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Update README.md
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README.md
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@@ -78,11 +78,11 @@ A line includes four values: a sentence1 (the premise); a sentence2 (the hypothe
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Labels can be "entailment" if the premise entails the hypothesis, "contradiction" if it contradicts it or "neutral" if it neither implies it nor denies it.
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{
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"gold_label": "neutral",
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"pairID": 1,
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"sentence1": "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos.",
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"sentence2": "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
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}
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### Data Fields
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#### Initial Data Collection and Normalization
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Please refer to the respective documentations of the original datasets:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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#### Who are the source language producers?
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Please refer to the respective documentations of the original datasets:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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### Annotations
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#### Annotation process
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Please refer to the respective documentations of the original datasets:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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#### Who are the annotators?
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Please refer to the respective documentations of the original datasets:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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### Personal and Sensitive Information
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In general, no sensitive information is conveyed in the sentences.
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Please refer to the respective documentations of the original datasets:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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## Considerations for Using the Data
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### Discussion of Biases
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Please refer to the respective documentations of the original datasets:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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### Other Known Limitations
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The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.
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For discussion on the biases and limitations of the original datasets, please refer to their respective documentations:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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## Additional Information
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### Licensing Information
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This corpus is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0).
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Please refer to the respective documentations of the original datasets for information on their licenses:
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https://nlp.stanford.edu/projects/snli/
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https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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### Citation Information
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Labels can be "entailment" if the premise entails the hypothesis, "contradiction" if it contradicts it or "neutral" if it neither implies it nor denies it.
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{
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"gold_label": "neutral",
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"pairID": 1,
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"sentence1": "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos.",
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"sentence2": "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
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}
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### Data Fields
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|
|
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|
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#### Initial Data Collection and Normalization
|
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|
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+
Please refer to the respective documentations of the original datasets:
|
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+
https://nlp.stanford.edu/projects/snli/
|
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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#### Who are the source language producers?
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+
Please refer to the respective documentations of the original datasets:
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+
https://nlp.stanford.edu/projects/snli/
|
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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|
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### Annotations
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#### Annotation process
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+
Please refer to the respective documentations of the original datasets:
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+
https://nlp.stanford.edu/projects/snli/
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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#### Who are the annotators?
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+
Please refer to the respective documentations of the original datasets:
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+
https://nlp.stanford.edu/projects/snli/
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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### Personal and Sensitive Information
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In general, no sensitive information is conveyed in the sentences.
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+
Please refer to the respective documentations of the original datasets:
|
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+
https://nlp.stanford.edu/projects/snli/
|
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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## Considerations for Using the Data
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### Discussion of Biases
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|
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+
Please refer to the respective documentations of the original datasets:
|
158 |
+
https://nlp.stanford.edu/projects/snli/
|
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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### Other Known Limitations
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|
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The translation of the sentences was mostly unsupervised and may introduce some noise in the corpus. Machine translation from an English-language corpus is likely to generate syntactic and lexical forms that differ from those a human Spanish speaker would produce.
|
165 |
+
For discussion on the biases and limitations of the original datasets, please refer to their respective documentations:
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+
https://nlp.stanford.edu/projects/snli/
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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## Additional Information
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### Licensing Information
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This corpus is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0).
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+
Please refer to the respective documentations of the original datasets for information on their licenses:
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+
https://nlp.stanford.edu/projects/snli/
|
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+
https://arxiv.org/pdf/1809.05053.pdf
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https://cims.nyu.edu/~sbowman/multinli/
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### Citation Information
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