Instructions to use prachuryyaIITG/CLASSER_Assamese_MuRIL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prachuryyaIITG/CLASSER_Assamese_MuRIL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="prachuryyaIITG/CLASSER_Assamese_MuRIL")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("prachuryyaIITG/CLASSER_Assamese_MuRIL") model = AutoModelForTokenClassification.from_pretrained("prachuryyaIITG/CLASSER_Assamese_MuRIL") - Notebooks
- Google Colab
- Kaggle
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README.md
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@inproceedings{kaushik2026sampurner,
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@inproceedings{fetahu2023multiconer,
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title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition},
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author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Oleg and Malmasi, Shervin},
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}
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@inproceedings{kaushik2026sampurner,
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title={SampurNER: Fine-Grained Named Entity Recognition Dataset for 22 Indian Languages},
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volume={40},
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url={https://ojs.aaai.org/index.php/AAAI/article/view/40405},
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DOI={10.1609/aaai.v40i37.40405},
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number={37},
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journal={Proceedings of the AAAI Conference on Artificial Intelligence},
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author={Kaushik, Prachuryya and Anand, Ashish},
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year={2026},
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month={Mar.},
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pages={31410-31418}
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}
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@inproceedings{fetahu2023multiconer,
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title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition},
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author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Oleg and Malmasi, Shervin},
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