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Longformer Encoder-Decoder (LED) fine-tuned on Billsum

This model is a fine-tuned version of led-base-16384 on the billsum dataset.

As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, led-base-16384 was initialized from bart-base since both models share the exact same architecture. To be able to process 16K tokens, bart-base's position embedding matrix was simply copied 16 times.

How to use

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

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

tokenizer = AutoTokenizer.from_pretrained("d0r1h/LEDBill")
model = AutoModelForSeq2SeqLM.from_pretrained("d0r1h/LEDBill", return_dict_in_generate=True).to(device)

case = "......."

input_ids = tokenizer(case, return_tensors="pt").input_ids.to(device)
global_attention_mask = torch.zeros_like(input_ids)
global_attention_mask[:, 0] = 1

sequences = model.generate(input_ids, 
                           global_attention_mask=global_attention_mask).sequences
summary = tokenizer.batch_decode(sequences, 
                                 skip_special_tokens=True)
                                 

Evaluation results

When the model is used for summarizing Billsum documents(10 sample), it achieves the following results:

Model rouge1-f rouge1-p rouge2-f rouge2-p rougeL-f rougeL-p
LEDBill 34 37 15 16 30 32
led-base 2 15 0 0 2 15

This notebook shows how led can effectively be used for downstream task such summarization.

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Dataset used to train d0r1h/LEDBill

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Evaluation results