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-MultiMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M: direct link, hf CLI and curl.
- Browser
- Download file 196 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-NER-OrganismDetect-MultiMed-335M/resolve/main/test_results.json
196 Bytes
| { | |
| "eval_accuracy": 0.9669560212939428, | |
| "eval_f1": 0.8440999138673557, | |
| "eval_loss": 0.36793819069862366, | |
| "eval_precision": 0.8352272727272727, | |
| "eval_recall": 0.853163087637841 | |
| } |