Instructions to use Labib11/PMC_bge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Labib11/PMC_bge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Labib11/PMC_bge")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Labib11/PMC_bge") model = AutoModel.from_pretrained("Labib11/PMC_bge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Labib11/PMC_bge: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Labib11/PMC_bge/resolve/main/training_args.bin
- Command line
-
hf download hf://Labib11/PMC_bge/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Labib11/PMC_bge/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 168fe35236a48e8a87334adcb9ae41be1b8028dfc8d6e29f412cfc88c24803bb
- Size of remote file:
- 5.24 kB
- SHA256:
- 3da4c15b601d6f576dcd179cd8b78a831ce9b72d07fa3dd500671fb02c60c38b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.