Topic: bart Goto Github
Some thing interesting about bart
Some thing interesting about bart
bart,Abstractive and Extractive Text summarization using Transformers.
User: aj-naik
bart,Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper
Organization: amazon-science
bart,Code for Findings of EMNLP 2022 short paper "CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model".
User: andychiangsh
Home Page: https://cdgp-demo.nlpnchu.org/
bart,Multilingual/multidomain question generation datasets, models, and python library for question generation.
User: asahi417
Home Page: https://www.autoqg.net
bart,JAX implementation of the bart-base model
User: ayaka14732
Home Page: https://arxiv.org/abs/1910.13461
bart,An English-to-Cantonese machine translation model
User: ayaka14732
bart,TrAVis: Visualise BERT attention in your browser
User: ayaka14732
Home Page: https://ayaka14732.github.io/TrAVis/
bart,A project improves the quality and accuracy of the Vietnamese language.
User: bmd1905
bart,LightSeq: A High Performance Library for Sequence Processing and Generation
Organization: bytedance
bart,Script to pre-train hugginface transformers BART with Tensorflow 2
User: cosmoquester
bart,Implementation and helper scripts for the BART-TL model - https://www.aclweb.org/anthology/2021.eacl-main.121/
User: cristianviorelpopa
bart,Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
Organization: dbiir
Home Page: https://github.com/dbiir/UER-py/wiki
bart,MinT: Minimal Transformer Library and Tutorials
User: dpressel
bart,A general purpose Gaussian process regression module
User: gattocrucco
Home Page: https://gattocrucco.github.io/lsqfitgp/docs
bart,Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
User: guillaume-be
Home Page: https://docs.rs/crate/rust-bert
bart,A tool to automatically summarize documents abstractively using the BART or PreSumm Machine Learning Model.
User: hhousen
Home Page: https://haydenhousen.com/projects/docsum/
bart,Fine-tuning BART on COVID Dialogue Dataset
User: huangxt39
bart,[KBS] PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation PyTorch Implementation
User: imkett
bart,The first-ever vast natural language generation benchmark for Indonesian, Sundanese, and Javanese. We provide multiple downstream tasks, pre-trained IndoGPT and IndoBART models, and a starter code! (EMNLP 2021)
Organization: indonlp
bart,Automated Categorization: Utilizing the power of neural networks, this project offers an automated solution to categorize bank descriptions, reducing manual effort and enhancing efficiency while maintaining privacy.
User: j-convey
bart,About Code for the paper "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP 2023 Findings)
User: jongwooko
Home Page: https://arxiv.org/abs/2310.10054
bart,Code for EMNLP 2021 paper "Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization"
User: junpliu
bart,Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer (ACL 2021)
User: laihuiyuan
Home Page: https://arxiv.org/abs/2105.06947
bart,code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"
User: mikewangwzhl
bart,Extract Molecular SMILES embeddings from language models pre-trained with various objectives architectures.
Organization: moleculetransformers
bart,Cybertron: the home planet of the Transformers in Go
Organization: nlpodyssey
bart,Self-contained Machine Learning and Natural Language Processing library in Go
Organization: nlpodyssey
bart,Abstractive text summarization by fine-tuning seq2seq models.
User: nsi319
bart,Implement Question Generator with SOTA pre-trained Language Models (RoBERTa, BERT, GPT, BART, T5, etc.)
User: p208p2002
Home Page: https://huggingface.co/p208p2002/bart-squad-qg-hl
bart,Source codes and dataset of Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge
User: pkchat-focus
bart,An application that summarizes and classifies long text using zero-shot transfer-learning hosted on Huggingface spaces.
User: pleonova
Home Page: https://huggingface.co/spaces/pleonova/multi-label-summary-text
bart,Point-and-click bartCause analysis and causal inference education
Organization: priism-center
Home Page: https://apsta.shinyapps.io/thinkCausal/
bart,Official Repository for The Paper, PianoBART: Symbolic Piano Music Understanding and Generating with Large-Scale Pre-Training
User: rs2002
bart,Code for our Paper, 'Summaformers @ LaySumm 20, LongSumm 20' at EMNLP 2020, Scholarly Document Processing Workshop
User: sayarghoshroy
bart,KorQuAD Korean domain Question Generation module based on KoBART
User: seoneun
bart,TextGen: Implementation of Text Generation models, include LLaMA, BLOOM, GPT2, BART, T5, SongNet and so on. 文本生成模型,实现了包括LLaMA,ChatGLM,BLOOM,GPT2,Seq2Seq,BART,T5,UDA等模型的训练和预测,开箱即用。
User: shibing624
bart,Pytorch implementation of baseline models of KQA Pro, a large-scale dataset of complex question answering over knowledge base.
User: shijx12
Home Page: http://thukeg.gitee.io/kqa-pro/
bart,Comparing state of the art models for text summary generation
User: singhsidhukuldeep
bart,Build and train state-of-the-art natural language processing models using BERT
User: sudharsan13296
Home Page: https://www.amazon.com/gp/product/B08LLDF377/ref=dbs_a_def_rwt_bibl_vppi_i5
bart,NAACL 2021 - Progressive Generation of Long Text
User: tanyuqian
bart,Tencent Pre-training framework in PyTorch & Pre-trained Model Zoo
Organization: tencent
Home Page: https://github.com/Tencent/TencentPretrain/wiki
bart,Codes for our paper "JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs" (ACL 2021 Findings)
Organization: thu-coai
bart,The French summarization dataset introduced in "BARThez: a Skilled Pretrained French Sequence-to-Sequence Model".
User: tixierae
Home Page: https://arxiv.org/pdf/2010.12321.pdf
bart,This repository contains various models for text summarization tasks. Each model has a separate directory containing the implementation code, pretrained weights, and a Jupyter notebook for testing the model on sample input texts. Feel free to use these models for your own text summarization tasks or to experiment with them further.
User: tuhin-snapd
bart,Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper
User: varunkumar-dev
bart,BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese (INTERSPEECH 2022)
Organization: vinairesearch
bart,Official implementation of the paper "IteraTeR: Understanding Iterative Revision from Human-Written Text" (ACL 2022)
User: vipulraheja
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