texts = df['description_text'].tolist() labels = df['feature_value'].astype('category').cat.codes.tolist() num_labels = len(df['feature_value'].unique())
Here is a deep dive into what these components represent and how they work together to enhance machine learning workflows. wals roberta sets 136zip
The suffix typically refers to a proprietary or specific archival format used to package these model sets. In large-scale deployment, "136" often denotes a specific versioning or a targeted parameter count (e.g., a distilled version of a model optimized for 136 million parameters). The zip aspect is crucial for: texts = df['description_text']
If you want a feature vector from RoBERTa (e.g., [CLS] embeddings) to use in another typological model: wals roberta sets 136zip