- Contextual Understanding:
Bidirectional Context: Unlike traditional models that process text in a unidirectional manner, transformers like BERT use bidirectional ...
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- Contextual Understanding:
Bidirectional Context: Unlike traditional models that process text in a unidirectional manner, transformers like BERT use bidirectional attention mechanisms to understand context from both directions. This allows for a more nuanced interpretation of meaning and better handling of ambiguity.
- Self-Attention Mechanism: Transformers use self-attention to weigh the importance of different words in a sentence relative to each other, enabling the model to capture complex dependencies and relationships over long distances in the text.
- Transfer Learning: Models like GPT and BERT are pretrained on large corpora and can be fine-tuned for specific tasks. This transfer learning approach allows these models to leverage extensive knowledge and adapt to various NLP applications with minimal additional training.
- Text Generation: Transformers, particularly models like GPT, excel in generating coherent and contextually relevant text. This capability is utilized in applications like chatbots, content creation, and automated summarization.
- Resource Requirements: Training large transformer models requires significant computational resources, including powerful GPUs or TPUs and extensive memory. This high resource demand can be a barrier for many organizations and researchers.
Energy Consumption: The computational intensity of training these models also results in high energy consumption, raising concerns about the environmental impact of large-scale AI research.
- Data Quantity and Quality: Transformers require vast amounts of high-quality training data to achieve optimal performance. Gathering and curating this data can be resource-intensive and challenging, especially for less-resourced languages or specialized domains.
