Alternatives and similar repositories for T2R:

Users that are interested in T2R are comparing it to the libraries listed below

• RUCAIBox / ELMER
  This repository is the official implementation of our EMNLP 2022 paper ELMER: A Non-Autoregressive Pre-trained Language Model for Efficie…
  ☆26Updated 2 years ago
• NonvolatileMemory / flash_attn_gqa
  triton ver of gqa flash attn, based on the tutorial
  ☆11Updated 8 months ago
• sustcsonglin / disco-pointer
  Official Implementation of ACL2023: Don't Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span …
  ☆13Updated last year
• VITA-Group / Data-Efficient-Scaling
  [ICML 2023] "Data Efficient Neural Scaling Law via Model Reusing" by Peihao Wang, Rameswar Panda, Zhangyang Wang
  ☆14Updated last year
• Shark-NLP / CAB
  ☆30Updated last year
• da03 / criticize_text_generation
  A method for evaluating the high-level coherence of machine-generated texts. Identifies high-level coherence issues in transformer-based …
  ☆11Updated 2 years ago
• chijames / KERPLE
  ☆19Updated 2 years ago
• gmftbyGMFTBY / Rep-Dropout
  [NeurIPS 2023] Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective
  ☆30Updated last year
• allenai / staged-training
  Staged Training for Transformer Language Models
  ☆32Updated 3 years ago
• Doraemonzzz / xmixers
  Xmixers: A collection of SOTA efficient token/channel mixers
  ☆11Updated 5 months ago
• lyutyuh / structured-span-selector
  A Structured Span Selector (NAACL 2022). A structured span selector with a WCFG for span selection tasks (coreference resolution, semanti…
  ☆21Updated 2 years ago
• kyegomez / Reka-Torch
  Implementation of the model: "Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models" in PyTorch
  ☆30Updated last week
• HazyResearch / prefix-linear-attention
  ☆53Updated 9 months ago
• ictnlp / Seq-NAT
  Source code for <Sequence-Level Training for Non-Autoregressive Neural Machine Translation>.
  ☆24Updated 3 years ago
• ducdauge / sft-llm
  Scaling Sparse Fine-Tuning to Large Language Models
  ☆16Updated last year
• sanagno / adaptively_sparse_attention
  ☆21Updated last year
• jenni-ai / T2FW
  Fine-Tuning Pre-trained Transformers into Decaying Fast Weights
  ☆19Updated 2 years ago
• yikangshen / megablocks
  ☆19Updated 10 months ago
• yxuansu / Contrastive_Search_versus_Contrastive_Decoding
  An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text Generation
  ☆27Updated 10 months ago
• ictnlp / FA-DAT
  Official Implementation for the ICLR2023 paper "Fuzzy Alignments in Directed Acyclic Graph for Non-autoregressive Machine Translation"
  ☆13Updated 2 years ago
• twinkle0331 / Xcompression
  [ICLR 2022] Code for paper "Exploring Extreme Parameter Compression for Pre-trained Language Models"(https://arxiv.org/abs/2205.10036)
  ☆22Updated last year
• whyNLP / Probabilistic-Transformer
  A probabilitic model for contextual word representation. Accepted to ACL2023 Findings.
  ☆23Updated last year
• juvi21 / CoPE-cuda
  Contextual Position Encoding but with some custom CUDA Kernels https://arxiv.org/abs/2405.18719
  ☆22Updated 10 months ago
• MikeWangWZHL / Zemi
  Repo for "Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks" ACL 2023 Findings
  ☆16Updated last year
• emorynlp / seq2seq-corenlp
  ☆13Updated 2 years ago
• haorannlp / mix
  Code for "Mixed Cross Entropy Loss for Neural Machine Translation"
  ☆20Updated 3 years ago
• llyx97 / sparse-and-robust-PLM
  [NeurIPS 2022] "A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models", Yuanxin Liu, Fandong Meng, Zheng Lin, Jiangnan Li…
  ☆21Updated last year
• sustcsonglin / gated_linear_attention_layer
  ☆32Updated last year
• jinlanfu / Polyglot_Prompt
  Code and dataset for Polyglot Prompting: Multilingual Multitask Prompt Training.
  ☆17Updated 2 years ago
• lancopku / MUKI
  [Findings of EMNLP22] From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
  ☆19Updated 2 years ago