Instructions to use MDDDDR/dmis_lab_biobert_v1.1_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MDDDDR/dmis_lab_biobert_v1.1_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MDDDDR/dmis_lab_biobert_v1.1_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MDDDDR/dmis_lab_biobert_v1.1_NER") model = AutoModelForTokenClassification.from_pretrained("MDDDDR/dmis_lab_biobert_v1.1_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c04ae102a1511119a71cb5b2a0214a9084958d1d88e39a4424f6e76e7f723fe0
- Size of remote file:
- 431 MB
- SHA256:
- 6ec2d9492bfd43c589e59793d6b61f0a2ee5c64f419db76fc96f22f99a491f69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.