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ReBART with Reverse Positional Embeddings

This is a custom BART model fine-tuned on the CNN/DailyMail dataset with reverse positional embeddings to better control the length of generated summaries.

How to use

from transformers import AutoTokenizer
from ReBART.modeling_rebart import ReBartForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("Ivanhoe9/rpebart-large-cnn")

license: apache-2.0 language: - en tags: - summarization - bart - custom - control-length datasets: - cnn_dailymail base_model: - facebook/bart-large

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Safetensors

Model size

0.4B params

Tensor type

F32

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