Create README.md

README.md

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