Datasets:

prachuryyaIITG
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FiNERVINER

text stringlengths 0 65
क्रिकेट O
– O
बेर'निका B-VisualWork
से I-VisualWork
दिथागिरि O
एलिजाबेथा B-Artist
भस्टान I-Artist
1973. O

सिस्मिक B-MedicalProcedure
ट'म'ग्राफी I-MedicalProcedure
आ O
अरगें O
फोथारनि O
सिङाव O
मोनसे O
सिगांनि O
मैग्मा O
हाबफैनायनि O
नेरसोनफोरखौ O
सिनायथि O
होदों O

सार O
आइजेक B-Politician
निउटनआ I-Politician
गिबिसिन O
खामानिखौ O
दिहुनदोंमोन O
टेलीस्कोप B-OtherPROD
। O

ट्रेनाव O
बारग' O
हादोरगिरि O
जन B-Politician
क्विन्सी I-Politician
एडाम्स I-Politician
बो O
दंमोन O

सनी B-Artist
बर्गेस I-Artist
रकाबिली O
महरगिरि O
आरो O
रकाबिली B-OtherLOC
हल I-OtherLOC
अफ I-OtherLOC
फेमनि I-OtherLOC
सासे I-OtherLOC
सोद्रोमा I-OtherLOC

बियो O
निब्ल'नि B-Facility
बागाननि I-Facility
बिगोमामोन I-Facility
। O

बेबादिनो O
गुबुंले O
जानायनि O
दाबिफोरा O
उनाव O
एल O
अरियेल O
आरो O
लुक'जेड B-Drink
नि O
बेरेखायै O
खालामनाय O
जादोंमोन O

ट्रेनफोरा O
भिभिया O
हेलनबर्गेन B-Station
आरो O
रिंकेबी B-Station
खारगासिनो O
दंमोन O

बिथांजोआ O
500 O
मीटर O
आरो O
1000 O
मीटर O
बादायलायनायाव O
2010 O
माइथायनि O
गोजां O
बोथोरनि O
अलिम्पिक O
>27 O
चाइना O
आर्टनि O
थाखाय O
बादायलायदोंमोन O

बिथांजोआ O

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FiNERVINER: Fine-grained Named Entity Recognition for Vulnerable languages of India's North Eastern Region

FiNERVINER is a high-quality fine-grained named entity recognition dataset created through annotation projection method.

The vulnerable languages are: Bodo (brx), Mizo (lus), and Manipuri (mni).

FiNERVINER Dataset Statistics

Language	Train set			Development set			Test set
Language	Sentences	Entities	Tokens	Sentences	Entities	Tokens	Sentences	Entities	Tokens	IAA (κ)
Bodo (brx)	212,835	302,713	2,958,455	23,649	33,808	329,145	1,000	1,423	14,082	0.875
Mizo (lus)	177,224	252,767	2,515,386	19,692	28,143	279,681	1,000	1,384	14,330	0.811
Manipuri (mni)	239,813	302,713	4,422,373	26,646	38,330	484,212	1,000	1,426	18,765	0.821

Note: IAA (Inter-Annotator Agreement) scores are represented using Cohen's κ.

Contributors

Prachuryya Kaushik
Prof. Ashish Anand

FiNERVINER is a part of the AWED-FiNER ecosystem: Paper | GitHub | Interactive Demo

Sample Usage

You can use the AWED-FiNER agentic tool to interact with expert models trained using this framework. Below is an example using the smolagents library:

from smolagents import CodeAgent, HfApiModel
from tool import AWEDFiNERTool

# Initialize the expert tool
ner_tool = AWEDFiNERTool()

# Initialize the agent (using a model of your choice)
agent = CodeAgent(tools=[ner_tool], model=HfApiModel())

# The agent will automatically use AWED-FiNER for specialized NER
# Case: Processing a vulnerable language (Bodo)
agent.run("Recognize the named entities in this Bodo sentence: 'बिथाङा दिल्लियाव थाङो।'")

Citation

If you use this dataset, please cite the following papers:

@inproceedings{kaushik2026finerviner,
  title={FiNERVINER: Fine-grained Named Entity Recognition for Vulnerable languages of India's North Eastern Region},
  author={Kaushik, Prachuryya and Anand, Ashish},
  booktitle={Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  volume={15},
  year={2026}
}

@inproceedings{kaushik2026sampurner,
  title={SampurNER: Fine-grained Named Entity Recognition dataset for 22 Indian Languages},
  author={Kaushik, Prachuryya and Anand, Ashish},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  year={2026}
}

@misc{kaushik2026awedfineragentswebapplications,
      title={AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers}, 
      author={Prachuryya Kaushik and Ashish Anand},
      year={2026},
      eprint={2601.10161},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.10161}, 
}