There are several popular models used for generating contextualized embeddings. Some of them include:

  1. BERT (Bidirectional Encoder Representations from Transformers): BERT is a pre-training language model that uses a bi-directional transformer architecture. It is trained on a large corpus of text to learn contextual embeddings. BERT has achieved state-of-the-art performance on various natural language processing tasks.

  2. GPT (Generative Pretrained Transformer): GPT is another powerful language model that uses transformer architecture. It is trained using unsupervised learning by predicting the next word in a sentence. GPT has been widely used for tasks like text generation, summarization, and question answering.

  3. Transformer-XL: Transformer-XL is an extension of the original transformer model. It addresses the limitation of the fixed-length context window in standard transformers by introducing a recurrence mechanism. This model is able to capture long-range dependencies and generate better contextualized embeddings.

  4. ELMO (Embeddings from Language Models): ELMO uses a combination of both forward and backward LSTM models to generate embeddings. It learns context-dependent representations by training on a large text corpus. ELMO has been widely used for tasks like sentiment analysis, named entity recognition, and text classification.

  5. RoBERTa (Robustly Optimized BERT approach): RoBERTa is a modified version of BERT that is trained with larger batch sizes and more data. It has achieved better performance on several benchmarks compared to the original BERT model.

  6. XLM (Cross-lingual Language Model): XLM is a language model that is trained on multiple languages simultaneously. It learns to encode the shared information across different languages, which helps in cross-lingual transfer. XLM can be used to generate cross-lingual embeddings that capture the context in a multi-lingual setting.

  These are just some of the popular models used for generating contextualized embeddings. Each model has its own advantages and applications, and the choice of model depends on the specific task and requirements.