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The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other
👫ReBot - Generation Module⏰
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 generation module. You can check summarization module on this repoistory.
🚨 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.
Model description
- Paper: Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
- Dataset : Conversation Chronicles
- Generation Module of Model : this repoistory
- Summarization Module of Model : chronological summarization module
Load with Transformers
To load our dataset with Hugging Face Transformers, please use the following code:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("jihyoung/rebot-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("jihyoung/rebot-generation")
Citation Information
@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",
}
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