Wals Roberta Sets Top ❲Edge❳

from transformers import RobertaModel, RobertaTokenizer model = RobertaModel.from_pretrained("roberta-base", output_hidden_states=True) tokenizer = RobertaTokenizer.from_pretrained("roberta-base") outputs = model(input_ids) hidden_states = outputs.hidden_states # Tuple of 13 (embedding + 12 layers) Take top 4 layers (layers 9-12 in 0-indexing for base) top_layer_embeddings = torch.stack(hidden_states[-4:]).mean(dim=0)

class RobertaWALSProjector(nn.Module): def __init__(self, roberta_dim=768, latent_dim=200): super().__init__() self.roberta = RobertaModel.from_pretrained("roberta-base") self.projection = nn.Linear(roberta_dim, latent_dim) def forward(self, input_ids): roberta_out = self.roberta(input_ids).pooler_output return self.projection(roberta_out) wals roberta sets top

| Component | Hyperparameter | Recommended Value | |-----------|---------------|-------------------| | WALS | Rank (latent dim) | 200-500 | | WALS | Regularization (lambda) | 0.01 to 0.1 | | WALS | Weighting exponent (alpha) | 0.5 (implicit feedback) | | WALS | Number of iterations | 20-30 | | RoBERTa | Model variant | roberta-base (125M) or roberta-large (355M) | | RoBERTa | Max sequence length | 128 or 256 tokens | | RoBERTa | Fine-tuning learning rate | 2e-5 to 5e-5 | | Hybrid | Projection layer | 1-layer linear with no activation | | Training | Batch size | 256-1024 (WALS) / 16-32 (RoBERTa) | from transformers import RobertaModel

Need to dive deeper? Experiment with the code snippets provided, and don’t forget to share your results with the NLP community. latent_dim) def forward(self