Vemployee/miiq-intent-e5-small

MiiQ retrieval-intent classifier (multilingual-e5-small)

Classifies a chat query to an enterprise knowledge-base assistant into one of 14 retrieval intents, so the assistant can choose how to search its documents.

Intent	Meaning
exact_lookup	One stated fact, a named clause/section/form/code, or exact wording
term_search	A bare keyword, or where a term is mentioned
compliance_check	Whether something is allowed, required, eligible or covered
explanation	What something means, why, how it works, what a policy says
comparison	Differences or similarities between named things
computation	Arithmetic: totals, counts, durations, percentages
reasoning	What-if, implications, or a judgement/recommendation drawn from documents
procedural	Steps or process for doing something
summarization	Summary or key points of a document
greeting, thanks, farewell, chitchat	Social messages (chitchat is about the assistant itself)
off_topic	Unrelated to the organisation's work

Model

intfloat/multilingual-e5-small, fine-tuned end to end with a linear layer (cross-entropy, 4 epochs). The encoder is stored in half precision; the head is intent_head.json (classes, coef, intercept).

To score a query: prefix it with "query: ", mean-pool the last hidden state over the attention mask, L2-normalise, then apply softmax(coef · x + intercept).

Training data

842 English and Arabic queries written for this purpose from a labelling guide, with AI assistance: workplace questions about HR, finance, IT, procurement, contracts, safety and equipment documents, questions about the user's own uploaded files, social messages and off-topic requests. No user data was used.

Evaluation

Set	Queries	Accuracy	English / Arabic
Held-out, written separately from the training data	243	93%	93% / 92%
Held-out questions about the user's own files, with contrasts about the assistant	97	88%	95% / 76%
Real queries typed during internal testing	82	80%

Median latency is about 23 ms per query on four CPU threads.

Limits

• Several intents overlap by nature (for example exact_lookup and computation), and labels follow one labelling guide.
• The real-query sample is small.
• The model is tuned for workplace knowledge-base assistants and has not been evaluated elsewhere.