from nubia import Nubia
import gradio
nubia = Nubia()
def predict(inp_1, inp_2):
features = nubia.score(inp_1, inp_2, get_features=True)
labels = {k: v for k, v in features["features"].items()}
labels["Pct logical contradiction"] = labels["contradiction"]
del labels["contradiction"]
labels["Pct logical agreeement"] = labels["logical_agreement"]
del labels["logical_agreement"]
labels["Pct irrelevancy (or new information)"] = labels["irrelevancy"]
del labels["irrelevancy"]
labels["Semantic Similarity (out of 5.0)"] = labels["semantic_relation"]
del labels["semantic_relation"]
del labels["grammar_hyp"]
del labels["grammar_ref"]
return {"nubia_score": features["nubia_score"]}, labels
title = "NUBIA: A Neural Metric for Text Generation"
description = "NUBIA stands for 'NeUral Based Interchangeability Assessor'. \n NUBIA gives a score on a scale of 0 to 1 reflecting how much it thinks the candidate text is interchangeable with the reference text. It also shows its rationale for the score obtained by comparing the candidate and reference text."
inputs = [
gradio.inputs.Textbox(lines=5, label="Reference Text"),
gradio.inputs.Textbox(lines=5, label="Candidate Text")
]
outputs = [
gradio.outputs.Label(label="Interchangeability Score"),
gradio.outputs.JSON(label="Neural Features (Explanation)")
def main():
plugin = LogDevicePlugin()
shell = Nubia(name="ldshell", plugin=plugin, command_pkgs=commands)
sys.exit(shell.run())
import sys
import commands
from nubia import Nubia, Options
from extra.tfx_plugin import TFXPlugin
if __name__ == "__main__":
plugin = TFXPlugin()
shell = Nubia(
name="tfx_template",
command_pkgs=commands,
plugin=plugin,
options=Options(persistent_history=False,
auto_execute_single_suggestions=False),
)
sys.exit(shell.run())
def cli_main():
plugin = NubiaSuzieqPlugin()
shell = Nubia(name="suzieq",
plugin=plugin,
options=Options(persistent_history=True))
sys.exit(shell.run())
def main():
plugin = LogDevicePlugin()
shell = Nubia(name="ldshell", plugin=plugin)
sys.exit(shell.run())
