Datasets:
Tasks:
Text Generation
Formats:
parquet
Sub-tasks:
language-modeling
Languages:
Danish
Size:
10M - 100M
ArXiv:
DOI:
License:
File size: 2,122 Bytes
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""""""
import logging
from functools import partial
from typing import Any
from datasets import Dataset
from transformers import AutoTokenizer
from dynaword.dataset_structure import COLUMN_ORDER, ColumnNames
logger = logging.getLogger(__name__)
# TODO: Add a step to compute the size categories and update the frontmatter
def _tokenize_function(
examples: dict[str, Any], tokenizer: AutoTokenizer
) -> dict[str, Any]:
token_count = [
len(tokens)
for tokens in tokenizer(examples[ColumnNames.text.value], padding=False)[ # type: ignore
"input_ids"
]
]
examples[ColumnNames.token_count.value] = token_count
return examples
def add_token_count(
ds: Dataset,
tokenizer_name: str = "AI-Sweden-Models/Llama-3-8B-instruct",
num_proc: int = 4,
) -> Dataset:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True)
tokenize = partial(_tokenize_function, tokenizer=tokenizer) # type: ignore
ds = ds.map(tokenize, batched=True, num_proc=num_proc)
return ds
def _filter_duplicates(example: dict[str, Any], seen_set: set) -> bool:
if example[ColumnNames.text.value] in seen_set:
return False
seen_set.add(example[ColumnNames.text.value])
return True
def remove_duplicate_text(ds: Dataset) -> Dataset:
logger.info("Removing duplicate texts")
seen_texts = set()
len_ds = len(ds)
ds = ds.filter(partial(_filter_duplicates, seen_set=seen_texts))
logger.info(f"Filtered {len_ds - len(ds)} duplicate examples")
return ds
def _filter_empty(example: dict[str, Any]) -> bool:
return len(example[ColumnNames.text.value].strip()) > 0
def remove_empty_texts(ds: Dataset, num_proc: int = 4) -> Dataset:
logger.info("Removing empty texts")
len_ds = len(ds)
ds = ds.filter(_filter_empty, num_proc=num_proc)
logger.info(f"Filtered {len_ds - len(ds)} empty examples")
return ds
def ensure_column_order(ds: Dataset) -> Dataset:
logger.info("Ensuring columns are in the correct order and are present")
ds = ds.select_columns(COLUMN_ORDER)
return ds
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