What are Pre-trained Language Models?
Pre-trained language models are machine learning models that have been trained on large amounts of text data and can be fine-tuned for specific natural language processing (NLP) tasks. These models learn general language features, such as grammar, syntax, and semantics, which can be adapted to various NLP tasks, such as sentiment analysis, named entity recognition, and text summarization.
Examples of Pre-trained Language Models
Some popular pre-trained language models include:
- BERT (Bidirectional Encoder Representations from Transformers)
- GPT-3 (Generative Pre-trained Transformer 3)
- RoBERTa (Robustly optimized BERT approach)
- T5 (Text-to-Text Transfer Transformer)
- OpenAI Codex
Resources for Pre-trained Language Models
To learn more about pre-trained language models and their applications, you can explore the following resources:
- Pretrained models, a comprehensive list of pre-trained models provided by Hugging Face
- Fine-tuning a pre-trained model, a tutorial from Hugging Face on how to fine-tune pre-trained models for specific tasks
- Transfer learning with pre-trained models in deep learning, an article on transfer learning and fine-tuning pre-trained models in deep learning