---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbart-finetuned-summarization

This model is a further fine-tuned version of [distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the the combination of 4 different summarisation datasets:
- [cnn_dailymail](https://huggingface.co/datasets/cnn_dailymail)
- [samsum](https://huggingface.co/datasets/samsum)
- [xsum](https://huggingface.co/datasets/xsum)
- [ccdv/pubmed-summarization](https://huggingface.co/datasets/ccdv/pubmed-summarization)

Please check out the offical model page and paper:
- [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6)
- [Pre-trained Summarization Distillation](https://arxiv.org/abs/2010.13002)

## Training and evaluation data

One can reproduce the dataset using the following code:

```python
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

```python
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](https://github.com/LxYuan0420/nlp/blob/main/notebooks/distilbart-finetune-summarisation.ipynb)

### 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