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Language Modeling
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://gitlab.com/guyemerson/pixie
공식 코드https://gitlab.com/kavyamanohar/ml-subword-asr
공식 코드Syllable Subword Tokens for Open Vocabulary Speech Recognition in Malayalam
https://gitlab.com/matej.martinc/tnt_kid
공식 코드TNT-KID: Transformer-based Neural Tagger for Keyword Identification
https://gitlab.com/necla-ml/grounding
공식 코드https://gitlab.com/teletton/diploma
공식 코드Detection of depression on social networks using transformers and ensembles
https://gitlab.irlab.org/irlab/cabuxa
공식 코드Conversations in Galician: a Large Language Model for an Underrepresented Language
https://huggingface.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B
공식 코드https://huggingface.co/alykassem/FLAN-T5-Paraphraser
공식 코드https://huggingface.co/emilgoh/mistral-verilog
공식 코드From English to ASIC: Hardware Implementation with Large Language Model
https://huggingface.co/facebook/muppet-roberta-base
공식 코드Muppet: Massive Multi-task Representations with Pre-Finetuning
https://huggingface.co/facebook/muppet-roberta-large
공식 코드Muppet: Massive Multi-task Representations with Pre-Finetuning
https://huggingface.co/facebook/xlm-v-base
공식 코드XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models
https://huggingface.co/fxtentacle/wav2vec2-xls-r-1b-tevr
공식 코드TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
https://huggingface.co/jslin09/bloom-560m-finetuned-fraud
공식 코드Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model
https://huggingface.co/uitnlp/visobert
공식 코드ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing
https://huggingface.co/wyu1/DictBERT
공식 코드Dict-BERT: Enhancing Language Model Pre-training with Dictionary
https://huggingface.co/zequnl/molxpt
공식 코드MolXPT: Wrapping Molecules with Text for Generative Pre-training
https://worksheets.codalab.org/worksheets/0x5a7d0fe35b144ad68998d74891a31ed6
공식 코드https://worksheets.codalab.org/worksheets/0x9987b5d9cce74cf4b2a5f84b54ee447b
공식 코드https://worksheets.codalab.org/worksheets/0xa915ba2f8b664ddf8537c83bde80cc8c
공식 코드https://worksheets.codalab.org/worksheets/0xf8122ebd24e94209a2a1764007509098
공식 코드https://bitbucket.org/paladinanalytics/direct-preference-optimization
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://github.com/1-punchMan/CLTS
https://github.com/10-zin/Synthesizer
Synthesizer: Rethinking Self-Attention in Transformer Models
https://github.com/1024er/cbert_aug
https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/123wowow123/chronopin
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
https://github.com/12kimih/hicupid
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://github.com/1hb6s7t/contrib
https://github.com/1wy/bert
https://github.com/201419/Optimizer-PyTorch
https://github.com/2023-MindSpore-1/ms-code-12/tree/main/Bert
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
https://github.com/2023-MindSpore-1/ms-code-13/tree/main/GPT
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/jasper
https://github.com/2023-MindSpore-1/ms-code-153
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.