G = """
@@ -63,7 +63,9 @@ examples_g = [
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  ]
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  examples_re = [
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- ["I was talking on the telephone to the father of an old friend of mine (boy, 21 years old). We were discussing the party the Saturday night before to which I had invited his son as a guest. I asked him if his son had a good time at the party. He told me not to tell his son that he had told me, but that he had had a good time, except he was a little surprised that I had acted the way I did."]
 
 
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  ]
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  interface_words = gr.Interface(
@@ -101,7 +103,7 @@ interface_model_RE = gr.Interface.load(
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  description=description_R,
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  examples=examples_re,
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  title="RE Generation",
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- mx_length=128,
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  )
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  interface_model_NER = gr.Interface.load(
 
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  - Sentiment Analysis (SA), with two English-only models (one for classification, one for generation) and a large multilingual model for classification.
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+ - Relation Extraction (RE), with an English-only model that identifies relevant characters and existing relations between them following the Activity feature of the Hall and Van de Castle framework.
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  - Name Entity Recognition (NER), with an English-only model that generates the identified characters.
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  """
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  description_S = """
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+ A BERT-base-cased model pre-trained on English-only text and tuned on annotated DreamBank English data.
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  """
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  description_G = """
 
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  ]
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  examples_re = [
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+ ["I was skating on the outdoor ice pond that used to be across the street from my house. I was not alone, but I did not recognize any of the other people who were skating around. I went through my whole repertoire of jumps, spires, and steps-some of which I can do and some of which I'm not yet sure of. They were all executed flawlessly-some I repeated, some I did only once. I seemed to know that if I went into competition, I would be sure of coming in third because there were only three contestants. Up to that time I hadn't considered it because I hadn't thought I was good enough, but now since everything was going so well, I decided to enter."],
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+ ["I was talking on the telephone to the father of an old friend of mine (boy, 21 years old). We were discussing the party the Saturday night before to which I had invited his son as a guest. I asked him if his son had a good time at the party. He told me not to tell his son that he had told me, but that he had had a good time, except he was a little surprised that I had acted the way I did."],
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+ ["I was walking alone with my dog in a forest."]
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  ]
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  interface_words = gr.Interface(
 
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  description=description_R,
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  examples=examples_re,
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  title="RE Generation",
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+ max_length=128,
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  )
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  interface_model_NER = gr.Interface.load(