Create README.md

README.md

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+---
+language:
+- ca
+- eu
+- multilingual
+multilinguality:
+- translation
+pretty_name: CA-EU Parallel Corpus
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+---
+
+# Dataset Card for CA-PT Parallel Corpus
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Source Data](#source-data)
+  - [Data preparation](#data-preparation)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Author](#author)
+  - [Contact Information](#contact-information)
+  - [Copyright](#copyright)
+  - [Licensing information](#licenciung-informatrion)
+  - [Funding](#funding)
+
+## Dataset Description
+
+### Dataset Summary
+
+The CA-EU Parallel Corpus is a Catalan-Basque dataset of **133.085.493** parallel sentences. The dataset was created to support Catalan NLP tasks, e.g., 
+Machine Translation.
+
+### Supported Tasks and Leaderboards
+
+The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score.
+
+### Languages
+
+The texts in the dataset are in Catalan and Basque.
+
+## Dataset Structure
+
+Two separated txt files are provided with the sentences sorted in the same order:
+
+- xxx.ca: contains XXX Catalan sentences.
+
+- xxx.eu: contains XXX Basque sentences.
+
+### Data Splits
+
+The dataset contains a single split: `train`.
+
+## Dataset Creation
+
+### Source Data
+
+Euskera-Catalan data collected from the web ([Opus](https://opus.nlpl.eu/)): 1.531.980 sentences
+Synthetic parallel data created in the frame of Project Ilenia by machine translating the parallel corpora ES-EU from Gaitu: 9.600.000 sentences.
+Synthetic parallel data created in the frame of Project Ilenia by machine translating the parallel corpora ES-GL from Nós: 36.000.000 sentences.
+Synthetic parallel data created in the frame of Project Ilenia by machine translating the parallel corpora ES-CA from AINA: 85.953.513 sentences.
+
+**Total:  133.085.493 parallel sentences** .
+
+### Data preparation
+
+ All datasets are deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75.
+ This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE).
+
+### Personal and Sensitive Information
+
+No anonymisation process was performed.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The purpose of this dataset is to help develop Machines Translation tasks for low-resource languages such as Catalan.
+
+### Discussion of Biases
+
+We are aware that since part of the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset.
+Nonetheless, we have not applied any steps to reduce their impact.
+
+### Other Known Limitations
+
+The dataset contains data of a general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
+
+## Additional Information
+
+### Author
+Language Technologies Unit (LangTech) at the Barcelona Supercomputing Center.
+
+### Contact information
+For further information, please send an email to [email protected].
+
+### Copyright
+Copyright Language Technologies Unit at Barcelona Supercomputing Center (2023).
+
+### Licensing information
+This work is licensed under a [Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+### Funding
+This work was funded by the SEDIA within the framework of ILENIA.