Instructions to use VSSA-SDSA/LT-NER-modernBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VSSA-SDSA/LT-NER-modernBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="VSSA-SDSA/LT-NER-modernBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("VSSA-SDSA/LT-NER-modernBERT") model = AutoModelForTokenClassification.from_pretrained("VSSA-SDSA/LT-NER-modernBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from VSSA-SDSA/LT-NER-modernBERT: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/VSSA-SDSA/LT-NER-modernBERT/resolve/main/training_args.bin
- Command line
-
hf download hf://VSSA-SDSA/LT-NER-modernBERT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/VSSA-SDSA/LT-NER-modernBERT/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- faa88652a0e3d53c5d8df26cf65c8596864565c3228a5aa461692aebedae2bf3
- Size of remote file:
- 5.3 kB
- SHA256:
- 678971d68fd35a1fda9194bcf5d73df4627cf4b7e287cf7d93002471e17ccefc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.