English-Gujarati BFSI Domain Parallel Corpora

A high-quality bilingual dataset containing sentence-aligned English-Gujarati text pairs for the Banking, Financial Services, and Insurance (BFSI) domain. Supports translation, NLP, and LLM training.

About This OTS Dataset

Introduction

Welcome to the English-Gujarati Bilingual Parallel Corpora dataset for the Banking, Financial Services, and Insurance(BFSI) domain. This meticulously curated dataset offers a rich collection of bilingual sentence pairs translated between English and Gujarati. It serves as a valuable resource for developing domain-specific machine translation systems, language models, and NLP applications within the BFSI sector.

Dataset Content

•Volume and Diversity

•

Extensive Coverage: Contains over 50,000 bilingual sentence pairs, ideal for a wide range of language processing tasks.

•

Translator Diversity: Created with the help of 200+ native Gujarati translators, ensuring varied linguistic styles, tone, and regional expressions.

•Sentence Diversity

•

Word Count: Sentences range from 7 to 25 words, suitable for model training and evaluation.

•

Syntactic Variety: Includes simple, compound, and complex sentence structures.

•

Grammatical Forms: Interrogative (questions) and imperative (commands), Affirmative and negative statements, Active and passive voice constructions.

•

Figurative Language: Incorporates idioms, metaphors, and colloquial expressions relevant to real-world BFSI communications.

•

Discourse Features: Includes logical connectors and transitional phrases for coherent, natural language flow.

•

Cross Translation: Supports bi-directional translation with content translated both from English to Gujarati and Gujarati to English.

Domain-Specific Content

•

Specialized Terminology: Covers technical vocabulary from banking, insurance, financial services, compliance, investment, and fintech.

•

Authentic Industry Language: Captures real-world usage, including expressions from customer service conversations, financial reporting, and policy documentation.

•

Contextual Coverage: Draws content from scenarios such as:

•Banking transactions and statements

•Risk management reports

•Compliance policies

•Claims processing and customer support dialogs

•

Cross-Domain Elements: Includes supporting vocabulary from general business, legal, and technology domains, relevant to modern BFSI operations.

Format and Structure

•

File Formats: Delivered in Excel format by default, with easy conversion to JSON, TMX, XML, XLIFF, XLS, and other widely supported industry formats.

•

Dataset Structure: Serial Number, Unique ID, Source Sentence and Source Word Count, Target Sentence and Target Word Count

Usage and Applications

•

Machine Translation and Localization: Supports training of accurate translation models and localization systems specific to the BFSI sector.

•

NLP Systems: Useful for enhancing tools such as grammar checkers, spell checkers, predictive text, and speech/text understanding engines.

•

Large Language Models (LLMs): Enables fine-tuning and bilingual enhancement of LLMs for:

•Financial content generation

•Summarization of market reports

•Automated responses to customer service and compliance queries

Secure and Ethical Collection

•

Built on Yugo: Entire dataset developed on FutureBeeAI’s proprietary Yugo platform.

•

Data Security: Data remained within a closed, secure environment during the entire creation process.

•

Privacy Compliant: Contains no personally identifiable information (PII).

•

IP-Safe: All content is original and does not infringe on third-party copyrights.

Updates and Customization

To ensure continued accuracy and domain relevance, the dataset is regularly updated.

•Annotation Options:

•Part-of-speech tagging

•Named Entity Recognition (NER)

•Sentiment analysis

•Intent classification

•Translation ranking and others

•

Corpus Classification: Tagging and categorization by sentence type, topic, or subdomain.

•

Custom Collection: Datasets can be built for any language pair and business domain based on specific client requirements.

Licensing

This English-Gujarati Bilingual Corpus for the BFSI domain is created by FutureBeeAI and is available for commercial use by organizations, researchers, and AI developers.

Use Cases

MT Engine

Language model

Predictive keyboards

Spell check

Grammar correction

Use of parallel corpus dataset in Text/speech system

Text/speech systems

Dataset Sample(s)

SAMPLE

SOURCE LANGUAGE

TARGET LANGUAGE

The system of tokenization to prevent bank frauds will come into effect from October 1.

બેન્ક ફ્રોડ રોકવા ટોકનાઈઝેશનની સિસ્ટમ ૧લી ઓક્ટોબરથી અમલમાં.

No one will be able to know your debit/credit card number with the new system.

નવી સિસ્ટમથી કોઈ તમારો ડેબિટ/ક્રેડિટ કાર્ડ નંબર જાણી નહિ શકે.

There is need to promote digital banking in rural areas.

ગ્રામીણ વિસ્તારોમાં ડિજિટલ બેંકિંગને પ્રોત્સાહન આપવાની જરૂર.

RBI introduces internet banking guidelines for rural banks

ગ્રામીણ બેંકો માટે ઈન્ટરનેટ બેંકિંગ માટેની RBIની માર્ગદર્શિકા રજૂ કરી.

The scope of services of regional rural banks is limited.

પ્રાદેશિક ગ્રામીણ બેંકોની સેવાઓનો વિસ્તાર મર્યાદિત છે.

RBI has recently issued a new guideline.

RBIએ હાલમાં જ એક નવી ગાઈડલાઈન બહાર પાડી છે.

Preserve the message received after making UPI payments.

UPI કર્યા બાદ પ્રાપ્ત થયેલા મેસેજને સાચવી રાખો.

Airtel Payments Bank has launched micro ATM on Wednesday.

એરટેલ પેમેન્ટ્સ બેંકે બુધવારે માઈક્રો એટીએમ લોન્ચ કર્યુ છે.

Customers of all banks will be able to withdraw money through micro ATMs

બધી જ બેંકોના ગ્રાહકો માઈક્રો એટીએમ દ્વારા રૂપિયા ઉપાડી શકશે

Where to invest to earn Rs 10 lakh in just three years?

માત્ર ત્રણ જ વર્ષમાં 10 લાખ કમાવવા શેમાં રોકાણ કરવું?

ATTRIBUTES

Target Language :Gujarati

Source Language :English

Domain :BFSI

Dataset Details

Dataset Type

Text Corpus

Volume

50K+ Sentences

Media type

Text

Language Pair

English-Gujarati

File Details

Type

Bilingual

Word Count

7 to 25 Words per Asset

Format

XLSX, TMX, XML, XLIFF, XLS

Annotation

Read the License Terms

Browse FAQs

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