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---
license: apache-2.0
datasets: billsum
tags:
- summarization
model-index:
- name: d0r1h/LEDBill
results:
- task:
type: summarization
name: Summarization
dataset:
name: billsum
type: billsum
config: default
split: test
metrics:
- name: ROUGE-1
type: rouge
value: 38.6502
verified: true
- name: ROUGE-2
type: rouge
value: 18.5458
verified: true
- name: ROUGE-L
type: rouge
value: 25.6561
verified: true
- name: ROUGE-LSUM
type: rouge
value: 33.1575
verified: true
- name: loss
type: loss
value: 2.1305277347564697
verified: true
- name: gen_len
type: gen_len
value: 288.372
verified: true
---
# Longformer Encoder-Decoder (LED) fine-tuned on Billsum
This model is a fine-tuned version of [led-base-16384](https://huggingface.co/allenai/led-base-16384) on the [billsum](https://huggingface.co/datasets/billsum) dataset.
As described in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) by Iz Beltagy, Matthew E. Peters, Arman Cohan, *led-base-16384* was initialized from [*bart-base*](https://huggingface.co/facebook/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
```Python
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](https://colab.research.google.com/drive/1iEEFbWeTGUSDesmxHIU2QDsPQM85Ka1K?usp=sharing) shows how *led* can effectively be used for downstream task such summarization.
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