led-base-16384
Maintainer: allenai
40
🏋️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
led-base-16384 is a long-document transformer model initialized from the bart-base model. To enable processing of up to 16,384 tokens, the position embedding matrix was simply copied 16 times. This model is especially interesting for long-range summarization and question answering tasks.
As described in the Longformer: The Long-Document Transformer paper by Beltagy et al., the Longformer Encoder-Decoder (LED) model uses a combination of sliding window (local) attention and global attention to effectively process long documents.
The model was released by Allenai, a non-profit AI research institute. Similar Longformer-based models include the longformer-base-4096 and the led-base-book-summary and led-large-book-summary models fine-tuned for book summarization.
Model inputs and outputs
led-base-16384 is a text-to-text transformer model. It takes a sequence of text as input and generates a sequence of text as output.
Inputs
- A sequence of text up to 16,384 tokens in length
Outputs
- A generated sequence of text summarizing or answering questions about the input
Capabilities
The model is capable of processing very long documents, up to 16,384 tokens. This makes it suitable for tasks like long-form summarization, where it can effectively capture the key information in lengthy texts. The combination of local and global attention also allows the model to understand long-range dependencies, which is valuable for question answering on complex passages.
What can I use it for?
led-base-16384 can be fine-tuned on a variety of downstream tasks that involve text generation from long-form inputs, such as:
- Summarizing long articles, papers, or books
- Answering questions about detailed, information-dense passages
- Generating reports or analytical summaries from large datasets
- Extending the capabilities of chatbots and virtual assistants to handle more complex queries
The provided notebook demonstrates how to effectively fine-tune the model for downstream tasks.
Things to try
One interesting aspect of the led-base-16384 model is its ability to process very long inputs. This can be especially useful for tasks like long-form text summarization, where the model can capture the key points and themes across an entire document, rather than just focusing on the most recent content.
Another potential application is question answering on complex, information-dense passages. The model's combination of local and global attention mechanisms allows it to understand long-range dependencies and provide more comprehensive answers to queries about detailed texts.
Researchers and developers could explore fine-tuning the model on domain-specific datasets to create customized solutions for their particular use cases, whether that's summarizing technical reports, answering questions about legal documents, or generating analytical insights from large datasets.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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