Instructions to use HPLT/hplt_bert_base_bg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HPLT/hplt_bert_base_bg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HPLT/hplt_bert_base_bg", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_bg", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from HPLT/hplt_bert_base_bg: direct link, hf CLI and curl.
- Browser
- Download file 525 MB
-
https://huggingface.co/HPLT/hplt_bert_base_bg/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HPLT/hplt_bert_base_bg/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HPLT/hplt_bert_base_bg/resolve/main/pytorch_model.bin
525 MB
- Xet hash:
- 072fd221479c39f4aaa4b9478a56d7d9c611ef7e25b009eaf5594ea66b8bcb5d
- Size of remote file:
- 525 MB
- SHA256:
- 6bd4a1862e79196f809e468f823dea2676a8cf932ad7afbbb2a0f84a0b29a500
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.