How to Use the AllenAI Longformer Encoder-Decoder (LED) for PubMed Data

January 12, 2021

In this guide, we will explore how to effectively use the Longformer Encoder-Decoder (LED) model, particularly the fine-tuned *led-large-16384* checkpoint on the PubMed dataset. This unofficial model has gained significant acclaim due to its competitive performance and is a notable tool for processing scientific literature.

Introduction to LED

The AllenAI Longformer Encoder-Decoder (LED) is engineered for summarizing long documents efficiently. The checkpoint is specifically fine-tuned on a dataset that encompasses a wealth of scientific papers, particularly from PubMed. For those interested, detailed evaluation information is accessible via this notebook.

Results Achieved

The model has achieved an impressive **Rouge-2** score of 19.33 on PubMed, making its performance competitive with state-of-the-art models in summarization tasks. This indicates its efficacy in distilling crucial information from extensive research articles.

Usage Instructions

To utilize this model for summarization, follow the steps outlined below:

Begin by importing the necessary libraries.
Initialize the LED model and tokenizer using the checkpoint mentioned earlier.
Prepare the input data from the PubMed dataset.
Run the model to generate summaries of your input articles.

Sample Code

Let’s walk through an analogy to understand the code better. Think of the entire summarization process as a chef preparing a gourmet dish. The LED model is like a specialized chef who processes an extensive list of ingredients (input text) and prepares a summarized dish (output summary).

from transformers import LEDForConditionalGeneration, LEDTokenizer
import torch

# Initialize the tokenizer and model
tokenizer = LEDTokenizer.from_pretrained("patrickvonplaten/led-large-16384-pubmed")
input_ids = tokenizer(LONG_ARTICLE, return_tensors="pt").input_ids.to("cuda")

# Prepare the attention mask for the model
global_attention_mask = torch.zeros_like(input_ids)
global_attention_mask[:, 0] = 1

# Load the model
model = LEDForConditionalGeneration.from_pretrained("patrickvonplaten/led-large-16384-pubmed", return_dict_in_generate=True).to("cuda")

# Generate the summary
sequences = model.generate(input_ids, global_attention_mask=global_attention_mask)
summary = tokenizer.batch_decode(sequences)

Troubleshooting

If you encounter issues while running the model, here are some troubleshooting steps:

CUDA Errors: Make sure you have a compatible GPU and have installed the required CUDA toolkit.
Out of Memory (OOM) Error: If the model is too large for your GPU, try running it on a smaller batch size or using a CPU instead.
Import Errors: Ensure that you have all the necessary libraries installed, specifically the Transformers library.

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Conclusion

Utilizing the LED model can significantly enhance your ability to extract and summarize valuable insights from extensive scientific texts. With its robust performance on the PubMed dataset, researchers can advance their findings more efficiently.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.