metadata

dataset_info:
  features:
    - name: anchor
      dtype: string
    - name: positive
      dtype: string
    - name: negative
      dtype: string
    - name: anchor_translated
      dtype: string
    - name: positive_translated
      dtype: string
    - name: negative_translated
      dtype: string
  splits:
    - name: train
      num_bytes: 92668231
      num_examples: 277386
    - name: test
      num_bytes: 2815453
      num_examples: 6609
    - name: dev
      num_bytes: 2669220
      num_examples: 6584
  download_size: 26042530
  dataset_size: 98152904
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: dev
        path: data/dev-*
task_categories:
  - translation
  - feature-extraction
language:
  - en
  - tr
tags:
  - turkish
size_categories:
  - n<1K

all-nli-triplets-turkish

This dataset is a bilingual (English and Turkish) version of the sentence-transformers/all-nli dataset. It provides triplets of sentences in both English and their corresponding Turkish translations, making it suitable for training and evaluating multilingual and Turkish-specific Natural Language Understanding (NLU) models.

Each triplet consists of:

An anchor sentence.
A positive sentence (semantically similar to the anchor).
A negative sentence (semantically dissimilar to the anchor).

The dataset enables tasks such as Natural Language Inference (NLI), semantic similarity, and multilingual sentence embedding.

Languages

English (Original)
Turkish (ISO 639-1: tr, Translated)

Dataset Structure

The dataset contains six columns:

Column	Description
`anchor`	Anchor sentence in English.
`positive`	Positive sentence in English (semantically similar).
`negative`	Negative sentence in English (semantically dissimilar).
`anchor_translated`	Anchor sentence translated to Turkish.
`positive_translated`	Positive sentence translated to Turkish.
`negative_translated`	Negative sentence translated to Turkish.

The dataset is divided into three splits:

Train
Test
Dev

Example Row

{
  "anchor": "Moreover, these excise taxes, like other taxes, are determined through the exercise of the power of the Government to compel payment.",
  "positive": "Government's ability to force payment is how excise taxes are calculated.",
  "negative": "Excise taxes are an exception to the general rule and are actually decided on the basis of GDP share.",
  "anchor_translated": "Ayrıca, bu özel tüketim vergileri, diğer vergiler gibi, hükümetin ödeme zorunluluğunu sağlama yetkisini kullanarak belirlenir.",
  "positive_translated": "Hükümetin ödeme zorlaması, özel tüketim vergilerinin nasıl hesaplandığını belirler.",
  "negative_translated": "Özel tüketim vergileri genel kuralın bir istisnasıdır ve aslında GSYİH payına dayalı olarak belirlenir."
}

Dataset Creation

Source

This dataset is based on the sentence-transformers/all-nli dataset. The English triplets were directly translated into Turkish to provide a bilingual resource.

Translation Process

Sentences were translated using a state-of-the-art machine translation model.
Quality checks were performed to ensure semantic consistency between the English and Turkish triplets.

Motivation

This bilingual dataset was created to address the lack of Turkish resources in Natural Language Processing (NLP). It aims to support tasks such as multilingual sentence embedding, semantic similarity, and Turkish NLU.

Supported Tasks and Benchmarks

Primary Tasks

Natural Language Inference (NLI): Train models to understand sentence relationships.
Semantic Similarity: Train and evaluate models on semantic similarity across languages.
Multilingual Sentence Embedding: Create models that understand multiple languages.

Dataset Details

Split	Size (Triplets)	Description
Train	558k	Triplets for training
Test	6.61k	Triplets for testing
Dev	6.58k	Triplets for validation

How to Use

Here’s an example of how to load and explore the dataset:

from datasets import load_dataset

# Load dataset
dataset = load_dataset("mertcobanov/all-nli-triplets-turkish")

# Access the train split
train_data = dataset["train"]

# Example row
print(train_data[0])

Caveats and Recommendations

The translations were generated using machine translation, and while quality checks were performed, there may still be minor inaccuracies.
For multilingual tasks, ensure the alignment between English and Turkish triplets is preserved during preprocessing.

Citation

If you use this dataset in your research, please cite the original dataset and this translation effort:

@inproceedings{sentence-transformers,
  title={SentenceTransformers: Multilingual Sentence Embeddings},
  author={Reimers, Nils and Gurevych, Iryna},
  year={2019},
  url={https://huggingface.co/datasets/sentence-transformers/all-nli-triplet}
}

@misc{mertcobanov_2024,
  author = {Mert Cobanov},
  title = {Turkish-English Bilingual Dataset for NLI Triplets},
  year = {2024},
  url = {https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish}
}

License

This dataset follows the licensing terms of the original sentence-transformers/all-nli dataset. Ensure compliance with these terms if you use this dataset.

This card highlights both the bilingual nature and the structure of your dataset while making it easy for others to understand its purpose and use cases. Let me know if you'd like further refinements!