Model Details
Full Model IDgoogle-bert/bert-base-uncased
Pipeline / Taskfill-mask
Librarytransformers
Downloads (all-time)73.1M
Likes2.8K
Last Modified2/19/2024
Author / Orggoogle-bert
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("fill-mask", model="google-bert/bert-base-uncased")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchtfjaxrustcoremlonnxsafetensorsbertfill-maskexbertendataset:bookcorpusdataset:wikipediaarxiv:1810.04805license:apache-2.0endpoints_compatibledeploy:sagemakerdeploy:azureregion:us
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: fill-mask
This model is designed for the fill-mask task. Explore more models for this use case.
All fill-mask Models →📊 Popularity
⬇ Downloads73.1M
❤️ Community Likes2.8K
🛠️ Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.