Token Classification
Transformers
Safetensors
English
xlm-roberta
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-BigMed-278M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-NER-ProteinDetect-BigMed-278M/resolve/main/test_results.json
195 Bytes
| { | |
| "eval_accuracy": 0.9738167614655111, | |
| "eval_f1": 0.9466405769061649, | |
| "eval_loss": 0.5913233160972595, | |
| "eval_precision": 0.9433842378563134, | |
| "eval_recall": 0.949919474044168 | |
| } |