from transformers import RobertaTokenizer, RobertaModel import torch tokenizer = RobertaTokenizer.from_pretrained("roberta-base") model = RobertaModel.from_pretrained("roberta-base") text = "Example linguistic phrase for analysis." inputs = tokenizer(text, return_tensors="pt") outputs = model(**inputs) # 'last_hidden_state' can now be combined with the WALS feature tensor embeddings = outputs.last_hidden_state Use code with caution. Best Practices and Data Integrity
The official and most structured way to access WALS data is through the dump, a standardized format for linguistic data. This version is a zipped archive that contains the data as a set of CSV (Comma-Separated Values) files. This wals_dataset.cldf.zip archive is a key resource for any data scientist working with typological linguistic data and serves as the foundation upon which the "WALS Roberta Sets" are built. WALS Roberta Sets 1-36.zip
To understand the significance of this dataset archive, it helps to break down the technical components that make up its name. What is WALS? This wals_dataset
: Training with these sets helps models generalize better to unseen languages. : Training with these sets helps models generalize