To truly appreciate the "extra quality" setting, let's look under the hood.
| Component | Standard | Extra Quality | |-----------|----------|----------------| | Embedding dim | 64-128 | 256-512 | | WALS iterations | 10-15 | 20-30 | | Unobserved weight | 0.001 | 0.0001 | | RoBERTa layer | last hidden | last 4 layers mean pooling | | Batch size | 256 | 1024 with gradient accumulation | | Precision | float32 | bfloat16 mixed precision | wals roberta sets extra quality
original_embeddings = model.get_input_embeddings().weight.detach().numpy() vocab_size, hidden_dim = original_embeddings.shape To truly appreciate the "extra quality" setting, let's
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