user_factors = model_wals.user_factors # shape: (n_users, 50) item_factors = model_wals.item_factors # shape: (n_items, 50)
Could you confirm:
Let me know, and I’ll provide a more precise, step‑by‑step solution. wals roberta sets upd
Here’s a concise, interesting content outline for WALS (Weighted Angle and Length Scaling) RoBERTa setups — a niche but powerful technique for improving sentence embeddings, especially for semantic textual similarity (STS) and retrieval tasks.
trainer.train()
model_wals = AlternatingLeastSquares(factors=50, regularization=0.01, iterations=15)
interaction_matrix = csr_matrix((ratings, (user_ids, item_ids))) user_factors = model_wals
If the "upd" refers to a specific updated release of a dataset (such as the WALS for Transformers initiatives often found on HuggingFace or GitHub), the usability is generally high for NLP researchers.
