Instructions to use dejanai/LinkBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dejanai/LinkBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dejanai/LinkBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dejanai/LinkBERT") model = AutoModelForMaskedLM.from_pretrained("dejanai/LinkBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dejanai/LinkBERT: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/dejanai/LinkBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dejanai/LinkBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dejanai/LinkBERT/resolve/main/pytorch_model.bin
1.33 GB
- Xet hash:
- 5aac75fdc6ea9b7cb3b02c5637529550407a263e49a1e1e2141061bc35b84ca0
- Size of remote file:
- 1.33 GB
- SHA256:
- da8ea5f8dd9d2c939f26deec0ea11d9f3709211f60e66d9e3853012e47761fb6
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