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README.md

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----
-license: cc-by-nc-4.0
-task_categories:
-- token-classification
-language:
-- en
-pretty_name: FiNER
-size_categories:
-- 1K<n<10K
-multilinguality:
-- monolingual
-task_ids:
-- named-entity-recognition
----
-
-# Dataset Card for "FiNER-ORD"
-
-## 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 and Annotation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Additional Information](#additional-information)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contact Information](#contact-information)
-
-## Dataset Description
-
-- **Homepage:** [https://github.com/gtfintechlab/FiNER](https://github.com/gtfintechlab/FiNER)
-- **Repository:** [https://github.com/gtfintechlab/FiNER](https://github.com/gtfintechlab/FiNER)
-- **Paper:** [Arxiv Link]()
-- **Point of Contact:** [Agam A. Shah](https://shahagam4.github.io/)
-- **Size of train dataset file:** 1.34 MB
-- **Size of validation dataset file:** 160 KB  
-- **Size of test dataset file:** 412 KB  
-
-### Dataset Summary
-
-The FiNER-Open Research Dataset (FiNER-ORD) consists of a manually annotated dataset of financial news articles (in English)
-collected from [webz.io] (https://webz.io/free-datasets/financial-news-articles/).
-In total, there are 47851 news articles available in this data at the point of writing this paper. 
-Each news article is available in the form of a JSON document with various metadata information like
-the source of the article, publication date, author of the article, and the title of the article. 
-For the manual annotation of named entities in financial news, we randomly sampled 220 documents from the entire set of news articles. 
-We observed that some articles were empty in our sample, so after filtering the empty documents, we were left with a total of 201 articles.  
-We use [Doccano](https://github.com/doccano/doccano), an open-source annotation tool,
-to ingest the raw dataset and manually label person (PER), location (LOC), and organization (ORG) entities.
-For our experiments, we use the manually labeled FiNER-ORD to benchmark model performance. 
-Thus, we make a train, validation, and test split of FiNER-ORD. 
-To avoid biased results, manual annotation is performed by annotators who have no knowledge about the labeling functions for the weak supervision framework. 
-The train and validation sets are annotated by two separate annotators and validated by a third annotator. 
-The test dataset is annotated by another annotator. We present a manual annotation guide in the Appendix of the paper detailing the procedures used to create the manually annotated FiNER-ORD. 
-
-After manual annotation, the news articles are split into sentences. 
-We then tokenize each sentence, employing a script to tokenize multi-token entities into separate tokens (e.g. PER_B denotes the beginning token of a person (PER) entity
-and PER_I represents intermediate PER tokens). We exclude white spaces when tokenizing multi-token entities.
-The descriptive statistics on the resulting FiNER-ORD are available in the Table of [Data Splits](#data-splits) section.
-
-For more details check [information in paper]()
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-- It is a monolingual English dataset
-
-## Dataset Structure
-
-### Data Instances
-
-#### FiNER-ORD
-
-- **Size of train dataset file:** 1.34 MB
-- **Size of validation dataset file:** 160 KB  
-- **Size of test dataset file:** 412 KB   
-
-An example of a 'train' looks as follows.
-
-```
-{
-    "chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
-    "id": "0",
-    "ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-    "pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
-    "tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
-}
-```
-
-The original data files have `-DOCSTART-` lines used to separate documents, but these lines are removed here.
-Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation.
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### conll2003
-- `id`: a `string` feature.
-- `tokens`: a `list` of `string` features.
-- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
-
-```python
-{'"': 0, "''": 1, '#': 2, '$': 3, '(': 4, ')': 5, ',': 6, '.': 7, ':': 8, '``': 9, 'CC': 10, 'CD': 11, 'DT': 12,
- 'EX': 13, 'FW': 14, 'IN': 15, 'JJ': 16, 'JJR': 17, 'JJS': 18, 'LS': 19, 'MD': 20, 'NN': 21, 'NNP': 22, 'NNPS': 23,
- 'NNS': 24, 'NN|SYM': 25, 'PDT': 26, 'POS': 27, 'PRP': 28, 'PRP$': 29, 'RB': 30, 'RBR': 31, 'RBS': 32, 'RP': 33,
- 'SYM': 34, 'TO': 35, 'UH': 36, 'VB': 37, 'VBD': 38, 'VBG': 39, 'VBN': 40, 'VBP': 41, 'VBZ': 42, 'WDT': 43,
- 'WP': 44, 'WP$': 45, 'WRB': 46}
-```
-
-- `chunk_tags`: a `list` of classification labels (`int`). Full tagset with indices:
-
-```python
-{'O': 0, 'B-ADJP': 1, 'I-ADJP': 2, 'B-ADVP': 3, 'I-ADVP': 4, 'B-CONJP': 5, 'I-CONJP': 6, 'B-INTJ': 7, 'I-INTJ': 8,
- 'B-LST': 9, 'I-LST': 10, 'B-NP': 11, 'I-NP': 12, 'B-PP': 13, 'I-PP': 14, 'B-PRT': 15, 'I-PRT': 16, 'B-SBAR': 17,
- 'I-SBAR': 18, 'B-UCP': 19, 'I-UCP': 20, 'B-VP': 21, 'I-VP': 22}
-```
-
-- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
-
-```python
-{'O': 0, 'PER_B': 1, 'PER_I': 2, 'LOC_B': 3, 'LOC_I': 4, 'ORG_B': 5, 'ORG_I': 6}
-```
-
-### Data Splits
-
-| **FiNER-ORD** | **Train** | **Validation** | **Test** |
-|------------------|----------------|---------------------|---------------|
-| # Articles      | 135            | 24                  | 42            |
-| # Tokens        | 80,531         | 10,233              | 25,957        |
-| # LOC entities  | 1,255          | 267                 | 428           |
-| # ORG entities  | 3,440          | 524                 | 933           |
-| # PER entities  | 1,374          | 222                 | 466           |
-
-
-## Dataset Creation and Annotation
-
-### Paper
-
-[Information in paper ]()
-
-
-## Additional Information
-
-
-### Licensing Information
-
-[Information in paper ]()
-
-### Citation Information
-
-```
-@article{shah2023finer,
-  title={FiNER: Financial Named Entity Recognition Dataset and Weak-supervision Model},
-  author={Agam Shah and Ruchit Vithani and Abhinav Gullapalli and Sudheer Chava},
-  journal={Under Review at SIGIR},
-  year={2023}
-}
-
-```
-
-
-### Contact Information
-
-Please contact Agam Shah (ashah482[at]gatech[dot]edu) about any FiNER-related issues and questions.  
-[@shahagam4](https://github.com/shahagam4)  
-[Website](https://shahagam4.github.io/) 

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