Instructions to use lzhang472/bert-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lzhang472/bert-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lzhang472/bert-en")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lzhang472/bert-en") model = AutoModelForMaskedLM.from_pretrained("lzhang472/bert-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7387fd3e240227351515ffadb6d0c9c11b5e342ea873f8b4e0deed81421589e
- Size of remote file:
- 438 MB
- SHA256:
- a3ea250132c61843624645a372206f9e7f8313b946e205dba9180a2696aa03cd
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