2 dépôts
Awesome GitHub RepositoriesEncoder-Decoder Training Methods
Training procedures for sequence-to-sequence models that encode input and decode output sequences.
Distinct from Encoder-Decoder Model Integrations: Distinct from Encoder-Decoder Model Integrations: focuses on the training process itself, not integration connectors.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Encoder-Decoder Training Methods. Refine with filters or upvote what's useful.
Awesome Encoder-Decoder Training Methods GitHub Repositories
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- datawhalechina/so-large-lm
datawhalechina/so-large-lm
7,400Voir sur GitHubThis project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu
Explains training methods for sequence-to-sequence encoder-decoder architectures.
- bentrevett/pytorch-seq2seq
bentrevett/pytorch-seq2seq
5,697Voir sur GitHubThis is a collection of educational Jupyter Notebook tutorials that teach sequence-to-sequence modeling using PyTorch and TorchText, focused on neural machine translation. The project provides hands-on guides for building and training encoder-decoder architectures with recurrent neural networks like LSTM and GRU, implementing attention mechanisms that allow the decoder to focus on relevant input tokens during sequence generation. The tutorials cover the full pipeline of machine translation, from tokenizing multilingual text using language-specific tokenizers to training multi-layer encoder-de
Teaches building and training multi-layer LSTM/GRU encoder-decoder architectures for machine translation.
Jupyter Notebookattentioncnn-seq2seqencoder-decoder
