From the model card
Fields below are copied from the tags and counters on the HuggingFace repository google-bert/bert-base-uncased at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- google-bert
- Pipeline tag
- fill-mask
- Library
- Transformers
- Framework tags
- PyTorch, TensorFlow, JAX, Rust (candle)
- Weight formats
- Core ML, ONNX, safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Language tags
- English (en)
- Papers cited
- arXiv:1810.04805
- Datasets declared
- bookcorpus, wikipedia
- Downloads (HF counter at last fetch)
- 58,556,227
- Likes (HF counter at last fetch)
- 2,910
- Model card
- https://huggingface.co/google-bert/bert-base-uncased
Use cases
- Fine-tuning for text classification (sentiment, topic, intent)
- Named entity recognition with a token classification head
- Extractive question answering on short passages
- Sentence embedding via mean pooling of hidden states
- Transfer learning starting point for domain-specific NLP tasks
Pros
- Extensively benchmarked — failure modes and quirks well documented
- Multi-framework support: PyTorch, TensorFlow, JAX, CoreML, ONNX, Rust
- Apache 2.0 license; large ecosystem of domain-specific fine-tuned checkpoints
- Low barrier for integration in HuggingFace-based pipelines
Cons
- Lowercase tokenization breaks case-sensitive tasks like proper noun NER
- 512-token context window insufficient for long documents without chunking
- Encoder-only architecture cannot generate free-form text
- Outperformed by DeBERTa and more recent encoders on most NLU benchmarks
- No multilingual capability in the base checkpoint
Tags
transformerspytorchtfjaxrustcoremlonnxsafetensorsbertfill-maskexbertendataset:bookcorpusdataset:wikipediaarxiv:1810.04805license:apache-2.0endpoints_compatibledeploy:sagemakerdeploy:azureregion:us