README.md

---
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<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - intent-classification
  - multi-class-classification
pretty_name: Badhon/BanglaMedicalIntent
tags:
  - bangla
  - bengali
  - banglish
  - code-mixing
  - transliteration
  - low-resource
  - intent-detection
  - out-of-scope-detection
  - customer-support
  - medical
  - healthcare
  - synthetic
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: validation
        path: val.csv
      - split: test
        path: test.csv
dataset_info:
  features:
    - name: text
      dtype: string
    - name: intent
      dtype:
        class_label:
          names:
            '0': greeting
            '1': goodbye
            '2': thanks
            '3': appointment_book
            '4': appointment_manage
            '5': doctor_info
            '6': test_diagnostic
            '7': report_result
            '8': medicine_query
            '9': symptom_query
            '10': emergency
            '11': admission_discharge
            '12': billing_insurance
            '13': hospital_info
            '14': vaccination
            '15': complaint
            '16': agent_request
            '17': out_of_scope
    - name: script
      dtype: string
  splits:
    - name: train
      num_examples: 4494
    - name: validation
      num_examples: 809
    - name: test
      num_examples: 839
---


# Medical Conversation Intent Classification

An 18-intent classification dataset for a Bangladeshi hospital/clinic front-desk
chatbot, covering the three ways patients actually write:

| script | example | rows |
|---|---|---|
| `bn` Bengali script | `ডাক্তারের সিরিয়াল লাগবে` | 2,007 |
| `en` English | `I want to book as a new patient` | 1,928 |
| `bl` Banglish (romanized Bangla) | `report pathan` | 1,898 |
| `mx` code-mixed mid-sentence | `amar report ki ready হয়েছে` | 309 |

**6,142 rows, 18 intents** — including an explicit `out_of_scope` reject class
and a safety-critical `emergency` class. Every row is a distinct string; there
are no duplicate texts across the three splits.

> ⚠️ **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) and
> [Emergency](#emergency-read-this-before-deploying) before you rely on a number
> measured here.

## Scope, and it is the important part

This is an intent classifier for a hospital's **front desk**: booking, reports,
billing, directions. It is **not a diagnostic system.** `symptom_query` exists so
the bot can *recognize* that someone is describing symptoms and route them to a
human or a doctor — not so it can answer them. `emergency` is a separate intent
for the same reason: it must be detectable with high recall so the flow can
short-circuit to "call 999 / come to the ER now" instead of trying to be helpful.

## Dataset structure

### Fields

| field | type | description |
|---|---|---|
| `text` | `string` | the user message, 1–18 words (mean 5.2, 95th percentile 9) |
| `intent` | `class_label` | one of 18 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/BanglaMedicalIntent")
# DatasetDict({train: 4494, validation: 809, test: 839})
```

| split | rows | `bn` | `en` | `bl` | `mx` |
|---|---|---|---|---|---|
| `train` | 4,494 | 1,464 | 1,425 | 1,387 | 218 |
| `validation` | 809 | 272 | 254 | 244 | 39 |
| `test` | 839 | 271 | 249 | 267 | 52 |

Each split carries all four writing systems in roughly the same proportion, so
per-script accuracy on `test` is comparable to per-script accuracy on `train`.

**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.

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 **0.695** on `test` and **0.738** on the
hand-written holdout. Those two numbers agreeing is what tells you the benchmark
is measuring generalization.

### Label distribution

| intent | train | val | test | total | description |
|---|---|---|---|---|---|
| `doctor_info` | 382 | 64 | 73 | 519 | specialty, qualifications, sitting days, consultation fee |
| `symptom_query` | 373 | 73 | 48 | 494 | describes how they feel — routed, never answered clinically |
| `test_diagnostic` | 333 | 68 | 60 | 461 | lab tests, imaging, packages, prep, fasting, cost |
| `appointment_book` | 314 | 51 | 60 | 425 | wants a new appointment/serial |
| `emergency` | 288 | 46 | 55 | 389 | immediate danger — overrides every other label |
| `greeting` | 255 | 49 | 47 | 351 | opener, whole message |
| `goodbye` | 251 | 45 | 50 | 346 | sign-off |
| `vaccination` | 242 | 38 | 64 | 344 | schedule, availability, child immunization, certificates |
| `report_result` | 221 | 38 | 50 | 309 | a report they are already waiting for — ready? send it? |
| `hospital_info` | 217 | 47 | 37 | 301 | location, timings, departments, ambulance number, parking |
| `appointment_manage` | 209 | 43 | 41 | 293 | change, cancel, or check an existing booking |
| `medicine_query` | 207 | 40 | 41 | 288 | dosage, timing, side effects, substitutes, refills, stock |
| `admission_discharge` | 209 | 38 | 40 | 287 | beds, cabins, ICU, discharge process, attendant/visiting rules |
| `thanks` | 214 | 32 | 38 | 284 | gratitude, whole message |
| `complaint` | 197 | 37 | 40 | 274 | grievance with no specific remedy asked |
| `billing_insurance` | 205 | 35 | 32 | 272 | cost of admission, bill payment, insurance, receipts |
| `out_of_scope` | 186 | 37 | 35 | 258 | chitchat, other domains, noise |
| `agent_request` | 191 | 28 | 28 | 247 | escalate to a human |

Roughly balanced by design (per-intent row caps during generation).

### Label boundaries

Documented in full in the `domains/medical.py` docstring; the pairs that get
confused most, in order:

- **`appointment_book` vs `appointment_manage`** — a *new* serial vs changing,
  cancelling or checking an *existing* one. `kobe amar serial` is manage.
- **`doctor_info` vs `appointment_book`** — facts about a doctor vs clearly
  trying to book. If they are trying to book, prefer `appointment_book`.
- **`test_diagnostic` vs `report_result`** — the test hasn't happened yet (what
  tests, prep, cost) vs it already happened and they want the result.
- **`symptom_query` vs `emergency`** — see below.
- **`hospital_info` vs `admission_discharge`** — logistics for visitors and
  outpatients vs inpatient beds, ICU, and discharge.
- **`complaint`** — dissatisfied with no actionable request fitting above.

Overriding rules, in priority order:

1. **If it is an emergency, it is `emergency`**, whatever else it also is. A
   message that is both a symptom description and an emergency is `emergency`.
2. A greeting glued onto a real request is labeled by the **request**.

### `out_of_scope`

The reject class, and the reason to prefer this dataset over a 17-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 hospital (that is
`complaint` — actionable, route to a human), and vague-but-clinical fragments.

The class is capped at the same size as the others on purpose. An oversized
reject class raises the false-fallback rate — real patients routed to "I don't
understand" — which costs more in production than a missed rejection.

## Emergency: read this before deploying

**Do not ship the emergency path on this model alone.**

Explicit cardiac and stroke templates were added after the first training run
routed "chest pain radiating to the left arm" to `symptom_query` and a stroke
description to `admission_discharge`; that lifted test recall from 0.47 to 0.69.
Measured on the hand-written holdout, `emergency` recall is ~0.7–0.8: it catches
chest pain, bleeding, seizures and accidents, but it has been observed to miss
plain phrasings like "we need an ambulance at once". Synthetic templates teach
the phrasings someone thought to write down, and the tail of how people actually
report a crisis is longer than that.

Before deployment, gate the emergency path with, at minimum:

- a **keyword/regex pre-filter** (ambulance, 999, unconscious, not breathing,
  bleeding, chest pain, and the Bangla/Banglish equivalents) that fires
  regardless of what the classifier says;
- a **low probability threshold** on this class, biased hard toward recall; and
- **real chat logs** replacing these templates as soon as you have them.

A false positive costs one unnecessary escalation. A false negative does not cost
the same thing.

## 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 medical holdout in
`domains/medical.py` (159 items, not shipped as a split because it must never be
trained on), which is itself split so that tuning and reporting use different
sentences:

- `DEV_HOLDOUT` (84) — tune against this: thresholds, hyperparameters, model selection
- `TEST_HOLDOUT` (75) — read once, when you are done

```bash
INTENT_DOMAIN=medical 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.

Report, at minimum: overall accuracy, macro F1, **`emergency` recall separately**,
**OOS recall**, and the **false-fallback rate** (in-scope inputs wrongly sent to
fallback). Overall accuracy alone hides both classes that matter here.

## 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 (`report`, `test`, `serial`, `ICU`,
  `appointment`) 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, not random character swaps.
- **Fragments** — context-free follow-up turns (`kobe?`, `koto?`, `ready?`)
  where the intent rides on 1–4 words.
- **Glued social openers** — `assalamu alaikum apu amar report ready hoyeche ki`,
  labelled `report_result`.
- **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 medical` (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 patients write, including its blind spots.
  A model at 0.70 here is not a model at 0.70 in production.
- **Front desk only.** No clinical content, no diagnosis, no triage beyond
  detecting that a message *is* an emergency. Nothing here supports answering a
  medical question.
- **Short inputs.** Mean under 5 words. Real patients describing symptoms write
  much longer messages, and models trained here will be poorly calibrated on
  them — which is exactly where `emergency` and `symptom_query` live.
- **Under-represented code-mixing.** 309 `mx` rows (5%) versus a real inbox where
  code-mixing is far more common than that.
- **Bangladesh-specific.** Hospital and department vocabulary, ambulance number
  (999), cities, festivals, and honorifics (`vai`, `apu`) are all local.
- **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.
- **No inter-annotator agreement figure**, because there was one annotator.
- **No PII** — no real patient names, IDs, phone numbers or addresses, and no
  real medical records. Doctor names and IDs are made-up strings from a fixed
  list.

### Intended and out-of-scope uses

**Intended:** bootstrapping a Bangla/Banglish hospital front-desk 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; as any part of a diagnostic, triage, or clinical decision system; and
**never** as the sole gate on an emergency path. Any deployment touching patient
safety needs a human in the loop, a keyword pre-filter, and a calibrated reject
threshold.

## Citation

```bibtex
@misc{banglamedicalintent,
  title  = {BanglaMedicalIntent: Bangla / English / Banglish Medical Front-Desk Intent Classification},
  year   = {2026},
  note   = {Synthetic dataset, 18 intents, template-disjoint splits},
  howpublished = {\url{https://huggingface.co/datasets/Badhon/BanglaMedicalIntent}}
}
```

## 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
  hospital or clinic 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.