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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'iriisnepal_u_nepberta_512' of the dataset.
Error code: FeaturesError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0xd2 in position 7: invalid continuation byte
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, 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 1918, 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 2093, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 73, in _generate_tables
batch = f.read(self.config.chunksize)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 826, 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 0xd2 in position 7: invalid continuation byteNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Nepali LLM Datasets
This repository contains two configurations of Nepali LLM datasets:
Configurations
1. Scrapy Engine
- Description: Contains data collected using a web scraping engine.
- Files: [List any specific files or formats]
2. Nepberta
- Description: This dataset is derived from the Nepberta project and contains cleaned data specifically related to the project. The dataset contains **cleaned text chunks of size ~50 mb ** of all articles into a single giant string, with each article ending in <|endoftext|>. This long string is then segmented into chunks, each approximately 500 MB in size.
- Files: contains 23 files each ~500Mb (chunk_1.txt, chunk_2.txt, ... chunk_23.txt)
- split:train
- files: chunk_1.txt to chunk_18.txt
- split:test
- files: chunk_19.txt to chunk_23.txt
Usage
To load the datasets:
# it loads entire dataset first
from datasets import load_dataset
# Load nepberta configuration
nepberta_dataset = load_dataset("Aananda-giri/nepali_llm_datasets", name="nepberta", split='train') # use `streaming=True` to avoid downloading all the dataset
# length of chunks
len(nepberta_train['text']) # 18 : number of chunks
len(nepberta_train['text'][0]) # length of large text equivalent to 500 MB text
# use streaming=True to avoid downloading entire dataset
nepberta_train = load_dataset("Aananda-giri/nepali_llm_datasets", name="nepberta", streaming=True)['train']
# using next
next_text_chunk = next(iter(nepberta_train))
print(len(next_text_chunk['text']))
# using for loop
for large_chunk in nepberta_train:
print(len(large_chunk['text']))
break
# code to process large_chunk['text']
# Load scrapy engine data
scrapy_train = load_dataset("Aananda-giri/nepali_llm_datasets", name="scrapy_engine" split="train")
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