add test script

nllb.py

@@ -0,0 +1,144 @@
+# 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.
+"""No Language Left Behind (NLLB)"""
+
+import datasets
+import csv
+import json
+
+_CITATION = ""  # TODO
+
+
+_DESCRIPTION = ""
+
+_HOMEPAGE = (
+    ""
+)
+
+_LICENSE = (
+    ""
+)
+
+with open("https://huggingface.co/datasets/allenai/nllb/resolve/main/all_lang_pairs.json") as f:
+    _LANGUAGE_PAIRS = json.load(f)
+
+_LANGUAGE_PAIRS = _LANGUAGE_PAIRS[:2]
+
+_URL_BASE = "https://storage.googleapis.com/allennlp-data-bucket/nllb/"
+
+_URLs = {
+    f"{src_lg}-{trg_lg}": f"{_URL_BASE}{src_lg}-{trg_lg}.gz"
+    for src_lg, trg_lg in _LANGUAGE_PAIRS
+}
+
+
+class NLLBTaskConfig(datasets.BuilderConfig):
+    """BuilderConfig for No Language Left Behind Dataset."""
+
+    def __init__(self, src_lg, tgt_lg, **kwargs):
+        super(NLLBTaskConfig, self).__init__(**kwargs)
+        self.src_lg = src_lg
+        self.tgt_lg = tgt_lg
+
+
+class NLLB(datasets.GeneratorBasedBuilder):
+    """No Language Left Behind Dataset."""
+
+    BUILDER_CONFIGS = [
+        NLLBTaskConfig(
+            name=f"{src_lg}-{tgt_lg}",
+            version=datasets.Version("1.0.0"),
+            description=f"No Language Left Behind (NLLB): {src_lg} - {tgt_lg}",
+            src_lg=src_lg,
+            tgt_lg=tgt_lg,
+        )
+        for (src_lg, tgt_lg) in _LANGUAGE_PAIRS
+    ]
+    BUILDER_CONFIG_CLASS = NLLBTaskConfig
+
+    def _info(self):
+        # define feature types
+        features = datasets.Features(
+            {
+                "translation": datasets.Translation(
+                    languages=(self.config.src_lg, self.config.tgt_lg)
+                ),
+                "laser_score": datasets.Value("float32"),
+                "source_sentence_lid": datasets.Value("float32"),
+                "target_sentence_lid": datasets.Value("float32"),
+                "source_sentence_source": datasets.Value("string"),
+                "source_sentence_url": datasets.Value("string"),
+                "target_sentence_source": datasets.Value("string"),
+                "target_sentence_url": 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 file
+        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.reader(f, delimiter="\t")
+            for id_, example in enumerate(f):
+                try:
+                    datarow = example.split("\t")
+                    row = {}
+                    row["translation"] = {
+                        source_lg: datarow[0],
+                        target_lg: datarow[1],
+                    }  # create translation json
+                    row["laser_score"] = float(datarow[2])
+                    row["source_sentence_lid"] = float(datarow[3])
+                    row["target_sentence_lid"] = float(datarow[4])
+                    row["source_sentence_source"] = datarow[5]
+                    row["source_sentence_url"] = datarow[6]
+                    row["target_sentence_source"] = datarow[7]
+                    row["target_sentence_url"] = datarow[8]
+                    row = {
+                        k: None if not v else v for k, v in row.items()
+                    }  # replace empty values
+                except:
+                    print(datarow)
+                    raise
+                yield id_, row
+
+
+# to test the script, go to the root folder of the repo (nllb) and run:
+# datasets-cli test nllb --save_infos --all_configs