This model is a fine-tuned version of allenai/led-large-16384 on the BookSum dataset (kmfoda/booksum). It aims to generalize well and be useful in summarizing lengthy text for both academic and everyday purposes. - See the Colab demo linked above or try the demo on Spaces To improve summary quality, use encodernorepeatngramsize=3 when calling the pipeline object. This setting encourages the model to utilize new vocabulary and construct an abstractive summary. Load the model into a pipeline object: Feed the text into the pipeline object: Important: For optimal summary quality, use the global attention mask when decoding, as demonstrated in this community notebook, see the definition of…
Open weights
apache-2.0
460M parameters
transformers
The Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization is a model I fine-tuned from allenai/led-base-16384 to condense extensive technical, academic, and narrative content in a fairly generalizable way. - Ideal for summarizing long narratives, articles, papers, textbooks, and other documents. - the sparknotes-esque style leads to 'explanations' in the summarized content, offering insightful output. The model was trained on the BookSum dataset released by SalesForce, which leads to the bsd-3-clause license. The training process involved 16 epochs with parameters tweaked to facilitate very fine-tuning-type training (super low learning rate). This model is the…
Open weights
apache-2.0
162M parameters
transformers
