starter files

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+files.yml filter=lfs diff=lfs merge=lfs -text

files.yml

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf83d2c1ebc9d0f7c13f507e88977346ba358c142c38e04b51219f7fb9c74fbb
+size 329

paradocs.py

@@ -0,0 +1,170 @@
+# 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.
+"""
+This file provides a HuggingFace dataset loader implementation for
+the ParaDocs dataset
+ParaDocs is a multilingual machine translation dataset that has
+labelled document annotations for ParaCrawl, NewsCommentary, and
+Europarl data which can be used to create parallel document 
+datasets for training of context-aware machine translation models.
+"""
+
+# https://huggingface.co/docs/datasets/dataset_script
+
+import csv
+import json
+import os
+import re
+import pathlib
+from pathlib import Path
+import yaml
+from ast import literal_eval
+
+import datasets
+
+import gzip
+try:
+    import lzma as xz
+except ImportError:
+    import pylzma as xz
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+ParaDocs is a multilingual machine translation dataset that has
+labelled document annotations for ParaCrawl, NewsCommentary, and
+Europarl data which can be used to create parallel document 
+datasets for training of context-aware machine translation models.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/rewicks/paradocs"
+
+_LICENSE = "cc-by-sa-4.0"
+
+_URL = "https://huggingface.co/datasets/rewicks/paradocs"
+
+# Load the file paths for all the splits (per language currently)
+
+file_list_url = "https://huggingface.co/datasets/rewicks/paradocs/raw/main/files.yml"
+import urllib.request
+with urllib.request.urlopen(file_list_url) as f:
+    try:
+        fnames = yaml.safe_load(f)
+    except yaml.YAMLError as exc:
+        print("Error loading the file paths for the dataset splits. Aborting.")
+        exit(1)
+
+_DATA_URL = fnames['fnames']
+
+_VARIANTS = list(_DATA_URL.keys())
+
+
+class ParaDocs(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "src": datasets.Value("string"),
+                    "tgt": datasets.Value("string"),
+                    "sim_score_one" : datasets.Value("float32"),
+                    "sim_score_two": datasets.Value("float32"),
+                    "collection": datasets.Value("string"),
+                    "src_paragraph_id": datasets.Value("string"),
+                    "tgt_paragraph_id": datasets.Value("string"),
+                    "src_sentence_id": datasets.Value("string"),
+                    "tgt_sentence_id": datasets.Value("string"),
+                    "src_start_id": datasets.Value("string"),
+                    "src_end_id": datasets.Value("string"),
+                    "tgt_start_id": datasets.Value("string"),
+                    "tgt_end_id": datasets.Value("string"),
+                    "src_lid_prob": datasets.Value("float32"),
+                    "tgt_lid_prob": datasets.Value("float32"),
+                    "src_docid": datasets.Value("string"),
+                    "tgt_docid": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_sources = {self.config.name: _DATA_URL[self.config.name]}
+
+        return [
+            datasets.SplitGenerator(
+                name=lang,
+                gen_kwargs={
+                    "filepaths": dl_manager.download(data_sources[lang])
+                }
+            )
+            for lang
+            in data_sources
+        ]
+
+    def _get_qa_pair_list_features(self, qa_pair, feature_name):
+        res = []
+
+        if feature_name in qa_pair:
+            if qa_pair[feature_name]:
+                return qa_pair[feature_name]  
+        else:
+            if feature_name.startswith('en'):
+                feature_name = '_'.join(feature_name.split('_')[1:])
+                return self._get_qa_pair_list_features(qa_pair, feature_name)
+
+        return res
+    
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for filepath in filepaths:
+            # logger.info("Generating examples from = %s", filepath)
+            try:
+                with open(filepath, "r", encoding="utf-8") as f:
+                    rstream = csv.DictReader(f,
+                                                delimiter='\t',
+                                                fieldnames = [
+                                                    "src",
+                                                    "tgt",
+                                                    "sim_score_one",
+                                                    "sim_score_two",
+                                                    "collection",
+                                                    "src_paragraph_id",
+                                                    "tgt_paragraph_id",
+                                                    "src_sentence_id",
+                                                    "tgt_sentence_id",
+                                                    "src_start_id",
+                                                    "src_end_id",
+                                                    "tgt_start_id",
+                                                    "tgt_end_id",
+                                                    "src_lid_prob",
+                                                    "tgt_lid_prob",
+                                                    "src_docid",
+                                                    "tgt_docid"
+                                                ],
+                                                quoting=csv.QUOTE_NONE
+                    )
+                    for example in rstream:
+                        yield id_, example
+                        id_ += 1
+            except:
+                print("Error reading file:", filepath)