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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
This is a weights storage for the PaperPersiChat pipeline models.
The pipeline is presented in the paper PaperPersiChat: Scientific Paper Discussion Chatbot using Transformers and Discourse Flow Management
Installation
git lfs install
git clone https://huggingface.co/ai-forever/paper_persi_chat
Usage
- Full pipeline:
See more details on https://github.com/ai-forever/paper_persi_chat
- Single models:
Inference examples: 
Three models (Summarizer, QA module and Response Generator) can be imported using transformers library after weights downloading:
from transformers import BartForConditionalGeneration, BartTokenizer
model_name_or_path = 'paper_persi_chat/distilbart_summarizer' # or 'paper_persi_chat/bart_response_generator'
tokenizer = BartTokenizer.from_pretrained(model_name_or_path)
model = BartForConditionalGeneration.from_pretrained(model_name_or_path).to('cuda')
from transformers import pipeline
model = pipeline("question-answering", model='paper_persi_chat/deberta_qa')
pred = model(question,
context,
max_seq_len=384,
doc_stride=64,
max_answer_len=384)
Citation
If you find our models helpful, feel free to cite our publication PaperPersiChat: Scientific Paper Discussion Chatbot using Transformers and Discourse Flow Management:
@inproceedings{chernyavskiy-etal-2023-paperpersichat,
title = "{P}aper{P}ersi{C}hat: Scientific Paper Discussion Chatbot using Transformers and Discourse Flow Management",
author = "Chernyavskiy, Alexander and
Bregeda, Max and
Nikiforova, Maria",
booktitle = "Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.54",
pages = "584--587",
}
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