https://github.com/thudm/chatglm
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Papers with Code 아카이브
Abstractive Text Summarization
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- 하위 태스크
- 3
- 평가표
- 20
- 보존된 평가 기록
- 98
Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.
Source: Generative Adversarial Network for Abstractive Text Summarization
Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thudm/chatglm2-6b
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thudm/chatglm3
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thudm/swissarmytransformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thudm/visualglm-6b
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
https://github.com/thumt/THUMT
https://github.com/thunlp-mt/ckd
https://github.com/timrozday/spl-indications-bart
https://github.com/tjrwlgns1198/ChatBot
https://github.com/tomdyer10/fake_news
https://github.com/toru34/li_emnlp_2017
Deep Recurrent Generative Decoder for Abstractive Text Summarization
https://github.com/tree-park/kor-to-eng-translation
https://github.com/tree-park/transformer_lm
https://github.com/truclycs/ocr_seq2seq
https://github.com/tshu-w/dbcopilot
https://github.com/tshu-w/llm4em
https://github.com/tvinith/bert
https://github.com/tyxr/bert
https://github.com/u7javed/Transformer-Multi-Language-Translator
https://github.com/uabinf/nlp-fall-2019-project-anuradha_shinjitha
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/uakarsh/Eit-Enhanced-Interactive-Transformer
https://github.com/udnet96/BART-various-finetune
https://github.com/um-arm-lab/efficient-eng-2-ltl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/umeiko/mindspore-seq2seq
https://github.com/uribo/buckyR
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
https://github.com/usyd-fsalab/fp6_llm
https://github.com/uzi0espil/research-papers-implementation/tree/master/Attention%20Is%20All%20You%20Need
https://github.com/valentinmace/noisy-text
https://github.com/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/veekaybee/what_are_embeddings
https://github.com/vgaraujov/seq2seq-spanish-plms
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vijay120/bert
https://github.com/vikashranjan/transformer
https://github.com/vinairesearch/phogpt
https://github.com/vinayak19th/Brevis-2.0
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.