
Model from the preprint [Unlimiformer: Long-Range Transformers with Unlimited Length Input](https://arxiv.org/abs/2305.01625).

This model was finetuned from a BART-base model using the retrieval-augmented training strategy described in section 3.2 of the paper. It was finetuned on the dataset BookSum (full-book setting).

*The inference demo is disabled because you must add the Unlimiformer files to your repo before this model can handle unlimited length input!* See the [Unlimiformer GitHub](https://github.com/abertsch72/unlimiformer) for setup instructions. 
{"datasets": ["abertsch/booksum-fullbooks"], "pipeline_tag": "text2text-generation", "inference": false}
transformers pytorch bart feature-extraction text2text-generation dataset:abertsch/booksum-fullbooks arxiv:2305.01625 region:us
TAGS
#transformers #pytorch #bart #feature-extraction #text2text-generation #dataset-abertsch/booksum-fullbooks #arxiv-2305.01625 #region-us 


Model from the preprint Unlimiformer: Long-Range Transformers with Unlimited Length Input.

This model was finetuned from a BART-base model using the retrieval-augmented training strategy described in section 3.2 of the paper. It was finetuned on the dataset BookSum (full-book setting).

*The inference demo is disabled because you must add the Unlimiformer files to your repo before this model can handle unlimited length input!* See the Unlimiformer GitHub for setup instructions. 
TAGS
#transformers #pytorch #bart #feature-extraction #text2text-generation #dataset-abertsch/booksum-fullbooks #arxiv-2305.01625 #region-us 
