DeBERTa Framework
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A DeBERTa Framework is a transformer-based LM framework that specifically supports the implementation, training, and fine-tuning of DeBERTa models.
- Context:
- It can (typically) leverage the disentangled attention mechanism to enhance the efficiency of attention computations in Transformer Models.
- It can (often) provide scripts and utilities for both MLM and RTD pre-training objectives.
- It can allow the integration of enhancements like gradient-disentangled embedding sharing as seen in DeBERTaV3.
- It can support various size models from DeBERTa Base to DeBERTa XXLarge.
- It can facilitate fine-tuning on a diverse range of NLP tasks including but not limited to Natural Language Inference, Question Answering, and Text Classification.
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- Example(s):
- Microsoft's implementation of DeBERTa, which includes detailed documentation and code for training and using DeBERTa models.
- The inclusion of DeBERTa models in Hugging Face's model repository, enabling easy access and integration into existing applications.
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- Counter-Example(s):
- General-purpose frameworks that do not include specific optimizations or enhancements for DeBERTa models.
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- See: BERT Framework, ELECTRA Framework, Language Model Optimization Techniques.