jihyoung/ConversationChronicles
Viewer β’ Updated β’ 200k β’ 825 β’ 10
How to use jihyoung/rebot-summarization with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline
pipe = pipeline("summarization", model="jihyoung/rebot-summarization") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("jihyoung/rebot-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("jihyoung/rebot-summarization")ReBot is a novel multi-session dialgoue model which can generate dialogue with chronological dynamics! ReBot consists two modules: (1) chronological summarization module; (2) dialogue generation module.
This repoistory for dialogue summarization module. You can check generation module on this repoistory.
π¨ Please be cautious when testing our model with the Hosted Inference API. Our model takes sequences as input, so you should provide sequences as input through the API as well.
To load our dataset with Hugging Face Transformers, please use the following code:
from transformers import pipeline
summarizer = pipeline("summarization", model="jihyoung/rebot-summarization")
@inproceedings{jang-etal-2023-conversation,
title = "Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations",
author = "Jang, Jihyoung and
Boo, Minseong and
Kim, Hyounghun",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.838",
doi = "10.18653/v1/2023.emnlp-main.838",
pages = "13584--13606",
}