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+ {
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+ "description": "Translation dataset based on the data from statmt.org.\n\nVersions exist for different years using a combination of data\nsources. The base `wmt` allows you to create a custom dataset by choosing\nyour own data/language pair. This can be done as follows:\n\n```python\nfrom datasets import inspect_dataset, load_dataset_builder\n\ninspect_dataset(\"wmt16\", \"path/to/scripts\")\nbuilder = load_dataset_builder(\n \"path/to/scripts/wmt_utils.py\",\n language_pair=(\"fr\", \"de\"),\n subsets={\n datasets.Split.TRAIN: [\"commoncrawl_frde\"],\n datasets.Split.VALIDATION: [\"euelections_dev2019\"],\n },\n)\n\n# Standard version\nbuilder.download_and_prepare()\nds = builder.as_dataset()\n\n# Streamable version\nds = builder.as_streaming_dataset()\n```\n\n",
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+ "citation": "\n@inproceedings{mager-etal-2021-findings,\n title = \"Findings of the {A}mericas{NLP} 2021 Shared Task on Open Machine Translation for Indigenous Languages of the {A}mericas\",\n author = \"Mager, Manuel and\n Oncevay, Arturo and\n Ebrahimi, Abteen and\n Ortega, John and\n Rios, Annette and\n Fan, Angela and\n Gutierrez-Vasques, Ximena and\n Chiruzzo, Luis and\n Gim{'e}nez-Lugo, Gustavo and\n Ramos, Ricardo and\n Meza Ruiz, Ivan Vladimir and\n Coto-Solano, Rolando and\n Palmer, Alexis and\n Mager-Hois, Elisabeth and\n Chaudhary, Vishrav and\n Neubig, Graham and\n Vu, Ngoc Thang and\n Kann, Katharina\",\n booktitle = \"Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas\",\n month = jun,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.americasnlp-1.23\",\n doi = \"10.18653/v1/2021.americasnlp-1.23\",\n pages = \"202--217\",\n abstract = \"This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.\",\n}\n",
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+ "homepage": "https://turing.iimas.unam.mx/americasnlp/2021/st.html",
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+ "license": "",
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+ "features": {
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+ "translation": {
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+ "languages": [
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+ "quy",
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+ "es"
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+ ],
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+ "_type": "Translation"
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+ }
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+ },
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+ "supervised_keys": {
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+ "input": "quy",
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+ "output": "es"
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+ },
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+ "builder_name": "americasnlp-mt-21",
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+ "config_name": "quy-es",
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+ "version": {
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+ "version_str": "1.0.0",
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+ },
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+ "train": {
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+ "name": "train",
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+ "num_examples": 121064,
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+ "dataset_name": "americasnlp-mt-21"
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+ },
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+ "name": "validation",
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+ "dataset_name": "americasnlp-mt-21"
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+ },
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+ "name": "test",
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+ "num_bytes": 122691,
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+ "num_examples": 1003,
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+ "dataset_name": "americasnlp-mt-21"
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+ }
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+ },
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+ "download_checksums": {
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+ }