Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
species-recognition
taxonomy
organism-identification
biodiversity
species
Instructions to use OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 196 Bytes
f6cd5af | 1 2 3 4 5 6 7 | {
"eval_accuracy": 0.9600019183732195,
"eval_f1": 0.8120842572062084,
"eval_loss": 0.3793276250362396,
"eval_precision": 0.7771883289124668,
"eval_recall": 0.8502611723737666
} |