WALS_Roberta_Sets_1-36/ ├── set1_consonants/ │ ├── train.jsonl │ ├── dev.jsonl │ ├── test.jsonl │ └── wals_labels.txt ├── set2_vowels/ │ └── ... ├── ... ├── set36_...(final feature) ├── roberta_tokenizer/ │ ├── vocab.json │ └── merges.txt └── metadata.yaml
tokenizer = RobertaTokenizer.from_pretrained("./tokenizers/roberta_wals_tokenizer.json")
Aliyah wrote a short README for her lab: WALS Roberta Sets 1-36.zip
Assume set1.csv contains:
WALS—the World Atlas of Language Structures —was a treasure trove. It contained data on over 2,000 languages, mapping everything from word order (Subject-Verb-Object like English, or SOV like Japanese) to phoneme inventories. But raw WALS data was cumbersome. Someone named Roberta had done the unglamorous but heroic work of cleaning, splitting, and encoding that data into 36 balanced sets, perfectly formatted for training a RoBERTa-style language model. It contained data on over 2,000 languages, mapping
: Changes the masking pattern applied to training data sequences across epochs.
For RoBERTa fine-tuning:
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In the , navigate to the folder where you saved the sets. : Changes the masking pattern applied to training
language_id,wals_code,feature_value,family,area abc123,1A,2,Indo-European,Eurasia ...