This repository provides a comprehensive summary and collection of resources for pre-trained language models (PTMs) in Natural Language Processing (NLP). It aims to serve researchers and practitioners by consolidating key papers, model explanations, and development trends in the field.

How It Works

The project acts as a curated knowledge base, linking to external resources that detail various NLP pre-training techniques. It categorizes information by model type (e.g., ELMo, BERT, XLNet, RoBERTa, ELECTRA) and by specific techniques like self-supervised learning and model compression (distillation, quantization, pruning). This approach offers a structured overview of the evolution and advancements in PTMs.

Highlighted Details

• Extensive paper aggregation from various GitHub repositories and curated lists.
• Detailed explanations and comparisons of prominent models like BERT, XLNet, and RoBERTa.
• Focus on model compression techniques such as LayerDrop, BERT-of-Theseus, and TinyBERT.
• Covers the historical development from word embeddings to modern PTMs.

Maintenance & Community

This repository appears to be a personal or academic compilation, with ongoing updates indicated. Specific community channels or contributor information are not detailed in the README.

Licensing & Compatibility

The repository itself does not specify a license. The linked external resources may have their own licenses.

Limitations & Caveats

This repository is a collection of links and summaries, not a runnable codebase. Users will need to access and potentially implement the models and techniques described through the provided external links. The "continuous update" status suggests it is a living document rather than a static release.