Why are my sentence embeddings coming out as all zeros or identical for different inputs when using a Sentence Transformer model?

When sentence embeddings from a Sentence Transformer model output all zeros or identical vectors for different inputs, the issue typically stems from one of three areas: preprocessing errors, model initialization problems, or incorrect pooling configuration. Let’s break these down systematically.

First, preprocessing errors are a common culprit. Sentence Transformers require text to be tokenized and formatted correctly before inference. If your input text is being processed incorrectly—for example, if the tokenizer truncates all tokens due to a mismatched maximum sequence length—the model might receive empty or invalid inputs. For instance, if your tokenizer is configured to strip special characters or split words improperly, the resulting token IDs could become a series of padding tokens (like zeros), leading the model to produce meaningless embeddings. Always verify the tokenizer’s output by printing the input_ids and attention_mask tensors to ensure they reflect the actual text. For example, a sentence like “Hello, world!” should generate non-zero token IDs, not a tensor filled with zeros or repeated values.

Second, model initialization issues can cause unexpected behavior. If the model isn’t loaded correctly—such as using a randomly initialized model instead of a pretrained one—it will generate random or uniform embeddings. For example, calling SentenceTransformer() without specifying a valid pretrained checkpoint (e.g., model = SentenceTransformer('all-MiniLM-L6-v2')) might default to an untrained configuration. Similarly, if the model’s weights aren’t properly loaded due to file corruption or incorrect paths, the embeddings will lack meaningful structure. To debug, check the model’s metadata (e.g., model._model_card or model.get_sentence_embedding_dimension()) to confirm it’s the expected pretrained version. If you’re using custom code, ensure you’re not inadvertently reinitializing layers or disabling gradient updates incorrectly.

Finally, pooling layer misconfiguration often leads to identical embeddings. Sentence Transformers use pooling layers (e.g., mean-pooling or CLS-pooling) to aggregate token-level embeddings into sentence vectors. If this layer is missing, misconfigured, or applied incorrectly—for instance, using a static operation like averaging over empty token embeddings—the output will collapse to a constant value. For example, if your pipeline skips pooling and directly uses the first token’s embedding (common in BERT-style models without fine-tuning), all outputs might align to a single vector. To fix this, inspect the model’s pooling layer via print(model._modules['1']) (assuming the second module is the pooler) and ensure it’s active. If you’ve customized the model, validate that the pooling step isn’t being bypassed or replaced with a faulty operation.

To resolve these issues, start by validating preprocessing steps, confirm the model is loaded correctly, and verify the pooling layer’s behavior. Debugging tools like printing intermediate tensor values or using a minimal test case (e.g., a single short sentence) can isolate the problem quickly.