Instructions to use prachuryyaIITG/CLASSER_Sanskrit_MuRIL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prachuryyaIITG/CLASSER_Sanskrit_MuRIL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="prachuryyaIITG/CLASSER_Sanskrit_MuRIL")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("prachuryyaIITG/CLASSER_Sanskrit_MuRIL") model = AutoModelForTokenClassification.from_pretrained("prachuryyaIITG/CLASSER_Sanskrit_MuRIL") - Notebooks
- Google Colab
- Kaggle
Add library_name: transformers and link to AWED-FiNER paper
#1
by nielsr HF Staff - opened
Hi! I'm Niels, part of the community science team at Hugging Face.
This PR improves the model card by:
- Adding the
library_name: transformersmetadata tag to enable the automated "Use in Transformers" button and code snippets on the model page. - Adding a link to the associated paper: AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers.
- Ensuring the GitHub repository is properly referenced.
The existing sample usage and performance metrics have been preserved.
prachuryyaIITG changed pull request status to merged