Dimension mismatches or shape errors when using Sentence Transformer embeddings in another tool or network typically occur due to inconsistencies in how the embeddings are structured, processed, or expected by downstream components. Here’s a breakdown of common causes and solutions:
1. Embedding Dimension Mismatch
Sentence Transformer models output embeddings with a fixed dimension (e.g., 384 for all-MiniLM-L6-v2 or 768 for bert-base). If your downstream tool or network expects a different input size, a shape error will arise. For example, if you trained a classifier expecting 512-dimensional inputs but used a Sentence Transformer with 384 dimensions, the final layer of the classifier would fail to process the embeddings. To resolve this, ensure the embedding dimension matches the input requirements of your tool. You might need to adjust the downstream model’s input layer or choose a Sentence Transformer with the required output dimension.
2. Incorrect Pooling or Aggregation
Some Sentence Transformers return token-level embeddings (e.g., per-word vectors) instead of a single sentence embedding if not configured properly. For instance, using model.encode() without specifying pooling (like mean pooling) might leave you with a tensor shaped [batch_size, sequence_length, embedding_dim] instead of the expected [batch_size, embedding_dim]. Always verify whether you’re extracting sentence-level embeddings (via pooling) or token-level embeddings. Tools expecting fixed-length inputs will fail if given token-level outputs. Use model.encode(sentences, convert_to_tensor=True) to ensure proper pooling.
3. Preprocessing or Framework Compatibility
Differences in data types, tensor shapes, or preprocessing steps between frameworks (e.g., PyTorch vs. TensorFlow) can cause errors. For example, if you pass a PyTorch tensor to a TensorFlow model without converting it to a NumPy array first, the tool might reject it. Similarly, batch dimensions (e.g., [1, 384] vs. [384]) or normalization steps (e.g., L2-normalized embeddings) might conflict with downstream assumptions. Double-check input formats, convert tensors to the expected type, and ensure preprocessing aligns with what the tool requires (e.g., flattening dimensions or adding/removing batch axes).
In summary, always validate the embedding dimensions, ensure proper pooling for sentence-level outputs, and confirm compatibility between frameworks and preprocessing steps. For debugging, print the shape and type of embeddings before passing them to the next component.