First commit

.gitattributes

@@ -35,3 +35,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+training.csv filter=lfs diff=lfs merge=lfs -text
+validation.csv filter=lfs diff=lfs merge=lfs -text
+production.csv filter=lfs diff=lfs merge=lfs -text

production.csv

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+oid sha256:c1fba58898a0cf79fb773665f687022f11044afe0a02abd47eba6179361fedd0
+size 2196116

training.csv

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+oid sha256:6ea888cd792d83af844f697c1cb216bf97b9c07121428bd3074ebc12503c4a2a
+size 757973

validation.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:067a81b0112d05e36551ce0d7296376170d5f74dafb5e07241ccbfde8abfc582
+size 74350

xtreme_en.py

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+# 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.
+
+# Lint as: python3
+"""IMDb movie revies dataset mixed with Trip Advisor Hotel Reviews to simulate drift accross time."""
+
+
+import csv
+import json
+import os
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+
+# TODO: Add BibTeX citation to our BLOG
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = ""
+# _CITATION = """\
+# @InProceedings{huggingface:dataset,
+# title = {A great new dataset},
+# author={huggingface, Inc.
+# },
+# year={2020}
+# }
+# """
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This dataset was crafted to be used in our tutorial [Link to the tutorial when
+ready]. It consists on product reviews from an e-commerce store. The reviews
+are labeled on a scale from 1 to 5 (stars). The training & validation sets are
+fully composed by reviews written in english. However, the production set has
+some reviews written in spanish. At Arize, we work to surface this issue and
+help you solve it.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://huggingface.co/datasets/arize-ai/xtreme_en/resolve/main/"
+_URLS = {
+    "training": _URL + "training.csv",
+    "validation": _URL + "validation.csv",
+    "production": _URL + "production.csv",
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class XtremeEn(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="default", version=VERSION, description="Default"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+            # These are the features of your dataset like images, labels ...
+            {
+                "prediction_ts": datasets.Value("float"),
+                "language":datasets.Value("string"),
+                "split_text": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=[
+                            "O",
+                            "B-PER",
+                            "I-PER",
+                            "B-ORG",
+                            "I-ORG",
+                            "B-LOC",
+                            "I-LOC",
+                        ]
+                    )
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # Homepage of the dataset for documentation
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        extracted_paths = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split("training"),
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": extracted_paths['training'],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("validation"),
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": extracted_paths['validation'],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("production"),
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": extracted_paths['production'],
+                },
+            ),
+        ]
+
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath) as csv_file:
+            csv_reader = csv.reader(csv_file, delimiter='\t')
+            for id_, row in enumerate(csv_reader):
+                prediction_ts,language,text,ner_tags = row
+                ner_tags_list = list(ner_tags.strip('[]').split(' '))
+                tokens = text.split(":-:")
+                if id_==0: # Skip headers
+                    continue
+                yield id_, {
+                    "prediction_ts":prediction_ts,
+                    "language":language,
+                    "split_text": tokens,
+                    "ner_tags":ner_tags_list,
+                }