Instructions to use SmartPy/bert-hs-idpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmartPy/bert-hs-idpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SmartPy/bert-hs-idpt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SmartPy/bert-hs-idpt") model = AutoModelForMaskedLM.from_pretrained("SmartPy/bert-hs-idpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SmartPy/bert-hs-idpt: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/SmartPy/bert-hs-idpt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SmartPy/bert-hs-idpt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SmartPy/bert-hs-idpt/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- fa3ea5415e0479752ec806a27550ef32bcddf5a06395d30a09020840974276d9
- Size of remote file:
- 438 MB
- SHA256:
- d29d50a25c6c0b28964ed3c12698de5ddf2d460c367e60f47052f071b2930395
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