Instructions to use DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles") model = AutoModelForMaskedLM.from_pretrained("DunnBC22/bert-base-uncased-Masked_Language_Model-US_Economic_News_Articles") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2 opened over 2 years ago
by
librarian-bot
Adding `safetensors` variant of this model
#1 opened almost 3 years ago
by
SFconvertbot