I'm building a document classification pipeline with embeddings, what model type fits best

For a document classification pipeline using embeddings, the best fit is usually a sentence/document embedding model rather than a token-level or generative model.

Good model types to consider

1. Sentence Transformers / bi-encoder models

   • Best default choice for classification with embeddings.
   • Produce one vector per document.
   • Efficient for training a lightweight classifier on top.
   • Examples:
     • all-MiniLM-L6-v2
     • bge-small-en, bge-base-en
     • e5-base, e5-small

2. Transformer encoder models fine-tuned for classification

   • Best if you want end-to-end supervised classification.
   • Examples:
     • BERT, RoBERTa, DeBERTa
   • These often outperform generic embeddings if you have enough labeled data.

3. Domain-specific embedding models

   • Best if your documents are specialized:
     • legal
     • medical
     • financial
     • technical support
   • A domain-tuned embedding model can improve accuracy a lot.

Practical recommendation

• If you want a fast, scalable pipeline:
  • Use a sentence embedding model + logistic regression / linear SVM / small MLP
• If you want highest accuracy with labeled data:
  • Fine-tune a Transformer encoder classifier
• If your docs are long:
  • Use a long-context encoder or chunk the document and aggregate embeddings.

Rule of thumb

• Few labels / rapid prototyping: sentence embeddings
• Medium-to-large labeled dataset: fine-tuned encoder classifier
• Very long documents: long-context model or chunk-based embedding strategy

If you tell me:

• document length,
• number of classes,
• amount of labeled data,
• and whether this is single-label or multi-label,

I can recommend a specific model and architecture.