Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+  monolingual:
+  - no-annotation
+  monolingual_raw:
+  - found
+  parallel:
+  - expert-generated
+  parallel_raw:
+  - expert-generated
+language_creators:
+- found
+languages:
+  monolingual:
+  - chr
+  - en
+  monolingual_raw:
+  - chr
+  parallel:
+  - chr
+  - en
+  parallel_raw:
+  - chr
+  - en
+licenses:
+- other-different-license-per-source
+multilinguality:
+  monolingual:
+  - multilingual
+  monolingual_raw:
+  - monolingual
+  parallel:
+  - translation
+  parallel_raw:
+  - translation
+size_categories:
+  monolingual:
+  - 100K<n<1M
+  monolingual_raw:
+  - 1K<n<10K
+  parallel:
+  - 10K<n<100K
+  parallel_raw:
+  - 10K<n<100K
+source_datasets:
+- original
+task_categories:
+  monolingual:
+  - conditional-text-generation
+  monolingual_raw:
+  - sequence-modeling
+  parallel:
+  - conditional-text-generation
+  parallel_raw:
+  - conditional-text-generation
+task_ids:
+  monolingual:
+  - machine-translation
+  monolingual_raw:
+  - language-modeling
+  parallel:
+  - machine-translation
+  parallel_raw:
+  - machine-translation
+---
+
+# Dataset Card for ChrEn
+
+## 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
+
+- **Repository:** [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn)
+- **Paper:** [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791)
+- **Point of Contact:** [benfrey@email.unc.edu](benfrey@email.unc.edu)
+
+### Dataset Summary
+
+ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
+ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
+ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.
+
+### Supported Tasks and Leaderboards
+
+The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`).
+
+### Languages
+
+The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC).
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill.
+
+### Licensing Information
+
+The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions).
+
+### Citation Information
+
+```
+@inproceedings{zhang2020chren,
+  title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
+  author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
+  booktitle={EMNLP2020},
+  year={2020}
+}
+```

chr_en.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""ChrEn: Cherokee-English Machine Translation data"""
+
+from __future__ import absolute_import, division, print_function
+
+import openpyxl  # noqa: requires this pandas optional dependency for reading xlsx files
+import pandas as pd
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{zhang2020chren,
+  title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
+  author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
+  booktitle={EMNLP2020},
+  year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
+ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
+ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.
+"""
+
+_HOMEPAGE = "https://github.com/ZhangShiyue/ChrEn"
+
+_LICENSE = ""
+
+_URLs = {
+    "monolingual_raw": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/raw/monolingual_data.xlsx",
+    "parallel_raw": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/raw/parallel_data.xlsx",
+    "monolingual_chr": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/chr",
+    "monolingual_en5000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en5000",
+    "monolingual_en10000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en10000",
+    "monolingual_en20000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en20000",
+    "monolingual_en50000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en50000",
+    "monolingual_en100000": "https://raw.githubusercontent.com/ZhangShiyue/ChrEn/main/data/monolingual/en100000",
+    "parallel_train.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/train.chr",
+    "parallel_train.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/train.en",
+    "parallel_dev.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/dev.chr",
+    "parallel_dev.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/dev.en",
+    "parallel_out_dev.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_dev.chr",
+    "parallel_out_dev.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_dev.en",
+    "parallel_test.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/test.chr",
+    "parallel_test.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/test.en",
+    "parallel_out_test.chr": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_test.chr",
+    "parallel_out_test.en": "https://github.com/ZhangShiyue/ChrEn/raw/main/data/parallel/out_test.en",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class ChrEn(datasets.GeneratorBasedBuilder):
+    """ChrEn: Cherokee-English Machine Translation data."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="monolingual_raw", version=VERSION, description="Monolingual data with metadata"),
+        datasets.BuilderConfig(name="parallel_raw", version=VERSION, description="Parallel data with metadata"),
+        datasets.BuilderConfig(name="monolingual", version=VERSION, description="Monolingual data text only"),
+        datasets.BuilderConfig(
+            name="parallel", version=VERSION, description="Parallel data text pairs only with default split"
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = (
+        "parallel"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
+
+    def _info(self):
+        if (
+            self.config.name == "monolingual_raw"
+        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "text_sentence": datasets.Value("string"),
+                    "text_title": datasets.Value("string"),
+                    "speaker": datasets.Value("string"),
+                    "date": datasets.Value("int32"),
+                    "type": datasets.Value("string"),
+                    "dialect": datasets.Value("string"),
+                }
+            )
+        elif (
+            self.config.name == "parallel_raw"
+        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "line_number": datasets.Value("string"),  # doesn't always map to a number
+                    "sentence_pair": datasets.Translation(languages=["en", "chr"]),
+                    "text_title": datasets.Value("string"),
+                    "speaker": datasets.Value("string"),
+                    "date": datasets.Value("int32"),
+                    "type": datasets.Value("string"),
+                    "dialect": datasets.Value("string"),
+                }
+            )
+        elif (
+            self.config.name == "parallel"
+        ):  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features(
+                {
+                    "sentence_pair": datasets.Translation(languages=["en", "chr"]),
+                }
+            )
+        elif (
+            self.config.name == "monolingual"
+        ):  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  # Here we define them above because they are different between the two configurations
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download(_URLs)
+        if self.config.name in [
+            "monolingual_raw",
+            "parallel_raw",
+        ]:  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            return [
+                datasets.SplitGenerator(
+                    name="full",
+                    gen_kwargs={
+                        "filepaths": data_dir,
+                        "split": "full",
+                    },
+                )
+            ]
+        elif self.config.name == "monolingual":
+            return [
+                datasets.SplitGenerator(
+                    name=spl,
+                    gen_kwargs={
+                        "filepaths": data_dir,
+                        "split": spl,
+                    },
+                )
+                for spl in ["chr", "en5000", "en10000", "en20000", "en50000", "en100000"]
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=spl,
+                    gen_kwargs={
+                        "filepaths": data_dir,
+                        "split": spl,
+                    },
+                )
+                for spl in ["train", "dev", "out_dev", "test", "out_test"]
+            ]
+
+    def _generate_examples(self, filepaths, split):
+        if self.config.name == "monolingual_raw":
+            keys = ["text_sentence", "text_title", "speaker", "date", "type", "dialect"]
+            with open(filepaths["monolingual_raw"], "rb") as f:
+                monolingual = pd.read_excel(f, engine="openpyxl")
+                for id_, row in enumerate(monolingual.itertuples()):
+                    yield id_, dict(zip(keys, row[1:]))
+        elif self.config.name == "parallel_raw":
+            keys = ["line_number", "en_sent", "chr_sent", "text_title", "speaker", "date", "type", "dialect"]
+            with open(filepaths["parallel_raw"], "rb") as f:
+                parallel = pd.read_excel(f, engine="openpyxl")
+                for id_, row in enumerate(parallel.itertuples()):
+                    res = dict(zip(keys, row[1:]))
+                    res["sentence_pair"] = {"en": res["en_sent"], "chr": res["chr_sent"]}
+                    res["line_number"] = str(res["line_number"])
+                    del res["en_sent"]
+                    del res["chr_sent"]
+                    yield id_, res
+        elif self.config.name == "monolingual":
+            f = open(filepaths[f"monolingual_{split}"], encoding="utf-8")
+            for id_, line in enumerate(f):
+                yield id_, {"sentence": line.strip()}
+        elif self.config.name == "parallel":
+            fi = open(filepaths[f"parallel_{split}.en"], encoding="utf-8")
+            fo = open(filepaths[f"parallel_{split}.chr"], encoding="utf-8")
+            for id_, (line_en, line_chr) in enumerate(zip(fi, fo)):
+                yield id_, {"sentence_pair": {"en": line_en.strip(), "chr": line_chr.strip()}}

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