Wals Roberta Sets 136zip Here
If your goal is to work with WALS + RoBERTa but you cannot locate the exact 136zip file, consider these better-documented resources:
| Resource | Description |
|----------|-------------|
| WALS online API | https://wals.info/api/ – fetch features via JSON |
| URIEL typological database | 8,000+ languages with WALS features, ready for ML |
| XLM-RoBERTa (base) | Multilingual model, fine-tunable on WALS-derived tasks |
| lang2vec | Python library that converts WALS features into vectors |
| Typological Dataset for NLP | Hugging Face datasets hub – search "typology" |
Search these terms to find ready-to-use ZIPs or direct code.
class WALSDataset(torch.utils.data.Dataset): def init(self, encodings, labels): self.encodings = encodings self.labels = labels def getitem(self, idx): item = k: v[idx] for k, v in self.encodings.items() item['labels'] = torch.tensor(self.labels[idx]) return item def len(self): return len(self.labels) wals roberta sets 136zip
texts = df['description_text'].tolist() labels = df['feature_value'].astype('category').cat.codes.tolist() num_labels = len(df['feature_value'].unique())
If you cannot find the file or it is not working:
Disclaimer: I cannot provide a direct download link for copyrighted or obscure academic files. If this is a research artifact, you may need to access it via the author's published GitHub repository or a request to the research institution. If your goal is to work with WALS
Could you clarify your request? For example, are you asking to:
A common interpretation in NLP + typology:
Use a pre-trained RoBERTa model to predict WALS feature 136A (“Imperative-Hortative Systems”) from language descriptions or parallel text. class WALSDataset(torch
If that’s the case, I can outline how to develop such a feature:
If you have downloaded wals roberta sets 136zip, here is the standard workflow for using it: