Whether you are investigating the hypothetical "Proto-World" language, building a low-resource machine translation system, or simply probing how transformers encode word order—this zip file is your starting line. Download, extract, and load today to join the intersection of linguistic typology and neural language modeling. Keywords: WALS Roberta Sets 1-36.zip, linguistic typology, RoBERTa fine-tuning, World Atlas of Language Structures, computational linguistics dataset, cross-linguistic NLP.
trainer = Trainer( model=model, args=training_args, train_dataset=train_encodings, # tokenized from WALS Roberta Sets eval_dataset=test_encodings, ) WALS Roberta Sets 1-36.zip
import numpy as np import json from transformers import RobertaTokenizer, RobertaForSequenceClassification tokenizer = RobertaTokenizer.from_pretrained("./tokenizers/roberta_wals_tokenizer.json") Load set 1 (Consonant inventories) consonant_data = np.load("./data/set_01_consonants/wals_code_vectors.npy") labels = np.load("./data/set_01_consonants/labels.npy") building a low-resource machine translation system
print(f"Loaded {consonant_data.shape[0]} language samples for Set 1") Here is a minimal example using Hugging Face's Trainer API: World Atlas of Language Structures