Instructions to use prachuryyaIITG/MultiCoNER2_German_XLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prachuryyaIITG/MultiCoNER2_German_XLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="prachuryyaIITG/MultiCoNER2_German_XLM")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("prachuryyaIITG/MultiCoNER2_German_XLM") model = AutoModelForTokenClassification.from_pretrained("prachuryyaIITG/MultiCoNER2_German_XLM") - Notebooks
- Google Colab
- Kaggle
Add library_name to metadata and improve model card documentation
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team.
This PR adds the library_name: transformers metadata tag to your model card. This will enable the "Use in Transformers" button and generate an automated code snippet on the model page, making it easier for users to load and use your model.
I have also kept your excellent documentation regarding the MultiCoNER2 dataset, model performance, and the sample usage for the AWED-FiNER agentic tool.
prachuryyaIITG changed pull request status to merged