Update files from the datasets library (from 1.17.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.17.0

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,343 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- ay
+- bzd
+- cni
+- gn
+- hch
+- nah
+- oto
+- qu
+- shp
+- tar
+licenses:
+- unknown
+multilinguality:
+- multilingual
+- translation
+pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages-'
+size_categories:
+- unknown
+source_datasets:
+- extended|xnli
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+---
+
+# Dataset Card for AmericasNLI
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [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)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/nala-cub/AmericasNLI
+- **Paper:** https://arxiv.org/abs/2104.08726
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).
+
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+- aym
+- bzd
+- cni
+- gn
+- hch
+- nah
+- oto
+- quy
+- shp
+- tar
+
+## Dataset Structure
+
+### Data Instances
+
+#### all_languages
+
+An example of the test split looks as follows:
+
+```
+{'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p
+arlxapxti.', 'label': 2}
+```
+
+#### aym
+
+An example of the test split looks as follows:
+
+```
+{'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label
+': 2}
+```
+
+#### bzd
+
+An example of the test split looks as follows:
+
+```
+{'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2}
+```
+
+#### cni
+
+An example of the test split looks as follows:
+
+```
+{'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2}
+```
+
+#### gn
+
+An example of the test split looks as follows:
+
+```
+{'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2}
+```
+
+#### hch
+
+An example of the test split looks as follows:
+
+```
+{'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2}
+```
+
+#### nah
+
+An example of the test split looks as follows:
+
+```
+{'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2}
+```
+
+#### oto
+
+An example of the test split looks as follows:
+
+```
+{'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2}
+```
+
+#### quy
+
+An example of the test split looks as follows:
+
+``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani
+.', 'label': 2}
+```
+
+#### shp
+
+An example of the test split looks as follows:
+
+```
+{'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2}
+```
+
+#### tar
+
+An example of the test split looks as follows:
+
+```
+{'premise': 'Ga’lá ju, ke tási newalayé nejé echi k��tira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2}
+```
+
+### Data Fields
+
+#### all_languages
+    - language: a multilingual string variable, with languages including ar, bg, de, el, en.
+    - premise: a multilingual string variable, with languages including ar, bg, de, el, en.
+    - hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### aym
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### bzd
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### cni
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### hch
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### nah
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### oto
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### quy
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### shp
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+#### tar
+    - premise: a string feature.
+    - hypothesis: a string feature.
+    - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
+
+### Data Splits
+
+| Language          | ISO | Family       | Dev  | Test |
+|-------------------|-----|:-------------|-----:|-----:|
+| all_languages     | --  | --           | 6457 | 7486 |
+| Aymara            | aym | Aymaran      | 743  | 750  |
+| Ashaninka         | cni | Arawak       | 658  | 750  |
+| Bribri            | bzd | Chibchan     | 743  | 750  |
+| Guarani           | gn  | Tupi-Guarani | 743  | 750  |
+| Nahuatl           | nah | Uto-Aztecan  | 376  | 738  |
+| Otomi             | oto | Oto-Manguean | 222  | 748  |
+| Quechua           | quy | Quechuan     | 743  | 750  |
+| Raramuri          | tar | Uto-Aztecan  | 743  | 750  |
+| Shipibo-Konibo    | shp | Panoan       | 743  | 750  |
+| Wixarika          | hch | Uto-Aztecan  | 743  | 750  |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+The authors translate from the Spanish subset of XNLI.
+
+> AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version.
+
+As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726).
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+The dataset comprises expert translations from Spanish XNLI. 
+
+> Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.”
+
+As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726).
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+```
+@article{DBLP:journals/corr/abs-2104-08726,
+  author    = {Abteen Ebrahimi and
+               Manuel Mager and
+               Arturo Oncevay and
+               Vishrav Chaudhary and
+               Luis Chiruzzo and
+               Angela Fan and
+               John Ortega and
+               Ricardo Ramos and
+               Annette Rios and
+               Ivan Vladimir and
+               Gustavo A. Gim{\'{e}}nez{-}Lugo and
+               Elisabeth Mager and
+               Graham Neubig and
+               Alexis Palmer and
+               Rolando A. Coto Solano and
+               Ngoc Thang Vu and
+               Katharina Kann},
+  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of
+               Pretrained Multilingual Models in Truly Low-resource Languages},
+  journal   = {CoRR},
+  volume    = {abs/2104.08726},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2104.08726},
+  eprinttype = {arXiv},
+  eprint    = {2104.08726},
+  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+
+Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset.

americas_nli.py

@@ -0,0 +1,178 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages."""
+
+
+import csv
+
+import datasets
+from datasets.utils.download_manager import DownloadManager
+
+
+_CITATION = """
+@article{DBLP:journals/corr/abs-2104-08726,
+  author    = {Abteen Ebrahimi and
+               Manuel Mager and
+               Arturo Oncevay and
+               Vishrav Chaudhary and
+               Luis Chiruzzo and
+               Angela Fan and
+               John Ortega and
+               Ricardo Ramos and
+               Annette Rios and
+               Ivan Vladimir and
+               Gustavo A. Gim{\'{e}}nez{-}Lugo and
+               Elisabeth Mager and
+               Graham Neubig and
+               Alexis Palmer and
+               Rolando A. Coto Solano and
+               Ngoc Thang Vu and
+               Katharina Kann},
+  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of
+               Pretrained Multilingual Models in Truly Low-resource Languages},
+  journal   = {CoRR},
+  volume    = {abs/2104.08726},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2104.08726},
+  eprinttype = {arXiv},
+  eprint    = {2104.08726},
+  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """\
+AmericasNLI is an extension of XNLI (Conneau et al., 2018) – a natural language inference (NLI) dataset covering 15 high-resource languages – to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).
+"""
+
+VERSION = datasets.Version("1.0.0", "")
+_DEV_DATA_URL = "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv"
+_TEST_DATA_URL = "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv"
+
+_LANGUAGES = ("aym", "bzd", "cni", "gn", "hch", "nah", "oto", "quy", "shp", "tar")
+
+
+class AmericasNLIConfig(datasets.BuilderConfig):
+    """BuilderConfig for AmericasNLI."""
+
+    def __init__(self, language: str, languages=None, **kwargs):
+        """BuilderConfig for AmericasNLI.
+
+        Args:
+        language: One of aym, bzd, cni, gn, hch, nah, oto, quy, shp, tar or all_languages
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(AmericasNLIConfig, self).__init__(**kwargs)
+        self.language = language
+        if language != "all_languages":
+            self.languages = [language]
+        else:
+            self.languages = languages if languages is not None else _LANGUAGES
+
+
+class AmericasNLI(datasets.GeneratorBasedBuilder):
+    """TODO"""
+
+    VERSION = VERSION
+    BUILDER_CONFIG_CLASS = AmericasNLIConfig
+    BUILDER_CONFIGS = [
+        AmericasNLIConfig(
+            name=lang,
+            language=lang,
+            version=VERSION,
+            description=f"Plain text import of AmericasNLI for the {lang} language",
+        )
+        for lang in _LANGUAGES
+    ] + [
+        AmericasNLIConfig(
+            name="all_languages",
+            language="all_languages",
+            version=VERSION,
+            description="Plain text import of AmericasNLI for all languages",
+        )
+    ]
+
+    def _info(self):
+        if self.config.language == "all_languages":
+            features = datasets.Features(
+                {
+                    "language": datasets.Value("string"),
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            # No default supervised_keys (as we have to pass both premise
+            # and hypothesis as input).
+            supervised_keys=None,
+            homepage="https://github.com/nala-cub/AmericasNLI",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        dl_paths = dl_manager.download(
+            {
+                "dev_data": _DEV_DATA_URL,
+                "test_data": _TEST_DATA_URL,
+            }
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": dl_paths["dev_data"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": dl_paths["test_data"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: str):
+        """This function returns the examples in the raw (text) form."""
+        idx = 0
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for row in reader:
+                if row["language"] == self.config.language:
+                    yield idx, {
+                        "premise": row["premise"],
+                        "hypothesis": row["hypothesis"],
+                        "label": row["label"],
+                    }
+                    idx += 1
+                elif self.config.language == "all_languages":
+                    yield idx, {
+                        "language": row["language"],
+                        "premise": row["premise"],
+                        "hypothesis": row["hypothesis"],
+                        "label": row["label"],
+                    }
+                    idx += 1

dataset_infos.json

@@ -0,0 +1 @@
+{"aym": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "aym", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 117538, "num_examples": 743, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 115259, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 232797, "size_in_bytes": 2488890}, "bzd": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "bzd", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 143362, "num_examples": 743, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 127684, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 271046, "size_in_bytes": 2527139}, "cni": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "cni", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 113264, "num_examples": 658, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 116292, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 229556, "size_in_bytes": 2485649}, "gn": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "gn", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 115143, "num_examples": 743, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 101956, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 217099, "size_in_bytes": 2473192}, "hch": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "hch", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 127974, "num_examples": 743, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 120865, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 248839, "size_in_bytes": 2504932}, "nah": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "nah", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 50749, "num_examples": 376, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 102961, "num_examples": 738, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 153710, "size_in_bytes": 2409803}, "oto": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "oto", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 27018, "num_examples": 222, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 119658, "num_examples": 748, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 146676, "size_in_bytes": 2402769}, "quy": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "quy", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 125644, "num_examples": 743, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 112758, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 238402, "size_in_bytes": 2494495}, "shp": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "shp", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 124508, "num_examples": 743, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 118942, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 243450, "size_in_bytes": 2499543}, "tar": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "tar", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 139504, "num_examples": 743, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 122632, "num_examples": 750, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 262136, "size_in_bytes": 2518229}, "all_languages": {"description": "AmericasNLI is an extension of XNLI (Conneau et al., 2018) \u2013 a natural language inference (NLI) dataset covering 15 high-resource languages \u2013 to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).\n", "citation": "\n@article{DBLP:journals/corr/abs-2104-08726,\n  author    = {Abteen Ebrahimi and\n               Manuel Mager and\n               Arturo Oncevay and\n               Vishrav Chaudhary and\n               Luis Chiruzzo and\n               Angela Fan and\n               John Ortega and\n               Ricardo Ramos and\n               Annette Rios and\n               Ivan Vladimir and\n               Gustavo A. Gim{'{e}}nez{-}Lugo and\n               Elisabeth Mager and\n               Graham Neubig and\n               Alexis Palmer and\n               Rolando A. Coto Solano and\n               Ngoc Thang Vu and\n               Katharina Kann},\n  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of\n               Pretrained Multilingual Models in Truly Low-resource Languages},\n  journal   = {CoRR},\n  volume    = {abs/2104.08726},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.08726},\n  eprinttype = {arXiv},\n  eprint    = {2104.08726},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "TODO", "license": "", "features": {"language": {"dtype": "string", "id": null, "_type": "Value"}, "premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "americas_nli", "config_name": "all_languages", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"validation": {"name": "validation", "num_bytes": 1129092, "num_examples": 6457, "dataset_name": "americas_nli"}, "test": {"name": "test", "num_bytes": 1210591, "num_examples": 7486, "dataset_name": "americas_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/dev.tsv": {"num_bytes": 1090405, "checksum": "a2678f2820a2008547c5d993118979cc82a25d51a73570571566a1b74d8e8530"}, "https://raw.githubusercontent.com/nala-cub/AmericasNLI/main/test.tsv": {"num_bytes": 1165688, "checksum": "1e16c058de33ddaab4a037b1078a31ecab08afddfdc840140593b6da1439bcf8"}}, "download_size": 2256093, "post_processing_size": null, "dataset_size": 2339683, "size_in_bytes": 4595776}}

dummy/all_languages/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89cf19b1872bdcdcd3d1f135830a810e14e3a78d7343f321cfb4bfbb73f8236a
+size 934

dummy/aym/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dce034947b7c61eca33bb9bf0cdcf8eaf50459f7d955db4364927972d16483ec
+size 934

dummy/bzd/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93bd2f55d85c5c231d7367fe6a2069a64f6cc78f87dad4f72a43540b3f7e2a09
+size 1000

dummy/cni/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4f6fd74d817f2b43c85f5ce06887bc92d1e2f0199e3443e90ecac9026dec0e4
+size 887

dummy/gn/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e5f805e2054bffb86fb60492c1b428b6a610c9450179d6dabaaf87b41654ddc
+size 930

dummy/hch/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da9eb203d0e002647cf692594b920796434dfc2703552e377ea8f20cbd72d2de
+size 795

dummy/nah/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f48caa1102ca952b877954d3597394ab100b13edc425e43f3ad724c3eec244ed
+size 825

dummy/oto/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8582492fdd07bcbd088b3979aa5b35c011f18e2fa243ad1ca21e8b317e481c1
+size 958

dummy/quy/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f852ce0734e803a39a576898a45769577c571ce2b7c4c5b7b93bd53e957747f9
+size 917

dummy/shp/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ce22ced3464fd18149397d25fa94416a7fe92ebb09b656da4e3caf1ec5e84c3
+size 853

dummy/tar/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:060a051563726fbab44bebbb1f362a59cc82dd6a52e74edafc880373791e079c
+size 924