Auto Summarization

!pip install lightning-flash

!pip install -U gradsflow

In [ ]:

from flash.core.data.utils import download_data
from flash.text import SummarizationData, SummarizationTask

# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "data/")

# 2. Load the data
datamodule = SummarizationData.from_csv(
    "input",
    "target",
    train_file="data/xsum/train.csv",
    val_file="data/xsum/valid.csv",
    test_file="data/xsum/test.csv",
)

from flash.core.data.utils import download_data from flash.text import SummarizationData, SummarizationTask # 1. Download the data download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", "data/") # 2. Load the data datamodule = SummarizationData.from_csv( "input", "target", train_file="data/xsum/train.csv", val_file="data/xsum/valid.csv", test_file="data/xsum/test.csv", )

In [ ]:

from gradsflow import AutoSummarization

suggested_conf = dict(
    optimizers=["adam"],
    lr=(5e-4, 1e-3),
)

model = AutoSummarization(
    datamodule,
    suggested_backbones="sshleifer/distilbart-cnn-12-6",
    suggested_conf=suggested_conf,
    max_epochs=1,
    optimization_metric="train_loss",
    timeout=5,
)

print("AutoSummarization initialised!")
model.hp_tune()

from gradsflow import AutoSummarization suggested_conf = dict( optimizers=["adam"], lr=(5e-4, 1e-3), ) model = AutoSummarization( datamodule, suggested_backbones="sshleifer/distilbart-cnn-12-6", suggested_conf=suggested_conf, max_epochs=1, optimization_metric="train_loss", timeout=5, ) print("AutoSummarization initialised!") model.hp_tune()

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Last update: September 29, 2021