Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
Instructions to use OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-PathologyDetect-ElectraMed-560M
deb441c verified Download model.safetensors from OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M/resolve/main/model.safetensors
- Command line
-
hf download hf://OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/OpenMed/OpenMed-NER-PathologyDetect-ElectraMed-560M/resolve/main/model.safetensors
1.12 GB
- Xet hash:
- e60951b2fd5fbc85cdef9c5e1f979199cc11afa62c94fbc5f04186836b4fb06c
- Size of remote file:
- 1.12 GB
- SHA256:
- 682c4eb441a3d57ee6b34ea95e375313640a11d424f13482bf8c5fb086cf2d2b
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