metadata

tags:
  - generated_from_trainer
  - distilbart
model-index:
  - name: distilbart-finetuned-summarization
    results: []
license: apache-2.0
datasets:
  - cnn_dailymail
  - xsum
  - samsum
  - ccdv/pubmed-summarization
language:
  - en
metrics:
  - rouge

distilbart-finetuned-summarization

This model is a further fine-tuned version of distilbart-cnn-12-6 on the the combination of 4 different summarisation datasets:

Please check out the offical model page and paper:

Training and evaluation data

One can reproduce the dataset using the following code:

from datasets import DatasetDict, load_dataset
from datasets import concatenate_datasets

xsum_dataset = load_dataset("xsum")
pubmed_dataset = load_dataset("ccdv/pubmed-summarization").rename_column("article", "document").rename_column("abstract", "summary")
cnn_dataset = load_dataset("cnn_dailymail", '3.0.0').rename_column("article", "document").rename_column("highlights", "summary")
samsum_dataset = load_dataset("samsum").rename_column("dialogue", "document")

summary_train = concatenate_datasets([xsum_dataset["train"], pubmed_dataset["train"], cnn_dataset["train"], samsum_dataset["train"]])
summary_validation = concatenate_datasets([xsum_dataset["validation"], pubmed_dataset["validation"], cnn_dataset["validation"], samsum_dataset["validation"]])
summary_test = concatenate_datasets([xsum_dataset["test"], pubmed_dataset["test"], cnn_dataset["test"], samsum_dataset["test"]])

raw_datasets = DatasetDict()
raw_datasets["train"] = summary_train
raw_datasets["validation"] = summary_validation
raw_datasets["test"] = summary_test

Inference example

from transformers import pipeline

pipe = pipeline("text2text-generation", model="lxyuan/distilbart-finetuned-summarization")

text = """SINGAPORE: The Singapore Police Force on Sunday (Jul 16) issued a warning over a
fake SMS impersonating as its "anti-scam centre (ASC)". 

"In this scam variant, members of the public would receive a scam SMS from 'ASC',
requesting them to download and install an “anti-scam” app to ensure the security
of their devices," said the police. 

"The fake SMS would direct members of the public to a URL link leading to an
Android Package Kit (APK) file, an application created for Android’s operating
system purportedly from 'ASC'."

The fake website has an icon to download the “anti-scam” app and once downloaded,
Android users are asked to allow accessibility services to enable the service.

While the fake app purportedly claims to help identify and prevent scams by
providing comprehensive protection and security, downloading it may enable
scammers to gain remote access to devices.

"Members of the public are advised not to download any suspicious APK files
on their devices as they may contain malware which will allow scammers to
access and take control of the device remotely as well as to steal passwords
stored in the device," said the police. 

Members of the public are advised to adopt the following precautionary measures,
including adding anti-virus or anti-malware apps to their devices. They should
also disable “install unknown app” or “unknown sources” in their phone settings.

Users should check the developer information on the app listing as well as the
number of downloads and user reviews to ensure it is a reputable and legitimate
app, the police said.

Any fraudulent transactions should be immediately reported to the banks.
"""

pipe(text)

>>>"""The Singapore Police Force has issued a warning over a fake SMS
impersonating as its "anti-scam centre" that asks members of the public
to download an Android app to ensure the security of their devices, the
force said on Sunday. The fake SMS would direct people to a URL link
leading to an Android Package Kit (APK) file, an application created
for Android’s operating system purportedly from "ASC".
"""

Training procedure

Notebook link: here

Training hyperparameters

The following hyperparameters were used during training:

evaluation_strategy="epoch",
save_strategy="epoch",
logging_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
gradient_accumulation_steps=64,
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
weight_decay=0.01,
save_total_limit=2,
num_train_epochs=4,
predict_with_generate=True,
fp16=True,
push_to_hub=True

Training results

Training is still in progress

Epoch	Training Loss	Validation Loss	Rouge1	Rouge2	RougeL	RougeLsum	Gen Len
0	1.779700	1.719054	40.003900	17.907100	27.882500	34.888600	88.893600
1	1.633800	1.710876	40.628800	18.470200	28.428100	35.577500	88.885000
2	1.566100	1.694476	40.928500	18.695300	28.613300	35.813300	88.993700
3	1.515700	1.691141	40.860500	18.696500	28.672700	35.734600	88.457300

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3