Instructions to use MingZhong/DialogLED-base-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MingZhong/DialogLED-base-16384 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MingZhong/DialogLED-base-16384") model = AutoModelForSeq2SeqLM.from_pretrained("MingZhong/DialogLED-base-16384") - Notebooks
- Google Colab
- Kaggle
DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization.
Introduction
DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pre-training task on a large amount of long dialogue data for further training. Here is a base version of DialogLED, the input length is limited to 16,384 in the pre-training phase.
Finetuning for Downstream Tasks
Please refer to our GitHub page.