Clarify message-level labeling and mixed-purpose email examples

README.md

@@ -22,6 +22,20 @@ The purposes are **correspondence, transactions/records, alerts/updates, newslet
 
 This release contains a **LoRA adapter and an Unsloth Clef joint decision head**. It is not a merged base model and requires `FastDecisionModel`, not a generic chat pipeline. The base is `unsloth/gemma-4-E2B-it-unsloth-bnb-4bit`, pinned at `0ae102ac289cc3f17500a607ad817849778b8ba7`.
 
+## Labeling unit and mixed-purpose messages
+
+Labels are **message-level, not thread-level**. Each dataset row represents one email message, and the model judges the supplied subject and body. It does not combine the purposes of every message in a conversation or determine the conversation's workflow state.
+
+The seven purpose decisions are independent: multiple labels, or none, can apply to one message. Judge the **current sender's adopted purpose**. Historical quoted requests do not automatically become current requests; a forward can carry a purpose when the sender adopts or relays it. Greetings, signatures, incidental links and routine footers do not establish an additional purpose by themselves.
+
+| Schematic message | Intended purpose labels illustrated |
+|---|---|
+| A payment receipt together with a shipping-status update | `transactions/records` and `alerts/updates` |
+| A concrete invitation with only a routine newsletter subscription footer | `scheduling/invitations`; the footer alone does not establish `newsletters/publications` |
+| An editorial newsletter that also contains a substantive, concrete event invitation | `newsletters/publications` and `scheduling/invitations` |
+
+These examples explain the labeling policy, not guaranteed model predictions or additional benchmark results. Labels describe observable communicative purposes, rather than every topic mentioned in the message.
+
 ## How it was made
 
 The model trained on [Letterweave 0.3.0](https://huggingface.co/datasets/Groktopus/Letterweave-0.3.0), an original, synthetic, machine-labeled email corpus. It contains 1,471 messages: 1,158 retained records from the previous corpus and 313 new reviewed examples. The split is 942 training, 270 validation, and 259 test messages; the fresh subset contributes 187, 62, and 64 respectively. Existing labels and splits were preserved. Related fresh scenario-suite/context families share one model split, with overrides documented in the dataset.