feat: add the dimension notes

README.md

@@ -257,6 +257,10 @@ you can adjust it by passing the `truncate_dim` parameter to the `encode` functi
 embeddings = model.encode(['Sample text'], truncate_dim=256)
 ```
 
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+Note that the `truncate_dim` could be any integer between 1 and 1024 for the `separation`, `classification`, and `text-matching` tasks. As for the `retrieval.passage` and `retrieval.query` tasks, the value must be larger than the length of the instruction prompt. By default, the value must be larger than 9 for the `retrieval.passage` task and larger than 12 for the `retrieval.query` task.
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 The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`:
 
 ```bash