mrm8488/bert2bert_shared-german-finetuned-summarization

German BERT2BERT fine-tuned on MLSUM DE for summarization

Model

bert-base-german-cased (BERT Checkpoint)

Dataset

MLSUM is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset. MLSUM de

Results

Set	Metric	# Score
Test	Rouge2 - mid -precision	33.04
Test	Rouge2 - mid - recall	33.83
Test	Rouge2 - mid - fmeasure	33.15

Usage

import torch
from transformers import BertTokenizerFast, EncoderDecoderModel
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'mrm8488/bert2bert_shared-german-finetuned-summarization'
tokenizer = BertTokenizerFast.from_pretrained(ckpt)
model = EncoderDecoderModel.from_pretrained(ckpt).to(device)
def generate_summary(text):
   inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
   input_ids = inputs.input_ids.to(device)
   attention_mask = inputs.attention_mask.to(device)
   output = model.generate(input_ids, attention_mask=attention_mask)
   return tokenizer.decode(output[0], skip_special_tokens=True)
   
text = "Your text here..."

generate_summary(text)

Created by Manuel Romero/@mrm8488 with the support of Narrativa Made with ♥ in Spain