Token Classification
Transformers
Safetensors
English
modernbert
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-ModernMed-149M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M: direct link, hf CLI and curl.
- Browser
- Download file 194 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-NER-ProteinDetect-ModernMed-149M/resolve/main/test_results.json
194 Bytes
| { | |
| "eval_accuracy": 0.965604209837496, | |
| "eval_f1": 0.8715335032229051, | |
| "eval_loss": 0.6061640977859497, | |
| "eval_precision": 0.8648843930635838, | |
| "eval_recall": 0.878285640295222 | |
| } |