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Datasets:
tasal9
/
Pashto-Corpus-Training-Ready

Languages:
Pashto
Tags:
pashto
corpus
training
fine-tuning
language-modeling
low-resource-languages
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
_data_files: list<item: struct<filename: string>>
  child 0, item: struct<filename: string>
      child 0, filename: string
_fingerprint: string
_format_columns: null
_format_kwargs: struct<>
_format_type: null
_output_all_columns: bool
_split: null
text: string
length: int64
source: string
to
{'text': Value('string'), 'source': Value('string'), 'length': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 265, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 120, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              _data_files: list<item: struct<filename: string>>
                child 0, item: struct<filename: string>
                    child 0, filename: string
              _fingerprint: string
              _format_columns: null
              _format_kwargs: struct<>
              _format_type: null
              _output_all_columns: bool
              _split: null
              text: string
              length: int64
              source: string
              to
              {'text': Value('string'), 'source': Value('string'), 'length': Value('int64')}
              because column names don't match

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Pashto Corpus - Training Ready

Clean, preprocessed Pashto text dataset ready for fine-tuning language models.

Dataset Description

This dataset contains preprocessed and chunked Pashto text from educational materials, literary works, and academic texts. It's formatted and ready for training/fine-tuning language models like GPT, Llama, mT5, etc.

Splits

Split Samples Purpose
Train ~25,000 Model training
Validation ~2,800 Hyperparameter tuning
Test ~2,800 Final evaluation

Data Fields

  • text: Cleaned Pashto text chunk
  • source: Category (literary_works, grammar_books, textbooks_kpk, academic_research)
  • length: Character count

Preprocessing

  • Removed PDF artifacts and page numbers
  • Normalized Arabic/Pashto characters
  • Split long documents into chunks (max 2000 chars)
  • Quality filtering (minimum length, script ratio)
  • Train/Val/Test split (80/10/10)

Usage

Load with 🤗 Datasets 

from datasets import load_dataset

dataset = load_dataset("tasal9/Pashto-Corpus-Training-Ready")
print(dataset["train"][0]["text"][:200])

Tokenization Example 

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("google/mt5-base")

def tokenize(example):
    return tokenizer(
        example["text"],
        padding="max_length",
        truncation=True,
        max_length=512
    )

tokenized = dataset.map(tokenize, batched=True)

Training Example 

from transformers import AutoModelForCausalLM, TrainingArguments, Trainer

model = AutoModelForCausalLM.from_pretrained("gpt2")

training_args = TrainingArguments(
    output_dir="./pashto-model",
    per_device_train_batch_size=16,
    learning_rate=5e-5,
    num_train_epochs=3,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized["train"],
    eval_dataset=tokenized["validation"],
)

trainer.train()

License

CC-BY 4.0 - Free for commercial and research use with attribution.

Citation 

@dataset{pashto-corpus-training-2026,
  author = {Tasal},
  title = {Pashto Corpus - Training Ready},
  year = {2026},
  url = {https://huggingface.co/datasets/tasal9/Pashto-Corpus-Training-Ready},
  version = {1.0.0}
}

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