mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization
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Bert-mini2Bert-mini Summarization with 🤗EncoderDecoder Framework
This model is a warm-started BERT2BERT (mini) model fine-tuned on the CNN/Dailymail summarization dataset.
The model achieves a 16.51 ROUGE-2 score on CNN/Dailymail's test dataset.
For more details on how the model was fine-tuned, please refer to this notebook.
Results on test set 📝
Model in Action 🚀
from transformers import BertTokenizerFast, EncoderDecoderModel
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization')
model = EncoderDecoderModel.from_pretrained('mrm8488/bert-mini2bert-mini-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)Created by Manuel Romero/@mrm8488 | LinkedIn
Made with <span style="color: #e25555;">♥</span> in Spain