NagaNLP Project
Collection
Resources for the NagaNLP project: Low-resource NLP for Nagamese (Naga Pidgin), including conversational corpora, NER, and POS tagging resources. • 10 items • Updated • 3
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: The document is empty.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Trailing data
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: The document is empty.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
NagaNLP Conversational Corpus
Dataset Summary
This is the official conversational dataset for the NagaNLP project. It contains 10,021 instruction-following pairs (User / Assistant) in Nagamese (Naga Pidgin), intended for instruction-tuning or evaluating conversational models in a low-resource creole language.
Supported Tasks
- Text Generation / Instruction Following: given a user prompt in Nagamese, generate an appropriate assistant response in Nagamese.
Languages
Nagamese / Naga Pidgin (ISO 639-3: nag), a creole widely used as a lingua
franca across Nagaland and neighboring Northeast Indian states.
Dataset Structure
Data Instance
{
"prompt": "User: Itu kath laga 'tobacco pipe' bhal lagishe.",
"completion": "Assistant: Hoi, purana manu khan e..."
}
Data Fields
prompt: string — the user's instruction, prefixed withUser:.completion: string — the assistant's response, prefixed withAssistant:.
Data Splits
| Split | Pairs |
|---|---|
| train | 10,021 |
Note: This is the full, unsplit corpus. It is mapped to the train split
purely for HuggingFace Dataset Viewer compatibility — there is no dedicated
validation/test split.
Dataset Creation
Source Data
Instruction/response pairs were constructed as part of the NagaNLP project's human-in-the-loop synthetic data pipeline, covering everyday conversational scenarios (greetings, requests, small talk, general Q&A) in Nagamese.
Annotations
Pairs were generated and verified through a human-in-the-loop process; see the accompanying NagaNLP paper for full methodology.
Considerations for Using the Data
Limitations
- As with other NagaNLP resources, a portion of this corpus originates from a synthetic, human-in-the-loop generation pipeline rather than organically collected conversations — this should be disclosed when the dataset is used to train or evaluate models intended to reflect naturalistic Nagamese usage.
- Coverage of conversational domains (topics, registers, dialectal variation within Nagamese) is not exhaustive.
Licensing
Licensed under CC BY-NC 4.0 (attribution required, non-commercial use only). For commercial licensing inquiries, please contact the author.
Citation
@misc{maiti2025naganlp,
title={NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data},
author={Agniva Maiti and Manya Pandey and Murari Mandal},
year={2025},
eprint={2512.12537},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.12537}
}
Contact
Agniva Maiti — KIIT University — HuggingFace: agnivamaiti
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