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
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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
| 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](https://arxiv.org/abs/1810.04805) and first released in | |
| [this repository](https://github.com/google-research/bert). 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. |