--- language: - bn - en license: cc-by-nc-sa-4.0 annotations_creators: - machine-generated language_creators: - machine-generated - expert-generated multilinguality: - multilingual size_categories: - 1K ⚠️ **This is synthetic data.** It is a bootstrap for getting a CPU intent > classifier off the ground when you have no logs yet, not a substitute for real > ones. See [Limitations](#limitations-and-bias) before you rely on a number > measured here. The companion hand-written holdout is the honest signal. ## Dataset structure ### Fields | field | type | description | |---|---|---| | `text` | `string` | the user message, 1–16 words | | `intent` | `class_label` | one of 17 labels (below) | | `script` | `string` | `bn` \| `en` \| `bl` \| `mx` — writing system, useful for per-script error analysis | `script` is metadata, not a training feature. It exists so you can report accuracy per writing system, which is where the interesting failures hide — Banglish and code-mixed rows are consistently harder than either monolingual form. ### Splits ```python from datasets import load_dataset ds = load_dataset("Badhon/BanglaBankingIntent") # DatasetDict({train: 4277, validation: 692, test: 718}) ``` **The splits are disjoint at template level, not row level.** Each template is assigned to exactly one split *before* it expands into surface rows, so no test row is a respelling, recasing, code-mixing or politeness-affixed variant of a training row. Leakage is also blocked on a punctuation/case/affix-insensitive canonical form, so `balance` in train does not permit `Balance??` in test. A dataset built the naive way — expand first, split rows randomly — reports ~99.9% test accuracy that is pure memorization. Under template-level splitting the shipped transformer scores **0.715** on `test` and **0.518** on the hand-written holdout. That gap is the honest measure of how much of the test score is convention-following rather than generalization. ### Label distribution | intent | train | val | test | total | description | |---|---|---|---|---|---| | `fund_transfer` | 325 | 44 | 49 | 418 | wants to move money now — send/transfer/pay | | `balance_inquiry` | 305 | 37 | 44 | 386 | a read of current state — "how much is in there" | | `card_issue` | 291 | 48 | 46 | 385 | card blocked, lost, stolen, PIN, activation, expiry | | `loan_inquiry` | 295 | 43 | 47 | 385 | loans, EMI, interest rates, eligibility, repayment | | `branch_atm_info` | 268 | 60 | 49 | 377 | where is a branch/ATM, hours, is it open | | `transaction_failed` | 280 | 38 | 43 | 361 | a specific payment debited but did not arrive, or was declined | | `greeting` | 261 | 49 | 47 | 357 | opener, whole message | | `goodbye` | 264 | 46 | 46 | 356 | sign-off | | `account_opening` | 249 | 42 | 53 | 344 | wants to open a new account; documents, minimum deposit | | `transaction_history` | 268 | 35 | 33 | 336 | a read of past events — statements, "last 5 transactions" | | `thanks` | 230 | 32 | 36 | 298 | gratitude, whole message | | `complaint` | 221 | 37 | 39 | 297 | grievance with no specific remedy asked | | `charges_fees` | 217 | 36 | 41 | 294 | maintenance fee, transfer charge, annual fee, excise duty | | `account_issue` | 207 | 44 | 39 | 290 | an existing account is frozen, dormant, locked, KYC expired | | `login_issue` | 211 | 38 | 41 | 290 | cannot get into the app — password, OTP, app PIN reset | | `out_of_scope` | 206 | 34 | 38 | 278 | chitchat, other domains, noise | | `agent_request` | 179 | 29 | 27 | 235 | escalate to a human | Roughly balanced by design (per-intent row caps during generation). ### Label boundaries Several intents share vocabulary (`taka`, `account`, `transaction`) and differ only in what the user wants done. The tie-breaks used to label consistently, documented in full in the `domains/banking.py` docstring: - **`balance_inquiry` vs `transaction_history`** — current state vs past events. A question about *one* specific transaction that went wrong is `transaction_failed`, not history. - **`fund_transfer` vs `transaction_failed`** — wants to move money *now* vs the money already moved and is missing. The defining feature of `transaction_failed` is a broken transaction, not a general grievance. - **`login_issue` vs `card_issue`** — app/internet-banking access (password, OTP, app PIN) vs the physical/virtual card (including *card* PIN). This split is deliberate and is the most common labeling mistake in this set. - **`account_issue` vs `account_opening`** — an existing account is broken vs wants a new one. - **`complaint`** — angry with no actionable request that fits above. If the user is angry *and* names a failed transfer, label `transaction_failed`: the actionable intent wins. Rule that overrides all of the above: **a greeting glued onto a real request is labeled by the request, never the greeting.** `assalamu alaikum vai amar card block hoye gese` is `card_issue`. ### `out_of_scope` The reject class, and the reason to prefer this dataset over a 16-intent one. A closed-set softmax must put ~1.0 of its probability mass on *some* label, so a model without a reject class answers `tomar basa kothay?` as a confident `complaint`. No confidence threshold fixes that, because the model was never given a way to express "none of the above". Coverage spans bot-directed chitchat (`tumi ki manush`), other industries and domains (weather, cricket, prayer times, politics), general-assistant requests (write a poem, do this maths), and meta/noise (`test test`, keyboard mash, emoji-only, `hmm`). Deliberately **not** `out_of_scope`: profanity aimed at the bank (that is `complaint` — actionable, route to a human), and vague-but-financial fragments (`koto ache?` is `balance_inquiry`). The class is capped at the same size as the others on purpose. An oversized reject class raises the false-fallback rate — real customers routed to "I don't understand" — which costs more in production than a missed rejection. ## Evaluation **Do not report the `test` split alone.** It is template-disjoint from train, which makes it honest, but it still only answers "can you generalize across our own templates". Pair it with the hand-written banking holdout (156 items, not shipped as a split because it must never be trained on): ```bash INTENT_DOMAIN=banking python transformer_model/eval_holdout.py ``` Every holdout item is written by hand to share no template with the generated data, and the generator enforces this: any generated row matching a holdout item is dropped at source, so promoting a good holdout sentence into a template cannot silently contaminate training. For a reject class, accuracy is the wrong headline. Track the two numbers that trade off against each other: - **OOS recall** — off-domain inputs correctly routed to fallback - **false-fallback rate** — in-scope inputs wrongly sent to fallback (the real cost; this is what annoys customers) A model at 99% on the 16 business intents with 0% OOS recall is worse in production than one a point lower with 85%. ## How it was built Templates → bounded slot fills → sampled surface variants, with the split assigned at step one. Stages that exist because real messages have properties templates don't: - **Code-mixing** — a Banglish→Bengali lexicon flips a random 40–80% subset of words mid-sentence. Latin loanwords (`account`, `balance`, `transfer`, `card`, `OTP`, `EMI`) are deliberately excluded from the lexicon: Bangladeshi users type those in Latin even inside an otherwise-Bengali sentence, and that asymmetry is the pattern worth learning. - **Phonetic noise** — Banglish misspelling is sound-level substitution (`bh↔v`, `sh↔s`, `ph↔f`), dropped vowels (`kemon`→`kmon`) and word-boundary drift (`koto taka`→`kototaka`), not random character swaps. - **Fragments** — context-free follow-up turns (`koto?`, `kothay`, `hoyni`) where the intent rides on 1–4 words. - **Glued social openers** — `assalamu alaikum vai amar balance koto`, labelled `balance_inquiry`. - **Rambling preambles** — a sentence of context before the actual question, so the model sees inputs longer than 8 words. Reproduce with `python generate_domain_data.py banking` (seeded, deterministic). Adding a template to one intent does not reshuffle any other intent's split assignment. ## Limitations and bias Please read this section before using the dataset as a benchmark. - **Synthetic.** Generated from hand-written templates, not collected from users. It encodes one author's model of how customers write, including its blind spots. A model at 0.71 here is not a model at 0.71 in production. - **Short inputs.** Mean under 5 words. Models trained here will be poorly calibrated on long multi-paragraph messages. - **Under-represented code-mixing.** 353 `mx` rows (6%) versus a real inbox where code-mixing is far more common than that. It is seasoning here, not a first-class script. - **Bangladesh-specific.** Payment wallets (bKash, Nagad, Rocket), local bank and branch vocabulary, cities, festivals (Eid, Puja), and honorifics (`vai`, `apu`) are all local. Indian retail-banking vocabulary is absent entirely. - **Romanization is not standardized.** Banglish has no orthography. The phonetic-variant generator covers a fraction of real spelling space, and its substitution rules are hand-picked rather than learned from data. - **Label noise on the overlapping boundaries.** The tie-breaks above are applied consistently by construction, but they are one defensible reading of genuinely ambiguous cases. Your product may want them drawn elsewhere. - **No inter-annotator agreement figure**, because there was one annotator. - **No PII** — no real account numbers, names, phone numbers or addresses. Account and transaction references are made-up strings from a fixed list. ### Intended and out-of-scope uses **Intended:** bootstrapping a Bangla/Banglish banking intent classifier before you have logs; benchmarking small CPU models (fastText, distilled transformers) on code-mixed short text. **Not intended:** as evidence of production accuracy; as a general Bangla NLP benchmark; for any high-stakes routing (payments, disputes, fraud, legal) without a human in the loop and a calibrated reject threshold. Money movement in particular should never be triggered by this classifier alone. ## Citation ```bibtex @misc{banglabankingintent, title = {BanglaBankingIntent: Bangla / English / Banglish Banking Intent Classification}, year = {2026}, note = {Synthetic dataset, 17 intents, template-disjoint splits}, howpublished = {\url{https://huggingface.co/datasets/Badhon/BanglaBankingIntent}} } ``` ## Licensing **CC BY-NC-SA 4.0** ([Creative Commons Attribution-NonCommercial-ShareAlike 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)). The content is wholly generated from templates written for this repository, so there is no upstream corpus license to inherit. What the terms mean in practice: - **BY** — attribute the source when you use or redistribute it. - **NC** — **no commercial use.** Training a classifier that serves a commercial bank is a commercial use. If this dataset is meant to be deployable inside a business, `cc-by-sa-4.0` or `apache-2.0` is the licence you want instead. - **SA** — derivatives, including modified or extended versions of the data, must carry the same licence. Whether a *model* trained on it counts as a derivative work is legally unsettled and jurisdiction-dependent. Add a `LICENSE` file containing the full CC BY-NC-SA 4.0 text alongside this card; HuggingFace renders the tag either way, but the file is what makes the grant explicit to anyone who downloads the CSVs on their own.