BERT Large Uncased
| Property | Value |
|---|---|
| Parameter Count | 336M parameters |
| Architecture | 24-layer, 1024 hidden dimension, 16 attention heads |
| License | Apache 2.0 |
| Paper | Original Paper |
What is bert-large-uncased?
BERT Large Uncased is a powerful transformer-based language model developed by Google Research. It represents a significant advancement in natural language processing, trained on a massive corpus of uncased (lowercase) English text from BookCorpus and Wikipedia. The model employs bidirectional training of Transformer, enabling it to understand context from both directions.
Implementation Details
The model features a sophisticated architecture with 24 transformer layers, 1024 hidden dimensions, and 16 attention heads, totaling 336M parameters. It's trained using two innovative objectives: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). The training process involved 4 cloud TPUs in Pod configuration for one million steps with a 256 batch size.
- Trained on BookCorpus (11,038 books) and English Wikipedia
- Uses WordPiece tokenization with 30,000 vocabulary size
- Implements random masking of 15% of input tokens
- Supports both PyTorch and TensorFlow implementations
Core Capabilities
- Masked language modeling for contextual word prediction
- Next sentence prediction for understanding sentence relationships
- Feature extraction for downstream tasks
- High performance on tasks like SQUAD (91.0/84.3 F1/EM) and Multi NLI (86.05% accuracy)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its bidirectional training approach, large-scale architecture, and state-of-the-art performance on various NLP tasks. Its uncased nature makes it particularly suitable for applications where case sensitivity isn't critical.
Q: What are the recommended use cases?
The model excels in sequence classification, token classification, and question answering tasks. It's primarily designed for fine-tuning on downstream tasks rather than direct text generation. Users should note that it's best suited for tasks requiring whole sentence understanding rather than generative tasks.
