Instructions to use NasimB/bert-dp-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NasimB/bert-dp-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NasimB/bert-dp-4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NasimB/bert-dp-4") model = AutoModelForMaskedLM.from_pretrained("NasimB/bert-dp-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NasimB/bert-dp-4: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/NasimB/bert-dp-4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NasimB/bert-dp-4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NasimB/bert-dp-4/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 2d15d958b77b5d28ef51266d6871366df08be65ab98fe7535db2897746229f1e
- Size of remote file:
- 438 MB
- SHA256:
- f9e688dd3487efba8f90d300aab59e0e23358494dc57002a4f87790bf2a92cdb
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