Instructions to use google-bert/bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-uncased") - Inference
- Notebooks
- Google Colab
- Kaggle
Question about fine-tuning BERT for Named Entity Recognition on custom medical datasets
#100
by 121tester - opened
This is a test discussion created via API to test the UPDATE_DISCUSSIONS_TITLE action.
121tester changed discussion title from Test Discussion for Title Update to Updated Title - First Test
121tester changed discussion title from Updated Title - First Test to Updated Title - Second Test
121tester changed discussion title from Updated Title - Second Test to Bug Fix: Model Loading Issue
121tester changed discussion title from Bug Fix: Model Loading Issue to Feature Request: Add multilingual support π
121tester changed discussion title from Feature Request: Add multilingual support π to Fix
121tester changed discussion title from Fix to Question about fine-tuning BERT for Named Entity Recognition on custom medical datasets