Wals Roberta Sets 1-36.zip //top\\

If you aim to create a similar resource:

print(f"Loaded consonant_data.shape[0] language samples for Set 1")

import json from transformers import RobertaTokenizer, RobertaForSequenceClassification WALS Roberta Sets 1-36.zip

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model = RobertaForSequenceClassification.from_pretrained("roberta-base", num_labels=36) # 36 feature sets If you aim to create a similar resource:

import torch from transformers import RobertaTokenizer, RobertaForSequenceClassification # Define the target directory from the unzipped archive (e.g., Set 1) model_path = "./wals_roberta_models/set_1" # Load the specialized tokenizer and weights tokenizer = RobertaTokenizer.from_pretrained(model_path) model = RobertaForSequenceClassification.from_pretrained(model_path) print("WALS RoBERTa Set 1 loaded successfully.") Use code with caution. Step 3: Running Inference on Typological Data

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Each text file will contain the examples for that subset.

Researchers use WALS data to see if RoBERTa "knows" linguistics. For example, if we feed the model sentences from a language it hasn't seen much of, can its internal vectors predict that language's word order (Feature 81A in WALS)? Cross-Lingual Transfer:

RoBERTa (Robustly Optimized BERT Approach) is a transformers model pre‑trained on a large corpus of English data in a self‑supervised fashion. It builds on the BERT architecture but uses improved training methods (e.g., dynamic masking, larger batch sizes, more data) to achieve state‑of‑the‑art performance on many NLP tasks.