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03f1ac5
1
Parent(s):
864a486
Update app.py
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app.py
CHANGED
@@ -3,9 +3,10 @@ import gradio as gr
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from dataclasses import dataclass
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from prettytable import PrettyTable
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from pytorch_ie import
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from pytorch_ie.
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from pytorch_ie.
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from typing import List
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@@ -16,17 +17,11 @@ class ExampleDocument(TextDocument):
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relations: AnnotationList[BinaryRelation] = annotation_field(target="entities")
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ner_model = TransformerSpanClassificationModel.from_pretrained(model_name_or_path)
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ner_pipeline =
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model_name_or_path = "pie/example-re-textclf-tacred"
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re_taskmodule = TransformerRETextClassificationTaskModule.from_pretrained(model_name_or_path)
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re_model = TransformerTextClassificationModel.from_pretrained(model_name_or_path)
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re_pipeline = Pipeline(model=re_model, taskmodule=re_taskmodule, device=-1, num_workers=0)
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def predict(text):
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@@ -57,7 +52,10 @@ def predict(text):
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iface = gr.Interface(
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fn=predict,
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inputs=
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outputs="html",
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)
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iface.launch()
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from dataclasses import dataclass
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from prettytable import PrettyTable
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from pytorch_ie.annotations import LabeledSpan, BinaryRelation
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from pytorch_ie.auto import AutoPipeline
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from pytorch_ie.core import AnnotationList, annotation_field
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from pytorch_ie.documents import TextDocument
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from typing import List
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relations: AnnotationList[BinaryRelation] = annotation_field(target="entities")
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ner_model_name_or_path = "pie/example-ner-spanclf-conll03"
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re_model_name_or_path = "pie/example-re-textclf-tacred"
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ner_pipeline = AutoPipeline.from_pretrained(ner_model_name_or_path, device=-1, num_workers=0)
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re_pipeline = AutoPipeline.from_pretrained(re_model_name_or_path, device=-1, num_workers=0)
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def predict(text):
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iface = gr.Interface(
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fn=predict,
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inputs=gr.inputs.Textbox(
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lines=5,
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default="There is still some uncertainty that Musk - also chief executive of electric car maker Tesla and rocket company SpaceX - will pull off his planned buyout.",
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),
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outputs="html",
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)
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iface.launch()
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