Instructions to use HPLT/hplt_bert_base_so with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HPLT/hplt_bert_base_so with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HPLT/hplt_bert_base_so", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_so", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HPLT/hplt_bert_base_so: direct link, hf CLI and curl.
- Browser
- Download file 475 MB
-
https://huggingface.co/HPLT/hplt_bert_base_so/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HPLT/hplt_bert_base_so/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HPLT/hplt_bert_base_so/resolve/main/pytorch_model.bin
475 MB
- Xet hash:
- 70ff5b9ca21e1bb59722ac1806faa5b9c57e9bf922038379664989d422bbe65b
- Size of remote file:
- 475 MB
- SHA256:
- 58a33dc8ac9dfca778d987a6ac84c5f023fc047f9edff79e63d747f63b87685a
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