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:
- 7b08b03865da8ebac7093c4585e32775254036d2332f612c0e314262bd1e6545
- Size of remote file:
- 3.31 kB
- SHA256:
- f20b47c56c4e9fbf2f7f567dfbe0e85b9854d2a46824a6e86031651cdb4c2bf6
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