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--- |
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task_categories: |
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- translation |
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- sentence-similarity |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- multilingual |
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--- |
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# Multilingual Parallel Sentences with Semantic Similarity Scores and Quality Metrics |
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This dataset is a diverse collection of parallel sentences in English and various other languages, sourced from multiple high-quality datasets. |
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Each sentence pair includes a semantic similarity score calculated using the [Language-agnostic BERT Sentence Embedding (LaBSE) model](https://huggingface.co/sentence-transformers/LaBSE), |
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along with additional quality metrics. |
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### Supported Tasks |
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This dataset supports: |
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- Machine Translation |
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- Cross-lingual Semantic Similarity |
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- Multilingual Natural Language Understanding |
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- Translation Quality Estimation |
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### Languages |
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The dataset includes English paired with multiple languages from sources such as JW300, Europarl, TED Talks, OPUS-100, Tatoeba, Global Voices, and News Commentary. |
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Please see [sentence-transformers/parallel-sentences-datasets](https://huggingface.co/collections/sentence-transformers/parallel-sentences-datasets-6644d644123d31ba5b1c8785) |
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for the original sources. |
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## Dataset Structure |
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### Data Instances |
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Each instance contains: |
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- `english`: The English sentence (string) |
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- `non_english`: The corresponding sentence in another language (string) |
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- `distance`: Semantic similarity score (cosine distance) between the sentences (float) |
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- `quality`: Content quality score (float) |
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- `readability`: Readability score (float) |
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- `sentiment`: Sentiment score (float) |
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Example: |
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```json |
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{ |
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"english": "If we start to think exponentially, we can see how this is starting to affect all the technologies around us.", |
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"non_english": "Če začnemo misliti eksponentno, vidimo, kako to začenja vplivati na vse tehnologije okoli nas.", |
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"distance": 0.05299, |
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"quality": 0.3359375, |
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"readability": 0.103515625, |
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"sentiment": 0.45703125 |
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} |
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``` |
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### Data Splits |
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The dataset is divided into: |
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- Train: 867 042 rows (90%) |
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- Validation: 96 338 rows (10%) |
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- Total: 963 380 rows (100%) |
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## Dataset Creation |
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Sentences were downloaded from different splits and configurations of each dataset, ensuring a rich variety of linguistic representations. |
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To ensure high quality, the dataset was deduplicated, and only sentence pairs with a semantic similarity score (`distance`) below 0.25 were included. |
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A total of 5 000 sentences were downloaded from each split of each dataset, resulting in a final distribution of 90% training and 10% validation. |
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### Annotations |
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Semantic similarity scores were generated using the LaBSE model by calculating cosine distances between embeddings. |
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Additional metrics were annotated using the |
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[quality](https://huggingface.co/agentlans/deberta-v3-base-zyda-2-quality), |
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[readability](https://huggingface.co/agentlans/deberta-v3-base-zyda-2-readability), and |
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[sentiment](https://huggingface.co/agentlans/deberta-v3-base-zyda-2-sentiment) models. |
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## Considerations for Using the Data |
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### Social Impact |
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This dataset can enhance cross-lingual NLP models and applications by providing high-quality parallel sentences with semantic similarity and quality metrics. |
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### Known Limitations |
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- The semantic similarity (`distance`) and quality scores may not capture all nuances of cross-lingual similarity or translation quality. |
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- Coverage is limited to languages present in the source datasets. |
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- Filtering based on `distance < 0.25` may exclude some valid but less similar translations. |
