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#encoder-decoder

Every item tagged encoder-decoder, newest first.

2 items

Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models

Researchers propose using pre-trained language model checkpoints to warm-start encoder-decoder models, improving performance and efficiency. This approach enables leveraging large-scale pre-trained models for downstream tasks. You can apply this method to various NLP tasks. The technique reduces training time and improves model performance.

Key takeaways

Pre-trained language model checkpoints improve encoder-decoder model performance.
Warm-starting reduces training time for NLP tasks.
Method applicable to various downstream tasks.

HHugging Face Blog#natural-language-processing #pre-trained-models #encoder-decoder

modelsOct 10

Transformer-based Encoder-Decoder Models

The transformer-based encoder-decoder architecture combines two core components: an encoder that processes input sequences and a decoder that generates output sequences. This design enables flexible applications like machine translation, text summarization, and question answering. You can use pre-trained encoder-decoder models for various natural language processing tasks. The architecture has become a standard approach in the field.

Key takeaways

Combines encoder and decoder components for sequence-to-sequence tasks.
Enables applications like translation, summarization, and question answering.
Pre-trained models are available for various NLP tasks.

HHugging Face Blog#transformers #encoder-decoder #nlp