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Apoorv Saxena
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a1b42e5
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Parent(s):
02f29aa
Update app.py
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app.py
CHANGED
@@ -66,10 +66,24 @@ rel_input = gr.inputs.Textbox(lines=1, default="country")
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output = gr.outputs.Label()
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examples = [
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]
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title = "Interactive demo: KGT5"
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description = """Demo for <a href='https://arxiv.org/abs/2203.10321'>Sequence-to-Sequence Knowledge Graph Completion and Question Answering </a> (KGT5). This particular model is a T5-base model trained on the task of tail prediction on WikiKG90Mv2 dataset and obtains 0.239 validation MRR on this task (<a href="https://ogb.stanford.edu/docs/lsc/leaderboards/#wikikg90mv2">leaderboard</a>, see paper for details).
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@@ -82,6 +96,10 @@ description = """Demo for <a href='https://arxiv.org/abs/2203.10321'>Sequence-to
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article = """
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Under the hood, this demo concatenates the entity and relation, feeds it to the model and then samples 25 sequences, which are then ranked according to their sequence probabilities.
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For more details see the <a href='https://github.com/apoorvumang/kgt5'>Github repo</a> or the <a href="https://huggingface.co/apoorvumang/kgt5-base-wikikg90mv2">hf model page</a>.
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"""
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output = gr.outputs.Label()
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examples = [
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['Adrian Kochsiek', 'sex or gender'],
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['Apoorv Umang Saxena', 'family name'],
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['World War II', 'followed by'],
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['Apoorv Umang Saxena', 'country'],
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['Ippolito Boccolini', 'writing language'] ,
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['Roelant', 'writing system'] ,
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['The Accountant 2227', 'language of work or name'] ,
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['Microbial Infection and AMR in Hospitalized Patients With Covid 19', 'study type'] ,
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['Carla Fracci', 'manner of death'] ,
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['list of programs broadcast by Comet', 'is a list of'] ,
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['Loreta Podhradí', 'continent'] ,
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['Opistognathotrema', 'taxon rank'] ,
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['Museum Arbeitswelt Steyr', 'wheelchair accessibility'] ,
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['Heliotropium tytoides', 'subject has role'] ,
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['School bus crash rates on routine and nonroutine routes.', 'sponsor'] ,
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['Tachigalieae', 'taxon rank'] ,
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['Irena Salusová', 'place of detention'] ,
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]
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title = "Interactive demo: KGT5"
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description = """Demo for <a href='https://arxiv.org/abs/2203.10321'>Sequence-to-Sequence Knowledge Graph Completion and Question Answering </a> (KGT5). This particular model is a T5-base model trained on the task of tail prediction on WikiKG90Mv2 dataset and obtains 0.239 validation MRR on this task (<a href="https://ogb.stanford.edu/docs/lsc/leaderboards/#wikikg90mv2">leaderboard</a>, see paper for details).
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article = """
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Under the hood, this demo concatenates the entity and relation, feeds it to the model and then samples 25 sequences, which are then ranked according to their sequence probabilities.
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<br>
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The text representations of the relations and entities can be downloaded from here: <a href="https://storage.googleapis.com/kgt5-wikikg90mv2/rel_alias_list.pickle">https://storage.googleapis.com/kgt5-wikikg90mv2/rel_alias_list.pickle</a> and
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<a href="https://storage.googleapis.com/kgt5-wikikg90mv2/ent_alias_list.pickle">https://storage.googleapis.com/kgt5-wikikg90mv2/ent_alias_list.pickle</a>
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<br>
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For more details see the <a href='https://github.com/apoorvumang/kgt5'>Github repo</a> or the <a href="https://huggingface.co/apoorvumang/kgt5-base-wikikg90mv2">hf model page</a>.
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"""
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