PRIMERA-wcep

HF-version model for PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (ACL 2022).

The original code can be found here. You can find the script and notebook to train/evaluate the model in the original github repo.

• Note: due to the difference between the implementations of the original Longformer and the Huggingface LED model, the results of converted models are slightly different. We run a sanity check on both fine-tuned and non fine-tuned models on the Multinews dataset, and show the results below:

| Model | Rouge-1 | Rouge-2 | Rouge-L |

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

| PRIMERA | 42.0 | 13.6 | 20.8|

| PRIMERA-hf | 41.7 |13.6 | 20.5|

| PRIMERA(finetuned) | 49.9 | 21.1 | 25.9|

| PRIMERA-hf(finetuned) | 49.9 | 20.9 | 25.8|

You can use it by

from transformers import (

    AutoTokenizer,

    LEDConfig,

    LEDForConditionalGeneration,

)

tokenizer = AutoTokenizer.from_pretrained('allenai/PRIMERA')

config=LEDConfig.from_pretrained('allenai/PRIMERA')

model = LEDForConditionalGeneration.from_pretrained('allenai/PRIMERA')