Instructions to use hagara/biobert-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hagara/biobert-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hagara/biobert-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hagara/biobert-2") model = AutoModelForSequenceClassification.from_pretrained("hagara/biobert-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hagara/biobert-2: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/hagara/biobert-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hagara/biobert-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hagara/biobert-2/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- c83912c9805e2f1386ae225df1bb2e4d530a44b540a0d0ae6b6fdc0a5bf09bc5
- Size of remote file:
- 433 MB
- SHA256:
- 2f827a3025ee080ff5caf03704276d99482c688c609a4a0ebcea0f0e808de5bb
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