Instructions to use Contents/bert-base-uncased-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Contents/bert-base-uncased-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Contents/bert-base-uncased-test")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Contents/bert-base-uncased-test") model = AutoModel.from_pretrained("Contents/bert-base-uncased-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: fill-mask
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datasets:
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- wikipedia
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---
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# BERT base model (uncased)
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Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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[this paper](https://arxiv.org/abs/1810.04805) and first released in
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[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
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between english and English.
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Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
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the Hugging Face team.
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