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")# pip install -U transformers accelerate # 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
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Download README.md from Contents/bert-base-uncased-test: direct link, hf CLI and curl.
- Browser
- Download file 605 Bytes
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https://huggingface.co/Contents/bert-base-uncased-test/resolve/main/README.md
- Command line
-
hf download hf://Contents/bert-base-uncased-test/README.md
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curl -L -o README.md https://huggingface.co/Contents/bert-base-uncased-test/resolve/main/README.md
605 Bytes
metadata
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: fill-mask
datasets:
- wikipedia
BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.