Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M: direct link, hf CLI and curl.
- Browser
- Download file 196 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-NER-PharmaDetect-ElectraMed-560M/resolve/main/test_results.json
196 Bytes
| { | |
| "eval_accuracy": 0.9887782839835508, | |
| "eval_f1": 0.9561786218401129, | |
| "eval_loss": 0.3259938061237335, | |
| "eval_precision": 0.9482692797149402, | |
| "eval_recall": 0.9642210144927537 | |
| } |