airudit/UD_v2_17_POS_LEMMA

Dataset Information

This dataset is designed for the Part-of-Speech (POS) tagging and Lemmatization tasks and is used to train the Airudit multitask model.

Dataset Description

The dataset combines Universal Dependencies v2.17 French corpora available at
commul/universal_dependencies.

Included corpora:

• commul/universal_dependencies/fr_gsd
• commul/universal_dependencies/fr_sequoia
• commul/universal_dependencies/fr_partut
• commul/universal_dependencies/fr_parisstories
• commul/universal_dependencies/fr_rhapsodie
• commul/universal_dependencies/fr_poitevindivital
• commul/universal_dependencies/fr_pud

Dataset Structure

The dataset contains the following splits:

DatasetDict({
    train: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 20159
    })
    dev: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 3768
    })
    test_fr_gsd: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 416
    })
    test_fr_sequoia: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 456
    })
    test_fr_partut: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 110
    })
    test_fr_parisstories: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 697
    })
    test_fr_rhapsodie: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 840
    })
    test_fr_pud: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 1000
    })
    test_fr_poitevindivital: Dataset({
        features: ['sent_id', 'tokens', 'upos', 'lemmas'],
        num_rows: 239
    })
})

Labels

Labels are encoded in the dataset and can be retrieved as following :

from datasets import load_dataset
ds = load_dataset('airudit/UD_v2_17_POS')
ds["train"].features["upos"].feature.names
# ['NOUN', 'PUNCT', 'ADP', 'NUM', 'SYM', 'SCONJ', 'ADJ', 'PART', 'DET', 'CCONJ', 'PROPN', 'PRON', 'X', '_', 'ADV', 'INTJ', 'VERB', 'AUX']

🔍 Quick usage example:

from datasets import load_dataset
ds = load_dataset('airudit/UD_v2_17_POS')
ds["train"][0]
# {'sent_id': [...], 'tokens': [...], 'upos': [...], 'lemmas': [...]}

📦 Dataset Pre-Processing :

• Train and dev splits from all corpora are merged into unified train and dev splits.
• Test splits are preserved and renamed according to their source corpus.
• Only the relevant columns are kept: ["sent_id", "tokens", "upos", "lemmas"].
• In the Partut dataset, particles such as "ne" and "n'" are annotated with PART. To ensure consistency with other UD corpora, these labels are converted to ADV.
• The dataset was generated using the following script: multitask-nlp/src/multitask_nlp/datasets/pos_dataset_preparation.py