https://github.com/BangLab-UdeM-Mila/NLP4MatSci-HoneyBee
공식 코드HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
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Sylber: Syllabic Embedding Representation of Speech from Raw Audio
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Evaluating Large Language Models with Human Feedback: Establishing a Swedish Benchmark
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Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
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The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
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MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language Model
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Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval
On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.