Transformer-based Model Framework
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A Transformer-based Model Framework is a neural model framework that enables the development, training, and deployment of transformer-based models.
- 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.
- ...
- 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.
- ...
- Counter-Example(s):
- Convolutional Neural Network-based Framework, which focuses on different types of neural architectures not based on the transformer model.
- See: BERT Framework, GPT Framework, XLNet Framework, RoBERTa Framework.