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Model Description
DistilBERT is a smaller, faster, and lighter version of Google's foundational BERT model. Developed by Hugging Face, it uses a technique called knowledge distillation to replicate BERT's behavior while being 40% smaller and 60% faster, retaining 97% of its language understanding capabilities.
How It WorksKnowledge Distillation: A larger, fully-trained model (the "teacher," e.g., BERT) teaches a smaller model (the "student," DistilBERT). Instead of just learning from raw data, the student learns to mimic the exact outputs and predictions of the teacher.
Architectural Changes: DistilBERT is created by completely removing the token-type embeddings, pooler, and cutting the number of transformer layers in half.
- Developed by: Rajesh Gopal
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- Model type: DistilBERT
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Carbon emissions can be estimated using the
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RAJESH GOPAL
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