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Update README.md

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@@ -7,6 +7,8 @@ language_creators:
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  - found
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  language:
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  - ca
 
 
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  multilinguality:
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  - monolingual
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  pretty_name: teca
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  - unknown
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  source_datasets: []
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  task_categories:
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- - text-classification
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  task_ids:
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  - natural-language-inference
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@@ -38,6 +40,8 @@ Professional translation into Catalan of The Cross-lingual Natural Language Infe
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  XNLI is an evaluation corpus for language transfer and cross-lingual sentence classification in 15 languages. It is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations.
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  ### Supported Tasks and Leaderboards
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  Textual entailment, Text classification, Language Model
@@ -107,15 +111,19 @@ visit the [XNLI's webpage](https://github.com/facebookresearch/XNLI).
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  #### Who are the source language producers?
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  ### Annotations
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  #### Annotation process
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  #### Who are the annotators?
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- [N/A]
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  ### Personal and Sensitive Information
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  ## Considerations for Using the Data
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- The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations.
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-
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  ### Social Impact of Dataset
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  We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
@@ -148,7 +154,7 @@ This work was funded by the [Departament de la Vicepresidència i de Polítiques
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  ### Licensing Information
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-
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  ### Citation Information
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  - found
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  language:
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  - ca
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+ license:
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+ - cc-by-nc-sa-4.0
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  multilinguality:
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  - monolingual
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  pretty_name: teca
 
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  - unknown
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  source_datasets: []
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  task_categories:
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+ - text-classificationThis is a professional translation of the XQuAD corpus and its annotations.
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  task_ids:
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  - natural-language-inference
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  XNLI is an evaluation corpus for language transfer and cross-lingual sentence classification in 15 languages. It is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations.
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+ XNLI is restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
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+
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  ### Supported Tasks and Leaderboards
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  Textual entailment, Text classification, Language Model
 
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  #### Who are the source language producers?
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+ For more information on how XNLI was created, refer to the paper [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053), or
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+ visit the [XNLI's webpage](https://github.com/facebookresearch/XNLI).
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+
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  ### Annotations
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  #### Annotation process
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+ [N/A]
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  #### Who are the annotators?
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+ This is a professional translation of the XNLI corpus and its annotations.
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  ### Personal and Sensitive Information
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  ## Considerations for Using the Data
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  ### Social Impact of Dataset
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  We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
 
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  ### Licensing Information
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+ XNLI is restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
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  ### Citation Information
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