Add dataset loading script; Readme skeleton; metadata json

README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+- machine-generated
+- expert-generated
+language:
+- de-cs
+- de-en
+- de-fr
+- en-cs
+- en-de
+- en-fi
+- en-gu
+- en-kk
+- en-lt
+- en-ru
+- en-zh
+- fi-en
+- fr-de
+- gu-en
+- kk-en
+- lt-en
+- ru-en
+- zh-en
+license:
+- unknown
+multilinguality:
+- translation
+paperswithcode_id: null
+pretty_name: WMT19 Metrics Shared Task
+size_categories:
+- 100K<n<1M
+source_datasets: []
+task_categories:
+- translation
+task_ids: []
+---
+
+# Dataset Card for WMT19 Metrics Task
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [WMT19 Metrics Shared Task](https://www.statmt.org/wmt19/metrics-task.html)
+- **Repository:** [MT Metrics Eval Github Repository](https://github.com/google-research/mt-metrics-eval)
+- **Paper:** [Paper](https://aclanthology.org/W19-5302/)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+
+
+The dataset comprises the following language pairs:
+- de-cs
+- de-en
+- de-fr
+- en-cs
+- en-de
+- en-fi
+- en-gu
+- en-kk
+- en-lt
+- en-ru
+- en-zh
+- fi-en
+- fr-de
+- gu-en
+- kk-en
+- lt-en
+- ru-en
+- zh-en
+
+## 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
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@github-username](https://github.com/mustaszewski) for adding this dataset.

dataset_infos.json

@@ -0,0 +1 @@
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We will provide\nyou with all of the translations produced in the translation task along with the human reference translations.\nYou will return your automatic metric scores for translations at the system-level and/or at the sentence-level.\nWe will calculate the system-level and sentence-level correlations of your scores with WMT19 human judgements\nonce the manual evaluation has been completed.\n", "citation": "@inproceedings{ma-etal-2019-results,\n    title = {Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges},\n    author = {Ma, Qingsong and Wei, Johnny and Bojar, Ond\u0159ej and Graham, Yvette},\n    booktitle = {Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)},\n    month = {aug},\n    year = {2019},\n    address = {Florence, Italy},\n    publisher = {Association for Computational Linguistics},\n    url = {https://aclanthology.org/W19-5302},\n    doi = {10.18653/v1/W19-5302},\n    pages = {62--90}\n}\n", "homepage": "https://www.statmt.org/wmt19/metrics-task.html", "license": "Unknown", "features": {"translation": {"languages": ["kk", "en"], "id": null, "_type": "Translation"}, "mt_system": {"dtype": "string", "id": null, "_type": "Value"}, "mqm": {"dtype": "float32", "id": null, "_type": "Value"}, "wmt-raw": {"dtype": "float32", "id": null, "_type": "Value"}, "wmt-z": {"dtype": "float32", "id": null, "_type": "Value"}, "pair": {"dtype": "string", "id": null, "_type": "Value"}, "dataset": {"dtype": "string", "id": null, "_type": "Value"}, "sent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "doc_name": {"dtype": "string", "id": null, "_type": "Value"}, "doc_ref": {"dtype": "string", "id": null, "_type": "Value"}, "ref": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wmt19_metrics_task", "config_name": "kk-en", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6195294, "num_examples": 12000, "dataset_name": "wmt19_metrics_task"}}, "download_checksums": {"https://huggingface.co/datasets/muibk/wmt19_metrics_task/resolve/main/kk-en/train.csv": {"num_bytes": 6076885, "checksum": "ab5280b66c744435f6ac32210aa6b6c855be3b1df4fbf821b76d64389f7f3f2f"}}, "download_size": 6076885, "post_processing_size": null, "dataset_size": 6195294, "size_in_bytes": 12272179}, "lt-en": {"description": "This shared task will examine automatic evaluation metrics for machine translation. We will provide\nyou with all of the translations produced in the translation task along with the human reference translations.\nYou will return your automatic metric scores for translations at the system-level and/or at the sentence-level.\nWe will calculate the system-level and sentence-level correlations of your scores with WMT19 human judgements\nonce the manual evaluation has been completed.\n", "citation": "@inproceedings{ma-etal-2019-results,\n    title = {Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges},\n    author = {Ma, Qingsong and Wei, Johnny and Bojar, Ond\u0159ej and Graham, Yvette},\n    booktitle = {Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)},\n    month = {aug},\n    year = {2019},\n    address = {Florence, Italy},\n    publisher = {Association for Computational Linguistics},\n    url = {https://aclanthology.org/W19-5302},\n    doi = {10.18653/v1/W19-5302},\n    pages = {62--90}\n}\n", "homepage": "https://www.statmt.org/wmt19/metrics-task.html", "license": "Unknown", "features": {"translation": {"languages": ["lt", "en"], "id": null, "_type": "Translation"}, "mt_system": {"dtype": "string", "id": null, "_type": "Value"}, "mqm": {"dtype": "float32", "id": null, "_type": "Value"}, "wmt-raw": {"dtype": "float32", "id": null, "_type": "Value"}, "wmt-z": {"dtype": "float32", "id": null, "_type": "Value"}, "pair": {"dtype": "string", "id": null, "_type": "Value"}, "dataset": {"dtype": "string", "id": null, "_type": "Value"}, "sent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "doc_name": {"dtype": "string", "id": null, "_type": "Value"}, "doc_ref": {"dtype": "string", "id": null, "_type": "Value"}, "ref": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wmt19_metrics_task", "config_name": "lt-en", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6059225, "num_examples": 12000, "dataset_name": "wmt19_metrics_task"}}, "download_checksums": {"https://huggingface.co/datasets/muibk/wmt19_metrics_task/resolve/main/lt-en/train.csv": {"num_bytes": 5936666, "checksum": "7529cdbeca2e7d8b84c905403bd0395666faa7c66903008df92907eed40ecd30"}}, "download_size": 5936666, "post_processing_size": null, "dataset_size": 6059225, "size_in_bytes": 11995891}, "ru-en": {"description": "This shared task will examine automatic evaluation metrics for machine translation. We will provide\nyou with all of the translations produced in the translation task along with the human reference translations.\nYou will return your automatic metric scores for translations at the system-level and/or at the sentence-level.\nWe will calculate the system-level and sentence-level correlations of your scores with WMT19 human judgements\nonce the manual evaluation has been completed.\n", "citation": "@inproceedings{ma-etal-2019-results,\n    title = {Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges},\n    author = {Ma, Qingsong and Wei, Johnny and Bojar, Ond\u0159ej and Graham, Yvette},\n    booktitle = {Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)},\n    month = {aug},\n    year = {2019},\n    address = {Florence, Italy},\n    publisher = {Association for Computational Linguistics},\n    url = {https://aclanthology.org/W19-5302},\n    doi = {10.18653/v1/W19-5302},\n    pages = {62--90}\n}\n", "homepage": "https://www.statmt.org/wmt19/metrics-task.html", "license": "Unknown", "features": {"translation": {"languages": ["ru", "en"], "id": null, "_type": "Translation"}, "mt_system": {"dtype": "string", "id": null, "_type": "Value"}, "mqm": {"dtype": "float32", "id": null, "_type": "Value"}, "wmt-raw": {"dtype": "float32", "id": null, "_type": "Value"}, "wmt-z": {"dtype": "float32", "id": null, "_type": "Value"}, "pair": {"dtype": "string", "id": null, "_type": "Value"}, "dataset": {"dtype": "string", "id": null, "_type": "Value"}, "sent_id": {"dtype": "int32", "id": null, "_type": "Value"}, "doc_name": {"dtype": "string", "id": null, "_type": "Value"}, "doc_ref": {"dtype": "string", "id": null, "_type": "Value"}, "ref": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wmt19_metrics_task", "config_name": "ru-en", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 15401670, "num_examples": 30000, "dataset_name": "wmt19_metrics_task"}}, "download_checksums": {"https://huggingface.co/datasets/muibk/wmt19_metrics_task/resolve/main/ru-en/train.csv": {"num_bytes": 15044780, "checksum": "79e71d2a77be107875c4eeb5aea1174bae654d30792f10b00a36af1e5f44fae6"}}, "download_size": 15044780, "post_processing_size": null, "dataset_size": 15401670, "size_in_bytes": 30446450}, "zh-en": {"description": "This shared task will examine automatic evaluation metrics for machine translation. 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wmt19_metrics_task.py

@@ -0,0 +1,135 @@
+# 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.
+"""WMT19 Metrics Shared Task: Segment-Level Data"""
+
+import datasets
+import csv
+
+_CITATION = """\
+@inproceedings{ma-etal-2019-results,
+    title = {Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges},
+    author = {Ma, Qingsong and Wei, Johnny and Bojar, Ondřej and Graham, Yvette},
+    booktitle = {Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)},
+    month = {aug},
+    year = {2019},
+    address = {Florence, Italy},
+    publisher = {Association for Computational Linguistics},
+    url = {https://aclanthology.org/W19-5302},
+    doi = {10.18653/v1/W19-5302},
+    pages = {62--90}
+}
+"""
+
+_DESCRIPTION = """\
+This shared task will examine automatic evaluation metrics for machine translation. We will provide
+you with all of the translations produced in the translation task along with the human reference translations.
+You will return your automatic metric scores for translations at the system-level and/or at the sentence-level.
+We will calculate the system-level and sentence-level correlations of your scores with WMT19 human judgements
+once the manual evaluation has been completed.
+"""
+
+_HOMEPAGE = "https://www.statmt.org/wmt19/metrics-task.html"
+
+_LICENSE = "Unknown"
+
+
+_LANGUAGE_PAIRS = [('de', 'cs'), ('de', 'en'), ('de', 'fr'), ('en', 'cs'), ('en', 'de'), ('en', 'fi'), ('en', 'gu'), ('en', 'kk'), ('en', 'lt'), ('en', 'ru'), 
+('en', 'zh'), ('fi', 'en'), ('fr', 'de'), ('gu', 'en'), ('kk', 'en'), ('lt', 'en'), ('ru', 'en'), ('zh', 'en')]
+
+_URL_BASE = "https://huggingface.co/datasets/muibk/wmt19_metrics_task/resolve/main/"
+_URLs = {f"{src_lg}-{trg_lg}": f"{_URL_BASE}{src_lg}-{trg_lg}/train.csv" for src_lg, trg_lg in _LANGUAGE_PAIRS}
+
+
+class WmtMetricsTaskConfig(datasets.BuilderConfig):
+    """BuilderConfig for WMT Metrics Shared Task."""
+    def __init__(self, src_lg, tgt_lg, **kwargs):
+        super(WmtMetricsTaskConfig, self).__init__(**kwargs)
+        self.src_lg = src_lg
+        self.tgt_lg = tgt_lg
+
+
+class Wmt19MetricsTask(datasets.GeneratorBasedBuilder):
+    """WMT Metrics Shared Task."""
+
+    BUILDER_CONFIGS = [
+        WmtMetricsTaskConfig(
+            name=f"{src_lg}-{tgt_lg}",
+            version=datasets.Version("1.1.0"),
+            description=f"WMT 2019 Metrics Task: {src_lg} - {tgt_lg}",
+            src_lg=src_lg,
+            tgt_lg=tgt_lg,
+        )
+        for (src_lg, tgt_lg) in _LANGUAGE_PAIRS
+    ]
+    BUILDER_CONFIG_CLASS = WmtMetricsTaskConfig
+
+    def _info(self):
+        # define feature types
+        features = datasets.Features(
+            {
+                #'source' : datasets.Value("string"),
+                #'system_output':datasets.Value("string"),
+                'translation': datasets.Translation(languages=(self.config.src_lg, self.config.tgt_lg)),
+                'mt_system':datasets.Value("string"),
+                'mqm':datasets.Value("float32"),
+                'wmt-raw':datasets.Value("float32"),
+                'wmt-z':datasets.Value("float32"),
+                'pair':datasets.Value("string"),
+                'dataset':datasets.Value("string"),
+                'sent_id':datasets.Value("int32"),
+                'doc_name':datasets.Value("string"),
+                'doc_ref':datasets.Value("string"),
+                'ref':datasets.Value("string")
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        pair = f"{self.config.src_lg}-{self.config.tgt_lg}" # string identifier for language pair
+        url = _URLs[pair] # url for download of pair-specific train.csv
+        data_file = dl_manager.download_and_extract(url) # extract downloaded data and store path in data_file
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_file,
+                    "source_lg": self.config.src_lg,
+                    "target_lg": self.config.tgt_lg,
+                }
+            )
+        ]
+    
+    def _generate_examples(self, filepath, source_lg, target_lg):
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter=";") # read each line into dict
+            for id_, row in enumerate(reader):
+                row["translation"] = {source_lg : row["source"], target_lg: row["system_output"]} # create translation json
+                for key in ["source", "system_output"]: # remove obsolete columns
+                    row.pop(key)
+                row = {k: None if not v else v for k, v in row.items()} # replace empty values
+                yield id_, row
+
+# to test the script, go to the root folder of the repo (wmt19_metrics_task) and run:
+# datasets-cli test ./wmt19_metrics_task.py --save_infos --all_configs