Instructions to use NasimB/bert-dp-second with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-dp-second with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-dp-second")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-dp-second") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-dp-second", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NasimB/bert-dp-second: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/NasimB/bert-dp-second/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NasimB/bert-dp-second/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NasimB/bert-dp-second/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 14281dc378c582b6f864cfd4d28a8e02eadc197330af6b07a1af7e18c1935e72
- Size of remote file:
- 437 MB
- SHA256:
- 7ef74b22c1e258597d6fad96891dbed7a04a3dbaee5712042a1936f0fd38d913
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