Instructions to use prachuryyaIITG/MultiCoNER2_English_XLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prachuryyaIITG/MultiCoNER2_English_XLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="prachuryyaIITG/MultiCoNER2_English_XLM")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("prachuryyaIITG/MultiCoNER2_English_XLM") model = AutoModelForTokenClassification.from_pretrained("prachuryyaIITG/MultiCoNER2_English_XLM") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -77,13 +77,14 @@ If you use this model, please cite the following papers:
|
|
| 77 |
year={2023}
|
| 78 |
}
|
| 79 |
|
| 80 |
-
@misc{
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
|
|
|
|
| 87 |
}
|
| 88 |
|
| 89 |
@inproceedings{kaushik2026sampurner,
|
|
|
|
| 77 |
year={2023}
|
| 78 |
}
|
| 79 |
|
| 80 |
+
@misc{kaushik2026awedfineragentswebapplications,
|
| 81 |
+
title={AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers},
|
| 82 |
+
author={Prachuryya Kaushik and Ashish Anand},
|
| 83 |
+
year={2026},
|
| 84 |
+
eprint={2601.10161},
|
| 85 |
+
archivePrefix={arXiv},
|
| 86 |
+
primaryClass={cs.CL},
|
| 87 |
+
url={https://arxiv.org/abs/2601.10161},
|
| 88 |
}
|
| 89 |
|
| 90 |
@inproceedings{kaushik2026sampurner,
|