DistilBERT base uncased is a compact, open-weight transformer model distilled from BERT for English natural language understanding. Developers and researchers can download it from Hugging Face, run it locally with Transformers, or use supported inference services for fill-mask tasks.
In the Foundation models & chat space, Distilbert Base Uncased takes a focused approach. It focuses on running efficient transformer-based language understanding and fill-mask inference without using the larger BERT model. It is built as an open-source project for machine learning developers and researchers. Distilbert Base Uncased is open source under the Apache-2.0 license. Distilbert Base Uncased is available on the web and API, and it can be self-hosted.
Behind Distilbert Base Uncased is Hugging Face community, and it first shipped in 2018. Development happens publicly on GitHub with 164.5k stars and 829 commits in the last 90 days. Key capabilities include fill-mask inference, transformers support, and pyTorch support.
Summary written by a language model from the project’s public pages.
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