Transformer-based Language Model Framework
A Transformer-based Language Model Framework is a language model framework that is a transformer-based model framework (which that enables the development, training, and deployment of language models based on the transformer architecture).
- Context:
- It can (typically) provide infrastructure for training Transformer Models on large datasets.
- It can (often) include tools and libraries for fine-tuning pre-trained Language Models on specific tasks.
- It can range from being a General-Purpose Language Model Framework to being a Specific-Purpose Language Model Framework.
- It can support various programming languages and computational platforms.
- It can facilitate the integration of Transformer Models into applications for tasks like text generation, classification, and translation.
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- Example(s):
- TensorFlow and PyTorch frameworks, which offer extensive support for building and training Transformer Models like BERT and GPT.
- Hugging Face's Transformers library, which provides a broad range of pre-trained Transformer Models easily adaptable for various NLP tasks.
- DeBERTa Framework.
- BERT Framework.
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- Counter-Example(s):
- See: BERT Framework, GPT Framework, XLNet Framework, RoBERTa Framework.