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
File size: 605 Bytes
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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. |