Downloadbert-base-cased is a foundational transformer model pretrained on English using masked language modeling (MLM) and next sentence prediction (NSP). It is case-sensitive, treating "English" and "english" as distinct, making it suitable for tasks where casing matters. The model uses a bidirectional attention mechanism to deeply understand sentence structure, trained on BookCorpus and English Wikipedia. With 109M parameters and WordPiece tokenization (30K vocab size), it captures rich contextual embeddings. It is mostly intended for fine-tuning on downstream NLP tasks such as classification, token labeling, or question answering. The model can also be used out-of-the-box for masked token prediction using Hugging Face’s fill-mask pipeline. Though trained on neutral data, it still inherits and reflects societal biases present in the corpus.
Features
- Trained with masked language modeling and next sentence prediction
- Case-sensitive (treats “Apple” and “apple” differently)
- Bidirectional encoder using transformer architecture
- Pretrained on BookCorpus and English Wikipedia
- WordPiece tokenizer with 30,000 vocabulary tokens
- Fine-tuneable for classification, NER, QA, and other NLP tasks
- Hugging Face integration via PyTorch, TensorFlow, and JAX
- Outputs contextual embeddings for entire sequences or tokens
Project Samples
