If you did find wals_roberta_sets_136.zip from an untrusted source (e.g., unknown email, torrent):
Legitimate linguistic datasets rarely contain executables – but ZIP can hold anything. Stay cautious.
The WALS RoBERTa 136zip model finds applications across various NLP domains:
Search academic papers for:
training_args = TrainingArguments( output_dir='./wals136_results', num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, evaluation_strategy="epoch", )
trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, )
trainer.train()
texts = df['description_text'].tolist() labels = df['feature_value'].astype('category').cat.codes.tolist() num_labels = len(df['feature_value'].unique())
The .zip extension is a compressed archive. A well-structured wals_roberta_sets_136.zip might contain:
wals_roberta_sets_136/
├── train.jsonl # 100 lines of "input": "...", "label": ...
├── valid.jsonl # 20 lines
├── test.jsonl # 16 lines (total 136 examples)
├── features.txt # List of 136 WALS feature IDs used
├── language_ids.txt # ISO codes of included languages
├── config.json # RoBERTa fine-tuning parameters
└── tokenizer/ # Custom tokenizer files for linguistic symbols
Alternatively, it could hold model checkpoints: PyTorch .bin files + config.json for a RoBERTa model fine-tuned on WALS.
If you have downloaded wals roberta sets 136zip, here is the standard workflow for using it: