Datasets:
Tasks:
Question Answering
Languages:
Danish
Size Categories:
n<1K
Annotations Creators:
found
Source Datasets:
DDSC/partial-danish-gigaword-no-twitter
| from datasets import Dataset, load_dataset | |
| ds: Dataset = load_dataset("DDSC/partial-danish-gigaword-no-twitter") # type: ignore | |
| ds = ds["train"] | |
| # filter to only include spontaneous speech | |
| ds = ds.filter(lambda x: x["source"] == "spont", num_proc=6) | |
| texts = ds["text"] | |
| def remove_taler(text): | |
| if text.startswith("Taler"): | |
| text = text.split(":")[1:] | |
| text = ":".join(text) | |
| return text.strip() | |
| qa_pairs = [] | |
| for text in texts: | |
| lines = text.split("\n") | |
| lines = [remove_taler(line) for line in lines] | |
| questions = [ | |
| (i, text) | |
| for i, text in enumerate(lines) | |
| if len(text.split(" ")) > 7 and text.endswith("?") | |
| ] | |
| qa_pairs_ = [{"question": lines[i], "answer": lines[i + 1]} for i, _ in questions] | |
| qa_pairs += qa_pairs_ | |
| # filter qa pairs | |
| def get_length_of_pair(qa: dict): | |
| return len(qa["question"].split(" ")) + len(qa["answer"].split(" ")) | |
| def get_min_length_of_pair(qa: dict): | |
| return min(len(qa["question"].split(" ")), len(qa["answer"].split(" "))) | |
| qa_pairs = [ | |
| qa | |
| for qa in qa_pairs | |
| if get_length_of_pair(qa) < 20 and get_min_length_of_pair(qa) > 4 | |
| ] | |
| # create dataset | |
| qa_ds = Dataset.from_list(qa_pairs) | |
| # add readme | |
| qa_ds.info.description = """# Spontanous speech QA | |
| This dataset contains QA pairs from the spontaneous speech subsection of the Danish Gigaword. | |
| The dataset is created from the [DDSC dataset](DDSC/partial-danish-gigaword-no-twitter) and | |
| filtered to only include QA pairs where the question is less than 20 tokens and the answer is | |
| at least 4 tokens long. | |
| To find out more about the creation see the accompanying script. | |
| """ | |
| qa_ds.info.license = ds[0]["LICENSE"] | |
| qa_ds.info.dataset_name = "Spontanous Speech QA" | |
| # split dataset | |
| qa_ds = qa_ds.train_test_split(test_size=0.2) | |
| # upload dataset | |
| qa_ds.push_to_hub("spontanous-speech-qa") | |