Instructions to use GerMedBERT/medbert-512-no-duplicates with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GerMedBERT/medbert-512-no-duplicates with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GerMedBERT/medbert-512-no-duplicates")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GerMedBERT/medbert-512-no-duplicates") model = AutoModelForMaskedLM.from_pretrained("GerMedBERT/medbert-512-no-duplicates", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from GerMedBERT/medbert-512-no-duplicates: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/GerMedBERT/medbert-512-no-duplicates/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GerMedBERT/medbert-512-no-duplicates/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GerMedBERT/medbert-512-no-duplicates/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- 83deb1902a28cae7c91a1588c3b7d0779fe57ee11e2cb1dab4ae44884bd60c11
- Size of remote file:
- 437 MB
- SHA256:
- be774f4c18c09b1f245be0ee04bb85a8745d2afc2b53309341fd37a735af7d7c
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