Instructions to use MingZhong/DialogLED-large-5120 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MingZhong/DialogLED-large-5120 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MingZhong/DialogLED-large-5120") model = AutoModelForSeq2SeqLM.from_pretrained("MingZhong/DialogLED-large-5120", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://arxiv.org/abs/2109.02492). | |
| ## 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 large version of DialogLED, the input length is limited to 5,120 in the pre-training phase. | |
| ## Finetuning for Downstream Tasks | |
| Please refer to [our GitHub page](https://github.com/microsoft/DialogLM). |