Update parquet files

README.md

@@ -1,247 +0,0 @@
----
-pretty_name: TEDMulti
-paperswithcode_id: null
-dataset_info:
-  features:
-  - name: translations
-    dtype:
-      translation_variable_languages:
-        languages:
-        - ar
-        - az
-        - be
-        - bg
-        - bn
-        - bs
-        - calv
-        - cs
-        - da
-        - de
-        - el
-        - en
-        - eo
-        - es
-        - et
-        - eu
-        - fa
-        - fi
-        - fr
-        - fr-ca
-        - gl
-        - he
-        - hi
-        - hr
-        - hu
-        - hy
-        - id
-        - it
-        - ja
-        - ka
-        - kk
-        - ko
-        - ku
-        - lt
-        - mk
-        - mn
-        - mr
-        - ms
-        - my
-        - nb
-        - nl
-        - pl
-        - pt
-        - pt-br
-        - ro
-        - ru
-        - sk
-        - sl
-        - sq
-        - sr
-        - sv
-        - ta
-        - th
-        - tr
-        - uk
-        - ur
-        - vi
-        - zh
-        - zh-cn
-        - zh-tw
-        num_languages: 60
-  - name: talk_name
-    dtype: string
-  config_name: plain_text
-  splits:
-  - name: test
-    num_bytes: 23364983
-    num_examples: 7213
-  - name: train
-    num_bytes: 748209995
-    num_examples: 258098
-  - name: validation
-    num_bytes: 19435383
-    num_examples: 6049
-  download_size: 352222045
-  dataset_size: 791010361
----
-
-# Dataset Card for "ted_multi"
-
-## 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:** [https://github.com/neulab/word-embeddings-for-nmt](https://github.com/neulab/word-embeddings-for-nmt)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 335.91 MB
-- **Size of the generated dataset:** 754.37 MB
-- **Total amount of disk used:** 1090.27 MB
-
-### Dataset Summary
-
-Massively multilingual (60 language) data set derived from TED Talk transcripts.
-Each record consists of parallel arrays of language and text. Missing and
-incomplete translations will be filtered out.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### plain_text
-
-- **Size of downloaded dataset files:** 335.91 MB
-- **Size of the generated dataset:** 754.37 MB
-- **Total amount of disk used:** 1090.27 MB
-
-An example of 'validation' looks as follows.
-```
-This example was too long and was cropped:
-
-{
-    "talk_name": "shabana_basij_rasikh_dare_to_educate_afghan_girls",
-    "translations": "{\"language\": [\"ar\", \"az\", \"bg\", \"bn\", \"cs\", \"da\", \"de\", \"el\", \"en\", \"es\", \"fa\", \"fr\", \"he\", \"hi\", \"hr\", \"hu\", \"hy\", \"id\", \"it\", ..."
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### plain_text
-- `translations`: a multilingual `string` variable, with possible languages including `ar`, `az`, `be`, `bg`, `bn`.
-- `talk_name`: a `string` feature.
-
-### Data Splits
-
-|   name   |train |validation|test|
-|----------|-----:|---------:|---:|
-|plain_text|258098|      6049|7213|
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@InProceedings{qi-EtAl:2018:N18-2,
-  author    = {Qi, Ye  and  Sachan, Devendra  and  Felix, Matthieu  and  Padmanabhan, Sarguna  and  Neubig, Graham},
-  title     = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
-  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
-  month     = {June},
-  year      = {2018},
-  address   = {New Orleans, Louisiana},
-  publisher = {Association for Computational Linguistics},
-  pages     = {529--535},
-  abstract  = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},
-  url       = {http://www.aclweb.org/anthology/N18-2084}
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"plain_text": {"description": "Massively multilingual (60 language) data set derived from TED Talk transcripts.\nEach record consists of parallel arrays of language and text. Missing and\nincomplete translations will be filtered out.\n", "citation": "@InProceedings{qi-EtAl:2018:N18-2,\n  author    = {Qi, Ye  and  Sachan, Devendra  and  Felix, Matthieu  and  Padmanabhan, Sarguna  and  Neubig, Graham},\n  title     = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},\n  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},\n  month     = {June},\n  year      = {2018},\n  address   = {New Orleans, Louisiana},\n  publisher = {Association for Computational Linguistics},\n  pages     = {529--535},\n  abstract  = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},\n  url       = {http://www.aclweb.org/anthology/N18-2084}\n}\n", "homepage": "https://github.com/neulab/word-embeddings-for-nmt", "license": "", "features": {"translations": {"languages": ["ar", "az", "be", "bg", "bn", "bs", "calv", "cs", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fr-ca", "gl", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "kk", "ko", "ku", "lt", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "pt-br", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "ta", "th", "tr", "uk", "ur", "vi", "zh", "zh-cn", "zh-tw"], "num_languages": 60, "id": null, "_type": "TranslationVariableLanguages"}, "talk_name": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "ted_multi_translate", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 23364983, "num_examples": 7213, "dataset_name": "ted_multi_translate"}, "train": {"name": "train", "num_bytes": 748209995, "num_examples": 258098, "dataset_name": "ted_multi_translate"}, "validation": {"name": "validation", "num_bytes": 19435383, "num_examples": 6049, "dataset_name": "ted_multi_translate"}}, "download_checksums": {"http://phontron.com/data/ted_talks.tar.gz": {"num_bytes": 352222045, "checksum": "03457b9ebc6d60839f1a48c5a03c940266aff78b81fcda4c6d9e2a5a7fb670ae"}}, "download_size": 352222045, "dataset_size": 791010361, "size_in_bytes": 1143232406}}

plain_text/ted_multi-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d69f8bf5494304cf9186da09e8a1e3a0ee3634171f82f2af05e1693136f53223
+size 15400437

plain_text/ted_multi-train-00000-of-00002.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f9b75e40cc5d888d11f37956e072eacf4878d978eb91ddcd66fdce905f50231
+size 334377927

plain_text/ted_multi-train-00001-of-00002.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94cbd7a2533ef3d25b54f8028f4e4dae8d8eb605e64fed196f88c554cafb57ee
+size 164032406

plain_text/ted_multi-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:882dac5ac3a03d56881dd17764e165581c68907320f841400c274cced118ee89
+size 12877695

ted_multi.py

@@ -1,184 +0,0 @@
-# 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
-"""TED talk multilingual data set."""
-
-import csv
-
-import datasets
-
-
-_DESCRIPTION = """\
-Massively multilingual (60 language) data set derived from TED Talk transcripts.
-Each record consists of parallel arrays of language and text. Missing and
-incomplete translations will be filtered out.
-"""
-
-_CITATION = """\
-@InProceedings{qi-EtAl:2018:N18-2,
-  author    = {Qi, Ye  and  Sachan, Devendra  and  Felix, Matthieu  and  Padmanabhan, Sarguna  and  Neubig, Graham},
-  title     = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
-  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
-  month     = {June},
-  year      = {2018},
-  address   = {New Orleans, Louisiana},
-  publisher = {Association for Computational Linguistics},
-  pages     = {529--535},
-  abstract  = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},
-  url       = {http://www.aclweb.org/anthology/N18-2084}
-}
-"""
-
-_DATA_URL = "http://phontron.com/data/ted_talks.tar.gz"
-
-_LANGUAGES = (
-    "en",
-    "es",
-    "pt-br",
-    "fr",
-    "ru",
-    "he",
-    "ar",
-    "ko",
-    "zh-cn",
-    "it",
-    "ja",
-    "zh-tw",
-    "nl",
-    "ro",
-    "tr",
-    "de",
-    "vi",
-    "pl",
-    "pt",
-    "bg",
-    "el",
-    "fa",
-    "sr",
-    "hu",
-    "hr",
-    "uk",
-    "cs",
-    "id",
-    "th",
-    "sv",
-    "sk",
-    "sq",
-    "lt",
-    "da",
-    "calv",
-    "my",
-    "sl",
-    "mk",
-    "fr-ca",
-    "fi",
-    "hy",
-    "hi",
-    "nb",
-    "ka",
-    "mn",
-    "et",
-    "ku",
-    "gl",
-    "mr",
-    "zh",
-    "ur",
-    "eo",
-    "ms",
-    "az",
-    "ta",
-    "bn",
-    "kk",
-    "be",
-    "eu",
-    "bs",
-)
-
-
-class TedMultiTranslate(datasets.GeneratorBasedBuilder):
-    """TED talk multilingual data set."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of multilingual TED talk translations",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "translations": datasets.features.TranslationVariableLanguages(languages=_LANGUAGES),
-                    "talk_name": datasets.Value("string"),
-                }
-            ),
-            homepage="https://github.com/neulab/word-embeddings-for-nmt",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive = dl_manager.download(_DATA_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_file": "all_talks_train.tsv",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "data_file": "all_talks_dev.tsv",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "data_file": "all_talks_test.tsv",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_file, files):
-        """This function returns the examples in the raw (text) form."""
-        for path, f in files:
-            if path == data_file:
-                lines = (line.decode("utf-8") for line in f)
-                reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
-                for idx, row in enumerate(reader):
-                    # Everything in the row except for 'talk_name' will be a translation.
-                    # Missing/incomplete translations will contain the string "__NULL__" or
-                    # "_ _ NULL _ _".
-                    yield idx, {
-                        "translations": {
-                            lang: text
-                            for lang, text in row.items()
-                            if lang != "talk_name" and _is_translation_complete(text)
-                        },
-                        "talk_name": row["talk_name"],
-                    }
-                break
-
-
-def _is_translation_complete(text):
-    return text and "__NULL__" not in text and "_ _ NULL _ _" not in text