Instructions to use allenai/led-base-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/led-base-16384 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384") model = AutoModelForSeq2SeqLM.from_pretrained("allenai/led-base-16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from allenai/led-base-16384: direct link, hf CLI and curl.
- Browser
- Download file 862 Bytes
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https://huggingface.co/allenai/led-base-16384/resolve/main/README.md
- Command line
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hf download hf://allenai/led-base-16384/README.md
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curl -L -o README.md https://huggingface.co/allenai/led-base-16384/resolve/main/README.md
862 Bytes
metadata
language: en
license: apache-2.0
Introduction
Allenai's Longformer Encoder-Decoder (LED).
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.
This model is especially interesting for long-range summarization and question answering.
Fine-tuning for down-stream task
This notebook shows how led-base-16384 can effectively be fine-tuned on a downstream task.