Instructions to use Billwzl/bert-base-uncased-New_data_bert1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Billwzl/bert-base-uncased-New_data_bert1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Billwzl/bert-base-uncased-New_data_bert1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Billwzl/bert-base-uncased-New_data_bert1") model = AutoModelForMaskedLM.from_pretrained("Billwzl/bert-base-uncased-New_data_bert1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0440f91822afc89c3af62526d51bb5d7afe2cae64be7208e2b8cf6e9feb3045
- Size of remote file:
- 438 MB
- SHA256:
- 1536b9132ba8a7b05cf94574cc7ffd0aa0c07bbec76cae3115e24163bf8c31fd
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