https://github.com/ml-jku/helm
공식 코드Semantic HELM: A Human-Readable Memory for Reinforcement Learning
Papers with Code 아카이브
Language Modelling
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ml-jku/semantic-image-text-alignment
공식 코드Linear Alignment of Vision-language Models for Image Captioning
https://github.com/ml-research/q16
공식 코드https://github.com/ml4bio/rhofold
공식 코드Accurate RNA 3D structure prediction using a language model-based deep learning approach
https://github.com/ml4bio/rna-fm
공식 코드Accurate RNA 3D structure prediction using a language model-based deep learning approach
https://github.com/ml4bio/uspnet
공식 코드https://github.com/mlepori1/circuit_probing
공식 코드Uncovering Intermediate Variables in Transformers using Circuit Probing
https://github.com/mlfoundations/rtfm
공식 코드Large Scale Transfer Learning for Tabular Data via Language Modeling
https://github.com/mlfoundations/scaling
공식 코드Language models scale reliably with over-training and on downstream tasks
https://github.com/mlfoundations/tabliblib
공식 코드Large Scale Transfer Learning for Tabular Data via Language Modeling
https://github.com/mlgroupjlu/matrixnuclearnorm
공식 코드https://github.com/mllm-tool/mllm-tool
공식 코드MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning
https://github.com/mlmddetection/mlmddetection
공식 코드Masked Language Model Based Textual Adversarial Example Detection
https://github.com/mlpc-ucsd/BERT_Convolutions
공식 코드Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models
https://github.com/mlpen/lookupffn
공식 코드LookupFFN: Making Transformers Compute-lite for CPU inference
https://github.com/mlplyler/ICDA
공식 코드https://github.com/mlsw/dynamic-embedding-pruning
공식 코드Vocabulary-level Memory Efficiency for Language Model Fine-tuning
https://github.com/mlsw/partial-embedding-matrix-adaptation
공식 코드Vocabulary-level Memory Efficiency for Language Model Fine-tuning
https://github.com/mlvlab/RALF
공식 코드https://github.com/mlvlab/llamo
공식 코드https://github.com/mlzxy/arp
공식 코드Autoregressive Action Sequence Learning for Robotic Manipulation
https://github.com/mmcdermott/structure_inducing_pre-training
공식 코드https://github.com/mmoradi-iut/BioGPT-3
공식 코드GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain
https://github.com/mmuffo94/TransformerLM_arithmetics
공식 코드Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition
https://github.com/mnamysl/nat-acl2021
공식 코드Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling
https://github.com/mnhng/hier-char-emb
공식 코드https://github.com/mnoukhov/async_rlhf
공식 코드Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models
https://github.com/mnoukhov/elastic-reset
공식 코드https://github.com/mnusrat786/emoji-prediction-with-transformer
공식 코드https://github.com/mocherson/aki_bert
공식 코드AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
https://github.com/modeeric/orbit-llama
공식 코드https://github.com/model-similarity/lm-similarity
공식 코드https://github.com/model-similarity/lm-similarity/tree/main/applications
공식 코드https://github.com/modelscope/AdaSeq/tree/master/examples/babert
공식 코드Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
https://github.com/modelscope/adaseq
공식 코드Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
https://github.com/modeltc/llmc
공식 코드LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.