Instructions to use nthngdy/headless-bert-bs64-owt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nthngdy/headless-bert-bs64-owt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nthngdy/headless-bert-bs64-owt2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nthngdy/headless-bert-bs64-owt2") model = AutoModelForMaskedLM.from_pretrained("nthngdy/headless-bert-bs64-owt2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f242b405d9383b834172841c058c6fdebd8a649e7182fa980c8d62c16cb129d2
- Size of remote file:
- 495 MB
- SHA256:
- 9e6fe20a59e1bd32e5137401ed9320263df027787abba24bce2794d0ae392cb6
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