Instructions to use prachuryyaIITG/MultiCoNER2_Farsi_XLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prachuryyaIITG/MultiCoNER2_Farsi_XLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="prachuryyaIITG/MultiCoNER2_Farsi_XLM")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("prachuryyaIITG/MultiCoNER2_Farsi_XLM") model = AutoModelForTokenClassification.from_pretrained("prachuryyaIITG/MultiCoNER2_Farsi_XLM") - Notebooks
- Google Colab
- Kaggle
Add library_name metadata and link to paper
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team.
I'm opening this PR to improve the model card:
- Added
library_name: transformersto the metadata. This enables automated code snippets and the "Use in Transformers" button on the Hub. - Added a direct link to the AWED-FiNER paper in the description for better discoverability and context.
- Maintained the existing structure and citations.
Please review and merge if this looks good!
prachuryyaIITG changed pull request status to merged