Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
biomedical-nlp
gene-recognition
genetics
genomics
molecular-biology
cell-line-name
Instructions to use OpenMed/OpenMed-NER-GenomicDetect-TinyMed-65M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-GenomicDetect-TinyMed-65M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-TinyMed-65M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-TinyMed-65M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-TinyMed-65M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54efb3ca2aa35b02b8e59f6e35253392caeab5a29cf0f07787ac43f0ad86c7ec
- Size of remote file:
- 130 MB
- SHA256:
- f483daa4c95b91396608210227c9752195fede9ff03a63d43cedd457a3908cf9
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