language: en

license: apache-2.0

datasets:

- cnn_dailymail

tags:

- summarization

This model is a warm-started *BERT2BERT* ([small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)) model fine-tuned on the *CNN/Dailymail* summarization dataset.

The model achieves a **17.37** ROUGE-2 score on *CNN/Dailymail*'s test dataset.

For more details on how the model was fine-tuned, please refer to

[this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook.

| Metric | # Value |

| ------ | --------- |

| **ROUGE-2** | **17.37** |

from transformers import BertTokenizerFast, EncoderDecoderModel

import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization')

model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device)

def generate_summary(text):

# cut off at BERT max length 512

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 to be summarized here..."

generate_summary(text)