transformers pytorch bart text2text-generation conversational en dataset:jihyoung/ConversationChronicles arxiv:2310.13420 license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- jihyoung/ConversationChronicles
language:
- en
license: cc-by-4.0
pipeline_tag: conversational
---

# 👫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](https://huggingface.co/jihyoung/rebot-summarization).

🚨 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](https://arxiv.org/abs/2310.13420)
+ Dataset : [Conversation Chronicles](https://huggingface.co/datasets/jihyoung/ConversationChronicles)
+ Generation Module of Model : this repoistory
+ Summarization Module of Model : [chronological summarization module](https://huggingface.co/jihyoung/rebot-summarization)

## Load with Transformers

To load our dataset with Hugging Face Transformers, please use the following code:

```python
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",
}
```
