Update README.md

GAND encompasses gender ambiguous (w.r.t. a specific referent) natural data and is a benchmarking resource for evaluating gender in machine translation.

Dataset split:
train: 4037 examples
validation: 505 examples
test: 505 examples

To build GAND, data from two different sources were used: C4 (Common Crawl) and Open Subtitles.
- C4: We used the en subset from the allenai/c4 dataset, which is available on Hugging Face. C4 contains 364,868,892 texts (with 456 tokens per text on average in the first 1,000 texts of the randomly shuffled dataset).
- Open Subtitles: We used the monolingual English data from the Open Subtitles corpus, compiled within the OPUS project. The Open Subtitles corpus contains 2,739,528 texts (in this case, a text corresponds to a single subtitle; 8 tokens per subtitle on average in the first 1,000 subtitles of the randomly shuffled dataset).

More information on the construction of GAND is available on this GitHub repository: https://github.com/jhacken/GAND/

README.md

@@ -3,9 +3,11 @@ license: odc-by
 language:
 - en
 tags:
-- translation
-- nlp
+- gender
+- ambiguity
 pretty_name: 'GAND: Gender Ambiguous Natural Data'
 size_categories:
 - 1K<n<10K
+task_categories:
+- translation
 ---