Token Classification
Transformers
Safetensors
English
distilbert
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-TinyMed-66M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 134 Bytes
050bab5 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:c264049addb43434ccf4170b8c78deaae65124214cbeaacb8f534dc360109367
size 132755014
|