Wals Roberta Sets 1-36.zip

df = pd.read_csv('set1.csv') X = df.drop(['language_id', 'feature_value'], axis=1) # RoBERTa embeddings y = df['feature_value']

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consonant_data = np.load("./data/set_01_consonants/wals_code_vectors.npy") labels = np.load("./data/set_01_consonants/labels.npy")

The creation of this zip file represents a bridge between : df = pd

Where feature_value is a numeric or categorical code (e.g., 1=small inventory, 2=medium, 3=large).

The dataset file is a specialized archive used in computational linguistics, natural language processing (NLP), and artificial intelligence research. It bridges the gap between structural linguistics and modern deep learning models, specifically Facebook's RoBERTa architecture. consonant_data = np

Each set would be formatted to be compatible with RoBERTa's input requirements for a specific fine-tuning task, such as classification, regression, or token tagging.