Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
protein-interactions
molecular-biology
biochemistry
systems-biology
protein
protein_complex
protein_enum
protein_familiy_or_group
protein_variant
Instructions to use OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-33M/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.