Token Classification
Transformers
Safetensors
English
eurobert
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
custom_code
Instructions to use OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M: direct link, hf CLI and curl.
- Browser
- Download file 424 MB
-
https://huggingface.co/OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M/resolve/main/model.safetensors
- Command line
-
hf download hf://OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/OpenMed/OpenMed-NER-PathologyDetect-EuroMed-212M/resolve/main/model.safetensors
424 MB
- Xet hash:
- a74a36b904e1304fcc302a2200a44728366ef146c977a18d48b1990b1eddf1b7
- Size of remote file:
- 424 MB
- SHA256:
- cb11e1eefff06362b74059260c84a2c23bf627a55697519cae7547626da48e00
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