Delete loading script

financial_phrasebank.py

@@ -1,149 +0,0 @@
-# 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.
-
-"""Financial Phrase Bank v1.0: Polar sentiment dataset of sentences from
-financial news. The dataset consists of 4840 sentences from English language
-financial news categorised by sentiment. The dataset is divided by agreement
-rate of 5-8 annotators."""
-
-
-import os
-
-import datasets
-
-
-_CITATION = """\
-@article{Malo2014GoodDO,
-  title={Good debt or bad debt: Detecting semantic orientations in economic texts},
-  author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
-  journal={Journal of the Association for Information Science and Technology},
-  year={2014},
-  volume={65}
-}
-"""
-
-_DESCRIPTION = """\
-The key arguments for the low utilization of statistical techniques in
-financial sentiment analysis have been the difficulty of implementation for
-practical applications and the lack of high quality training data for building
-such models. Especially in the case of finance and economic texts, annotated
-collections are a scarce resource and many are reserved for proprietary use
-only. To resolve the missing training data problem, we present a collection of
-∼ 5000 sentences to establish human-annotated standards for benchmarking
-alternative modeling techniques.
-
-The objective of the phrase level annotation task was to classify each example
-sentence into a positive, negative or neutral category by considering only the
-information explicitly available in the given sentence. Since the study is
-focused only on financial and economic domains, the annotators were asked to
-consider the sentences from the view point of an investor only; i.e. whether
-the news may have positive, negative or neutral influence on the stock price.
-As a result, sentences which have a sentiment that is not relevant from an
-economic or financial perspective are considered neutral.
-
-This release of the financial phrase bank covers a collection of 4840
-sentences. The selected collection of phrases was annotated by 16 people with
-adequate background knowledge on financial markets. Three of the annotators
-were researchers and the remaining 13 annotators were master’s students at
-Aalto University School of Business with majors primarily in finance,
-accounting, and economics.
-
-Given the large number of overlapping annotations (5 to 8 annotations per
-sentence), there are several ways to define a majority vote based gold
-standard. To provide an objective comparison, we have formed 4 alternative
-reference datasets based on the strength of majority agreement: all annotators
-agree, >=75% of annotators agree, >=66% of annotators agree and >=50% of
-annotators agree.
-"""
-
-_HOMEPAGE = "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news"
-
-_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License"
-
-_REPO = "https://huggingface.co/datasets/financial_phrasebank/resolve/main/data"
-_URL = f"{_REPO}/FinancialPhraseBank-v1.0.zip"
-
-
-_VERSION = datasets.Version("1.0.0")
-
-
-class FinancialPhraseBankConfig(datasets.BuilderConfig):
-    """BuilderConfig for FinancialPhraseBank."""
-
-    def __init__(
-        self,
-        split,
-        **kwargs,
-    ):
-        """BuilderConfig for Discovery.
-        Args:
-          filename_bit: `string`, the changing part of the filename.
-        """
-
-        super(FinancialPhraseBankConfig, self).__init__(name=f"sentences_{split}agree", version=_VERSION, **kwargs)
-
-        self.path = os.path.join("FinancialPhraseBank-v1.0", f"Sentences_{split.title()}Agree.txt")
-
-
-class FinancialPhrasebank(datasets.GeneratorBasedBuilder):
-
-    BUILDER_CONFIGS = [
-        FinancialPhraseBankConfig(
-            split="all",
-            description="Sentences where all annotators agreed",
-        ),
-        FinancialPhraseBankConfig(split="75", description="Sentences where at least 75% of annotators agreed"),
-        FinancialPhraseBankConfig(split="66", description="Sentences where at least 66% of annotators agreed"),
-        FinancialPhraseBankConfig(split="50", description="Sentences where at least 50% of annotators agreed"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(
-                        names=[
-                            "negative",
-                            "neutral",
-                            "positive",
-                        ]
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir, self.config.path)},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="iso-8859-1") as f:
-            for id_, line in enumerate(f):
-                sentence, label = line.rsplit("@", 1)
-                yield id_, {"sentence": sentence, "label": label}