The World Atlas of Language Structures (WALS) is a monumental resource. Traditionally published as a book and later as an online database, WALS contains data on over 2,600 languages. It answers questions like: “Does this language have gendered pronouns?” or “What is the basic word order (SOV, SVO, etc.)?”
However, the raw WALS data is often distributed as CSV files or JSON with inconsistent encoding. This makes it difficult to feed directly into a transformer model like RoBERTa. That is why a pre-processed version—specifically the "sets" version—is so valuable.
Each line in the WALS sets should contain a language ID and a feature vector. Example: wals roberta sets 136zip best
"language": "eng", "text": "English word order subject verb object", "label": 42
Tokenize the text:
inputs = tokenizer("English word order subject verb object", return_tensors="pt", truncation=True, padding=True)
Finally, "best" is the most dangerous word. Best according to what metric? Accuracy? F1 score? Compression ratio? Linguistic plausibility? In supervised learning, "best" is defined by a loss function. But for the hybrid object "wals roberta sets 136zip," no ground truth exists. The World Atlas of Language Structures (WALS) is
Perhaps "best" refers to the optimal trade-off between three competing pressures:
This is a triple-objective optimization problem with no unique solution. What remains is the human judgment call—the "best" that emerges from a conference reviewer's whim, a benchmark leaderboard, or a grad student's late-night intuition. Tokenize the text: inputs = tokenizer("English word order
This dataset aligns language codes (ISO 639-3) with standardized language names. Many WALS dumps use outdated Glottocodes; the "best" version uses modern identifiers.
