Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
gene-recognition
genetics
genomics
molecular-biology
cell-line-name
Instructions to use OpenMed/OpenMed-NER-GenomicDetect-ElectraMed-560M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-GenomicDetect-ElectraMed-560M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-ElectraMed-560M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-ElectraMed-560M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-ElectraMed-560M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-GenomicDetect-ElectraMed-560M
0fd5b0a verified - Xet hash:
- 7d0c1dd37cf28ad5012df6dff1a226af355ec69ac96b8d08440993cb2c7b1159
- Size of remote file:
- 1.12 GB
- SHA256:
- aa1f30d686f8069249f3060c607e7e02b85cbd145ca03cdac459c9c557ef8e48
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