Datasets:

Abduull6771
/

hausa-english-medical-translation

english stringclasses 192 values	hausa stringclasses 192 values
I have nausea.	Ina da tashin zuciya.
The nausea is getting worse.	tashin zuciya yana ta karuwa.
You have diabetes.	Kuna da ciwon sukari.
You need to see a specialist.	Kuna bukatar ganin ƙwararren likita.
Bring your medical records.	Ku kawo takardun lafiyarku.
Call an ambulance immediately.	Ku kira motar asibiti nan take.
Apply medicine to the affected area.	Shafa magani a wurin da ya shafa.
The fever started yesterday.	zazzabi ya fara jiya.
The diagnosis is diabetes.	Ganewar cutar ita ce ciwon sukari.
Please arrive 15 minutes early.	Ku zo minti 15 da wuri.
The vomiting started yesterday.	amai ya fara jiya.
Apply medicine to the affected area.	Shafa magani a wurin da ya shafa.
We need more tests to confirm.	Muna bukatar ƙarin gwaje-gwaje don tabbatarwa.
Wash your hands frequently.	Ku wanke hannuwanku akai-akai.
I need to schedule an appointment.	Ina bukatar yin alƙawari.
Avoid smoking.	Ku guje wa shan taba.
Call an ambulance immediately.	Ku kira motar asibiti nan take.
The headache started yesterday.	ciwon kai ya fara jiya.
Swallow the tablet whole.	Hadiye kwayan gaba ɗaya.
Can you come on Monday?	Za ku iya zuwa ranar Litinin?
The dizziness is mild.	jujjuyawar kai ba shi da tsanani.
Exercise regularly.	Ku yi motsa jiki akai-akai.
Get enough rest.	Ku yi isasshen hutu.
Apply pressure to stop bleeding.	Ku danna don dakatar da zubar da jini.
Get vaccinated.	Ku yi rigakafi.
Please arrive 15 minutes early.	Ku zo minti 15 da wuri.
The dizziness is getting worse.	jujjuyawar kai yana ta karuwa.
The patient has dizziness.	Majinyaci yana da jujjuyawar kai.
Swallow the tablet whole.	Hadiye kwayan gaba ɗaya.
Apply pressure to stop bleeding.	Ku danna don dakatar da zubar da jini.
The patient needs urgent care.	Majinyaci yana bukatar kula da gaggawa.
The dizziness started yesterday.	jujjuyawar kai ya fara jiya.
Can you come on Monday?	Za ku iya zuwa ranar Litinin?
He/She is experiencing diarrhea.	Yana/Tana fama da gudawa.
The patient has pain.	Majinyaci yana da ciwo.
Wash your hands frequently.	Ku wanke hannuwanku akai-akai.
The test results are normal.	Sakamakon gwajin ya kasance na al'ada.
The diagnosis is malaria.	Ganewar cutar ita ce zazzabin cizon sauro.
Do you have any allergies?	Kuna da rashin jituwa da wani abu?
Do you have any allergies?	Kuna da rashin jituwa da wani abu?
Apply pressure to stop bleeding.	Ku danna don dakatar da zubar da jini.
Swallow the tablet whole.	Hadiye kwayan gaba ɗaya.
The fever is severe.	zazzabi yana da tsanani.
We need to run some tests.	Muna bukatar yin wasu gwaje-gwaje.
The pain started yesterday.	ciwo ya fara jiya.
Use mosquito nets.	Ku yi amfani da gidajen sauro.
The test results are normal.	Sakamakon gwajin ya kasance na al'ada.
Can you come on Monday?	Za ku iya zuwa ranar Litinin?
The diagnosis is malaria.	Ganewar cutar ita ce zazzabin cizon sauro.
The patient needs urgent care.	Majinyaci yana bukatar kula da gaggawa.
Use mosquito nets.	Ku yi amfani da gidajen sauro.
Swallow the tablet whole.	Hadiye kwayan gaba ɗaya.
The pain is getting worse.	ciwo yana ta karuwa.
Swallow the tablet whole.	Hadiye kwayan gaba ɗaya.
Take the medicine with water.	Ɗauki maganin da ruwa.
When is your next available appointment?	Yaushe alƙawarin ku na gaba?
Apply pressure to stop bleeding.	Ku danna don dakatar da zubar da jini.
I need to schedule an appointment.	Ina bukatar yin alƙawari.
I have headache.	Ina da ciwon kai.
Avoid smoking.	Ku guje wa shan taba.
You have tuberculosis.	Kuna da tarin huhu.
Please arrive 15 minutes early.	Ku zo minti 15 da wuri.
Have you taken any medicine?	Kun ɗauki wani magani?
Call an ambulance immediately.	Ku kira motar asibiti nan take.
The patient needs urgent care.	Majinyaci yana bukatar kula da gaggawa.
I will prescribe some medicine.	Zan rubuta muku magani.
Eat healthy food.	Ku ci abinci mai inganci.
The diagnosis is malaria.	Ganewar cutar ita ce zazzabin cizon sauro.
Apply pressure to stop bleeding.	Ku danna don dakatar da zubar da jini.
The test results are normal.	Sakamakon gwajin ya kasance na al'ada.
The nausea started yesterday.	tashin zuciya ya fara jiya.
Call an ambulance immediately.	Ku kira motar asibiti nan take.
Take one medicine daily.	Ɗauki ɗaya magani kowace rana.
Take the medicine with water.	Ɗauki maganin da ruwa.
Do not stop the medicine suddenly.	Kada ka daina maganin kwatsam.
Take three syrup three times daily.	Ɗauki uku ruwan magani sau uku a rana.
When did this start?	Yaushe wannan ya fara?
We need more tests to confirm.	Muna bukatar ƙarin gwaje-gwaje don tabbatarwa.
The patient needs urgent care.	Majinyaci yana bukatar kula da gaggawa.
Please arrive 15 minutes early.	Ku zo minti 15 da wuri.
Drink plenty of water.	Ku sha ruwa da yawa.
The patient needs urgent care.	Majinyaci yana bukatar kula da gaggawa.
We need to run some tests.	Muna bukatar yin wasu gwaje-gwaje.
The X-ray shows {finding}.	Hoton X-ray ya nuna {hausa_finding}.
The vomiting is getting worse.	amai yana ta karuwa.
Go to the emergency room.	Ku je dakin gaggawa.
The test results are normal.	Sakamakon gwajin ya kasance na al'ada.
Go to the emergency room.	Ku je dakin gaggawa.
The test results are normal.	Sakamakon gwajin ya kasance na al'ada.
The test results are normal.	Sakamakon gwajin ya kasance na al'ada.
The appointment is at 2 PM.	Alƙawarin ƙarfe biyu na rana.
Drink plenty of water.	Ku sha ruwa da yawa.
Do not stop the medicine suddenly.	Kada ka daina maganin kwatsam.
Drink plenty of water.	Ku sha ruwa da yawa.
Do you have any allergies?	Kuna da rashin jituwa da wani abu?
Wash your hands frequently.	Ku wanke hannuwanku akai-akai.
Drink plenty of water.	Ku sha ruwa da yawa.
We need to run some tests.	Muna bukatar yin wasu gwaje-gwaje.
The patient has fatigue.	Majinyaci yana da gajiya.
Where does it hurt?	A ina kake jin ciwo?

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YAML Metadata Warning:The task_ids "machine-translation" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation

Hausa-English Medical Translation Dataset

Dataset Description

This dataset contains 15,019 parallel English-Hausa sentence pairs specifically curated for medical and healthcare translation tasks. It's designed to support machine translation research for the Hausa language in the medical domain.

Dataset Summary

Language pair: English ↔ Hausa
Domain: Medical/Healthcare
Size: 15,019 sentence pairs
Split: 90% train, 10% validation
Generated: 2025-10-02

Supported Tasks

Machine Translation (English to Hausa)
Machine Translation (Hausa to English)
Medical text translation
Cross-lingual medical information retrieval

Dataset Structure

Data Instances

{
  "english": "The patient has a fever.",
  "hausa": "Majinyaci yana da zazzabi."
}

Data Fields

english: English medical/healthcare text
hausa: Corresponding Hausa translation

Data Splits

Train: 13,517 pairs
Validation: 1,502 pairs

Dataset Creation

Source Data

The dataset was generated using advanced natural language processing techniques to create high-quality medical translations covering:

Medical symptoms and conditions (25%)
Medications and treatments (20%)
Doctor-patient consultations (20%)
Prevention and wellness (15%)
Emergency situations (10%)
Medical knowledge base (10%)

Data Quality

All translations were generated using linguistic patterns and medical terminology
Quality validation through automatic checks
Domain-specific vocabulary coverage
Balanced representation across medical subcategories

Uses

Direct Use

This dataset can be used to:

Train machine translation models for English-Hausa medical translation
Fine-tune existing multilingual models for medical domain
Evaluate translation quality in low-resource language settings
Support healthcare accessibility in Hausa-speaking regions

Out-of-Scope Use

General domain translation (optimized for medical content)
Real-time clinical decision making without human oversight
Legal or official medical document translation without verification

Dataset Card Authors

This dataset was created as part of the Hausa Healthcare Translation Project to improve medical accessibility for Hausa-speaking communities.

Licensing Information

This dataset is released under the MIT License.

Citation Information

@dataset{hausa_english_medical_2025,
  title={Hausa-English Medical Translation Dataset},
  author={Healthcare Translation Project},
  year={2025},
  url={https://huggingface.co/datasets/Abduull6771/hausa-english-medical-translation}
}

Contributions

We welcome contributions to improve this dataset. Please see our repository for guidelines on how to contribute.

This dataset is part of efforts to improve healthcare accessibility for Hausa-speaking communities through better language technology.

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Number of rows:

15,019