BERT Base Cased
| Property | Value |
|---|---|
| Parameter Count | 109M parameters |
| License | Apache 2.0 |
| Paper | Original Paper |
| Training Data | BookCorpus + Wikipedia |
| Framework Support | PyTorch, TensorFlow, JAX |
What is bert-base-cased?
BERT-base-cased is a case-sensitive transformer model pretrained on English text using masked language modeling and next sentence prediction objectives. Developed by Google, it represents a foundational advancement in natural language processing, capable of understanding context in both directions within text sequences.
Implementation Details
The model was trained on 4 cloud TPUs for one million steps with a 256 batch size. It uses WordPiece tokenization with a 30,000 token vocabulary and implements special tokens [CLS] and [SEP] for classification and sentence separation. During training, 15% of tokens are masked with specific replacement strategies to enable bidirectional learning.
- Masked Language Modeling (MLM) with 15% token masking
- Next Sentence Prediction (NSP) for understanding sentence relationships
- 512 token maximum sequence length
- Adam optimizer with learning rate warmup
Core Capabilities
- Bidirectional context understanding
- Case-sensitive text processing
- Token classification and sequence classification
- Question answering tasks
- Feature extraction for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
This model's case-sensitivity and bidirectional architecture make it particularly useful for tasks where capitalization matters. It can capture nuanced contextual information from both directions in text, unlike traditional left-to-right language models.
Q: What are the recommended use cases?
The model excels at tasks requiring whole-sentence understanding, such as sequence classification, token classification, and question answering. It's not recommended for text generation tasks, where models like GPT-2 would be more appropriate.
