Duplicate from takala/financial_phrasebank

Co-authored-by: Parquet-converter (BOT) <parquet-converter@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,301 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license:
+- cc-by-nc-sa-3.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+- sentiment-classification
+pretty_name: FinancialPhrasebank
+dataset_info:
+- config_name: sentences_allagree
+  features:
+  - name: sentence
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': negative
+          '1': neutral
+          '2': positive
+  splits:
+  - name: train
+    num_bytes: 303371
+    num_examples: 2264
+  download_size: 681890
+  dataset_size: 303371
+- config_name: sentences_75agree
+  features:
+  - name: sentence
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': negative
+          '1': neutral
+          '2': positive
+  splits:
+  - name: train
+    num_bytes: 472703
+    num_examples: 3453
+  download_size: 681890
+  dataset_size: 472703
+- config_name: sentences_66agree
+  features:
+  - name: sentence
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': negative
+          '1': neutral
+          '2': positive
+  splits:
+  - name: train
+    num_bytes: 587152
+    num_examples: 4217
+  download_size: 681890
+  dataset_size: 587152
+- config_name: sentences_50agree
+  features:
+  - name: sentence
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': negative
+          '1': neutral
+          '2': positive
+  splits:
+  - name: train
+    num_bytes: 679240
+    num_examples: 4846
+  download_size: 681890
+  dataset_size: 679240
+tags:
+- finance
+---
+
+# Dataset Card for financial_phrasebank
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [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
+
+- **Homepage:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news) [ResearchGate](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10)
+- **Repository:**
+- **Paper:** [Arxiv](https://arxiv.org/abs/1307.5336)
+- **Leaderboard:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news/code) [PapersWithCode](https://paperswithcode.com/sota/sentiment-analysis-on-financial-phrasebank) =
+- **Point of Contact:** [Pekka Malo](mailto:pekka.malo@aalto.fi) [Ankur Sinha](mailto:ankur.sinha@aalto.fi)
+
+### Dataset Summary
+
+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.
+
+### Supported Tasks and Leaderboards
+
+Sentiment Classification
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{ "sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .",
+  "label": "negative"
+}
+```
+
+### Data Fields
+Each example contains:
+- sentence: a tokenized line from the dataset
+- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'
+
+### Data Splits
+There's no train/validation/test split.
+
+However the dataset is available in four possible configurations depending on the percentage of agreement of annotators:
+
+- `sentences_50agree`; Number of instances with >=50% annotator agreement: 4846 
+- `sentences_66agree`: Number of instances with >=66% annotator agreement: 4217
+- `sentences_75agree`: Number of instances with >=75% annotator agreement: 3453
+- `sentences_allagree`: Number of instances with 100% annotator agreement: 2264
+
+### Usage
+
+```python
+from datasets import load_dataset
+
+# Load the highest-agreement configuration
+ds = load_dataset("takala/financial_phrasebank", "sentences_allagree")
+
+print(ds)
+print(ds["train"][0])
+```
+
+Other configurations (e.g. `sentences_75agree`, `sentences_66agree`) can be loaded by changing the second argument.
+
+## Quick baseline (Transformers)
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+ds = load_dataset("takala/financial_phrasebank", "sentences_allagree")["train"]
+clf = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+print(ds[0]["sentence"])
+print(clf(ds[0]["sentence"]))
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+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.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The corpus used in this paper is made out of English news on all listed
+companies in OMX Helsinki. The news has been downloaded from the LexisNexis
+database using an automated web scraper. Out of this news database, a random
+subset of 10,000 articles was selected to obtain good coverage across small and
+large companies, companies in different industries, as well as different news
+sources. Following the approach taken by Maks and Vossen (2010), we excluded
+all sentences which did not contain any of the lexicon entities. This reduced
+the overall sample to 53,400 sentences, where each has at least one or more
+recognized lexicon entity. The sentences were then classified according to the
+types of entity sequences detected. Finally, a random sample of ∼5000 sentences
+was chosen to represent the overall news database.
+
+#### Who are the source language producers?
+
+The source data was written by various financial journalists.
+
+### Annotations
+
+#### Annotation process
+
+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.
+
+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: 
+
+#### Who are the annotators?
+
+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.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+All annotators were from the same institution and so interannotator agreement
+should be understood with this taken into account.
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
+
+If you are interested in commercial use of the data, please contact the following authors for an appropriate license:
+- [Pekka Malo](mailto:pekka.malo@aalto.fi)
+- [Ankur Sinha](mailto:ankur.sinha@aalto.fi)
+
+### Citation Information
+
+```
+@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}
+}
+```
+
+### Contributions
+
+Thanks to [@frankier](https://github.com/frankier) for adding this dataset.

data/FinancialPhraseBank-v1.0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e1a06c4900fdae46091d031068601e3773ba067c7cecb5b0da1dcba5ce989a6
+size 681890

financial_phrasebank.py

@@ -0,0 +1,149 @@
+# 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}