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: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows_from_streaming.py", line 162, in compute_first_rows_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2206, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1230, in _head
return _examples_to_batch(list(self.take(n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1379, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1039, in __iter__
yield from islice(self.ex_iterable, self.n)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 281, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 89, in _generate_tables
batch = f.read(self.config.chunksize)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 333, in read_with_retries
out = read(*args, **kwargs)
File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byteNeed help to make the dataset viewer work? Open a discussion for direct support.
Tibetan News Classification Corpus
This is the open-sourced training corpus of our Tibetan BERT Model.
Citation
Please cite our paper if you use this training corpus or the model:
@inproceedings{10.1145/3548608.3559255,
author = {Zhang, Jiangyan and Kazhuo, Deji and Gadeng, Luosang and Trashi, Nyima and Qun, Nuo},
title = {Research and Application of Tibetan Pre-Training Language Model Based on BERT},
year = {2022},
isbn = {9781450397179},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3548608.3559255},
doi = {10.1145/3548608.3559255},
abstract = {In recent years, pre-training language models have been widely used in the field of natural language processing, but the research on Tibetan pre-training language models is still in the exploratory stage. To promote the further development of Tibetan natural language processing and effectively solve the problem of the scarcity of Tibetan annotation data sets, the article studies the Tibetan pre-training language model based on BERT. First, given the characteristics of the Tibetan language, we constructed a data set for the BERT pre-training language model and downstream text classification tasks. Secondly, construct a small-scale Tibetan BERT pre-training language model to train it. Finally, the performance of the model was verified through the downstream task of Tibetan text classification, and an accuracy rate of 86\% was achieved on the task of text classification. Experiments show that the model we built has a significant effect on the task of Tibetan text classification.},
booktitle = {Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics},
pages = {519–524},
numpages = {6},
location = {Nanjing, China},
series = {ICCIR '22}
}
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