Instructions to use StevenZHB/Bio-Inject-Bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StevenZHB/Bio-Inject-Bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="StevenZHB/Bio-Inject-Bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("StevenZHB/Bio-Inject-Bert") model = AutoModelForMaskedLM.from_pretrained("StevenZHB/Bio-Inject-Bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from StevenZHB/Bio-Inject-Bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/StevenZHB/Bio-Inject-Bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://StevenZHB/Bio-Inject-Bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/StevenZHB/Bio-Inject-Bert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 39d0ec82f1c50b2874dd2c2d1fe2c4fe51a40f4657e1a381c93907fed1249fd3
- Size of remote file:
- 438 MB
- SHA256:
- 23d67f885bd6d14ada24620dcbb5411c4e1ab740d12c419ec0d3c8c02de9a6bd
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