Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
cl
Instructions to use OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-BloodCancerDetect-ElectraMed-335M
912c0ff verified Download openmed_vs_sota_grouped_bars.png from OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M: direct link, hf CLI and curl.
- Browser
- Download file 497 kB
-
https://huggingface.co/OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M/resolve/main/openmed_vs_sota_grouped_bars.png
- Command line
-
hf download hf://OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M/openmed_vs_sota_grouped_bars.png
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curl -L -o openmed_vs_sota_grouped_bars.png https://huggingface.co/OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-335M/resolve/main/openmed_vs_sota_grouped_bars.png
497 kB

- Xet hash:
- 26884d88e5c82ce8431230e47e3c804c40185c9a1a8edef46b74b425685913ad
- Size of remote file:
- 497 kB
- SHA256:
- 626b37d9b20c44e26c92a8b5bf774107393ae0ad0b482d8e7cb3dc31d960f611
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