Instructions to use miguelpr/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use miguelpr/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="miguelpr/bert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("miguelpr/bert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("miguelpr/bert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46f4ff7719cfe412b6278167836825175b870d5cd28e9a76a0616ca638a574ac
- Size of remote file:
- 5.05 kB
- SHA256:
- a3d6de96f93eb87ea81da7365422dc3d8de68ded9044925f89d219e7f5b70922
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.