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
finetune bert
#64
by ywxsuperstar - opened
Could it be that the weights of BERT base, or rather this model, are not suitable for the Yelp dataset (with lengths ranging from 180 to 400, and 5 classifications)? Currently, in my fine-tuning for sentiment analysis with 5 classifications, the accuracy can reach a maximum of only 60%. Has anyone else encountered such a situation?