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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- expert-generated
-language:
-- en
-license:
-- mit
-multilinguality:
-- monolingual
-pretty_name: sentiment-classification-reviews-with-drift
-size_categories:
-- 10K<n<100K
-task_categories:
-- text-classification
-task_ids:
-- sentiment-classification
----
-
-# Dataset Card for `reviews_with_drift`
-
-## 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)
-  - [language](#language)
-- [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
-
-### Dataset Summary
-
-This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
-
-### Supported Tasks and Leaderboards
-
-`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
-
-### language
-
-Text is mainly written in english.
-
-## 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
-
-[More Information Needed]
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-[More Information Needed]
-
-#### 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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.

beer_reviews_label_drift_neutral.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/beer_reviews_label_drift_neutral/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 BeerReviewsLabelDriftNeutral(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):
-        #class_names = ["negative", "neutral", "positive"]
-        class_names = ["negative", "neutral", "positive"]
-        # 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"),
-                "beer_ABV": datasets.Value("float"),
-                "beer_name":datasets.Value("string"),
-                "beer_style":datasets.Value("string"),
-                "review_appearance": datasets.Value("float"),
-                "review_palette": datasets.Value("float"),
-                "review_taste": datasets.Value("float"),
-                "review_aroma": datasets.Value("float"),
-                "text":datasets.Value("string"),
-                "label":datasets.ClassLabel(names=class_names),
-            }
-        )
-
-        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
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            supervised_keys=("text", "label"),
-            # Homepage of the dataset for documentation
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    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)
-            for id_, row in enumerate(csv_reader):
-                prediction_ts,name,style,ABV,appearance,palette,taste,aroma,text,label=row
-                if id_==0:
-                    continue
-                yield id_, {
-                    "prediction_ts":prediction_ts,
-                    "beer_name": name,
-                    "beer_style": style,
-                    "beer_ABV": ABV,
-                    "review_appearance": appearance,
-                    "review_palette": palette,
-                    "review_taste": taste,
-                    "review_aroma": aroma,
-                    "text": text,
-                    "label":label,
-                }

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