lorenzoscottb commited on
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1 Parent(s): 31c3f47

Update app.py

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  1. app.py +7 -16
app.py CHANGED
@@ -33,29 +33,20 @@ def test_input(words):
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  # )
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- title = "**BERT on a PLANE**"
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  description = """
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- Did you know that, logically speaking, a small cat is not a small animal, and that a fake smile is not a smile?
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- Learn more by testing our BERT model tuned to perform phrase-level adjective-noun entailment, via the [PLANE](https://aclanthology.org/2022.coling-1.359/) dataset.
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- **Intended uses & limitations**:
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- The scope of the model is not to run lexical entailment or hypernym detection (e.g., *"A dog is an animal*"), but to perform a very specific subset of phrase-level entailment, based on adjective-nouns phrases. The type of question you can ask the model are limited, and should have one of three forms:
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- - An adjective+Noun is a Noun (e.g. A red car is a car)
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-
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- - An adjective+Noun is a noun-hypernym (e.g. A red car is a vehicle)
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- - An adjective+Noun is a adjective+noun-hypernym (e.g. A red car is a red vehicle)
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-
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- Linguistically speaking, adjectives belong to three macro classes (intersective, subsective, and intensional). From a linguistic and logical stand, these class shape the truth value of the three forms above. For instance, since red is an intersective adjective, the three from are all true. A subjective adjective like small allows just the first two, but not the last – that is, logically speaking, a small car is not a small vehicle.
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- In other words, the model was built to study out-of-distribution compositional generalisation with respect to a very specific set of compositional phenomena.
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- This poses clear limitations to the question you can ask the model. For instance, if you had to query the model with a basic (false) hypernym detection task (e.g., *A dog is a cat*), the model will consider it as true.
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  The current model achieves an accuracy of 90% on out-of-distribution evaluation
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  Coming soon: check if words were in training data!"""
 
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  # )
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+ title = "BERT on a PLANE"
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  description = """
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+ Did you know that logically speaking **A small cat is not a small animal**, and that **A fake smile is not a smile**? Learn more by testing our BERT model tuned to perform phrase-level adjective-noun entailment, via the [PLANE](https://aclanthology.org/2022.coling-1.359/) dataset.
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+ Please note that the scope of the model is not to run lexical entailment or hypernym detection (e.g., *"A dog is an animal*"), but to perform a very specific subset of phrase-level entailment, based on adjective-nouns phrases. The type of question you can ask the model are limited, and should have one of three forms:
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+ - An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)
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+ - An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
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+ - An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a vehicle)
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+ Please note that, as in the examples above, the adjective should be the same for both phrases, and that the Hypernym(Noun) shoul be a true hypernym of the selected noun
 
 
 
 
 
 
 
 
 
 
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  The current model achieves an accuracy of 90% on out-of-distribution evaluation
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  Coming soon: check if words were in training data!"""