nemoguardrails.library.factchecking.align_score.server

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Module Contents

Classes

NameDescription
AlignScoreRequest-

Functions

Data

app

cli_app

device

models_path

API

class nemoguardrails.library.factchecking.align_score.server.AlignScoreRequest()

Bases: BaseModel

claim
str
evidence
str
nemoguardrails.library.factchecking.align_score.server.alignscore_base(
request: nemoguardrails.library.factchecking.align_score.server.AlignScoreRequest
)
nemoguardrails.library.factchecking.align_score.server.alignscore_large(
request: nemoguardrails.library.factchecking.align_score.server.AlignScoreRequest
)
nemoguardrails.library.factchecking.align_score.server.get_alignscore(
model,
evidence: str,
claim: str
) -> dict
nemoguardrails.library.factchecking.align_score.server.get_model(
model: str
)

Initialize a model.

Args model: The type of the model to be loaded, i.e. “base”, “large”.

nemoguardrails.library.factchecking.align_score.server.hello_world()
nemoguardrails.library.factchecking.align_score.server.start(
port: int = typer.Option(default=5000, ...,
models: typing.List[str] = typer.Option(default=['base...,
initialize_only: bool = typer.Option(default=False,...
)
nemoguardrails.library.factchecking.align_score.server.app = FastAPI()
nemoguardrails.library.factchecking.align_score.server.cli_app = typer.Typer()
nemoguardrails.library.factchecking.align_score.server.device = os.environ.get('ALIGN_SCORE_DEVICE', 'cpu')
nemoguardrails.library.factchecking.align_score.server.models_path = os.environ.get('ALIGN_SCORE_PATH')