metadata

language:
  - pt
license: apache-2.0
size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
  - token-classification
  - sentence-similarity
pretty_name: BidCorpus
dataset_info:
  - config_name: bidCorpus_NER_keyphrase
    features:
      - name: tokens
        sequence: string
      - name: id
        dtype: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-LOCAL
              '2': I-LOCAL
              '3': B-OBJETO
              '4': I-OBJETO
    splits:
      - name: train
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        num_examples: 1632
      - name: test
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        num_examples: 204
      - name: validation
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        num_examples: 204
    download_size: 514441
    dataset_size: 4564950
  - config_name: bidCorpus_gold
    features:
      - name: text
        dtype: string
      - name: certidao_protesto
        dtype: int64
      - name: certificado_boas_praticas
        dtype: int64
      - name: comprovante_localizacao
        dtype: int64
      - name: idoneidade_financeira
        dtype: int64
      - name: integralizado
        dtype: int64
      - name: licenca_ambiental
        dtype: int64
      - name: n_min_max_limitacao_atestados
        dtype: int64
    splits:
      - name: train
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        num_examples: 1454
      - name: test
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        num_examples: 182
      - name: validation
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        num_examples: 182
    download_size: 5647239
    dataset_size: 13939689
  - config_name: bidCorpus_object_similarity
    features:
      - name: objeto1
        dtype: string
      - name: nerObjeto1
        dtype: string
      - name: objeto2
        dtype: string
      - name: nerObjeto2
        dtype: string
      - name: humanScore
        dtype: float64
      - name: nerObjeto1_words
        dtype: int64
      - name: objeto1_words
        dtype: int64
      - name: percentual_words
        dtype: float64
      - name: nerObjeto2_words
        dtype: int64
      - name: objeto2_words
        dtype: int64
      - name: bertscore_ner
        dtype: int64
      - name: bertscore_objs
        dtype: int64
    splits:
      - name: train
        num_bytes: 2682850
        num_examples: 1403
      - name: test
        num_bytes: 342301
        num_examples: 176
      - name: validation
        num_bytes: 364743
        num_examples: 175
    download_size: 911048
    dataset_size: 3389894
  - config_name: bidCorpus_objects_correct_allowed
    features:
      - name: text
        dtype: string
      - name: corretude
        dtype: int64
      - name: permitido
        dtype: int64
    splits:
      - name: train
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        num_examples: 1089
      - name: test
        num_bytes: 278073
        num_examples: 137
      - name: validation
        num_bytes: 326285
        num_examples: 136
    download_size: 1108156
    dataset_size: 2341948
  - config_name: bidCorpus_objects_type
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
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        num_examples: 1709
      - name: test
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        num_examples: 214
      - name: validation
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        num_examples: 214
    download_size: 484599
    dataset_size: 1274529
  - config_name: bidCorpus_objects_type_cased
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
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        num_examples: 2326
      - name: test
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        num_examples: 582
    download_size: 770749
    dataset_size: 1813348
  - config_name: bidCorpus_qual_model
    features:
      - name: text
        dtype: string
      - name: certidao_protesto
        dtype: int64
      - name: certificado_boas_praticas
        dtype: int64
      - name: comprovante_localizacao
        dtype: int64
      - name: idoneidade_financeira
        dtype: int64
      - name: integralizado
        dtype: int64
      - name: licenca_ambiental
        dtype: int64
      - name: n_min_max_limitacao_atestados
        dtype: int64
    splits:
      - name: train
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        num_examples: 177133
      - name: test
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        num_examples: 22142
      - name: validation
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        num_examples: 22142
    download_size: 767641718
    dataset_size: 1958134251
  - config_name: bidCorpus_qual_weak_sup
    features:
      - name: text
        dtype: string
      - name: certidao_protesto
        dtype: int64
      - name: certificado_boas_praticas
        dtype: int64
      - name: comprovante_localizacao
        dtype: int64
      - name: idoneidade_financeira
        dtype: int64
      - name: integralizado
        dtype: int64
      - name: licenca_ambiental
        dtype: int64
      - name: n_min_max_limitacao_atestados
        dtype: int64
    splits:
      - name: train
        num_bytes: 1566000515
        num_examples: 177133
      - name: test
        num_bytes: 195502355
        num_examples: 22142
      - name: validation
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        num_examples: 22142
    download_size: 767927678
    dataset_size: 1958134251
  - config_name: bidCorpus_raw
    features:
      - name: ID-LICITACAO
        dtype: float64
      - name: ID-ARQUIVO
        dtype: float64
      - name: OBJETO
        dtype: string
      - name: JULGAMENTO
        dtype: string
      - name: CONDICAO_PARTICIPACAO
        dtype: string
      - name: HABILITACAO
        dtype: string
      - name: CREDENCIAMENTO
        dtype: string
    splits:
      - name: train
        num_bytes: 4248532882
        num_examples: 373650
    download_size: 1787451169
    dataset_size: 4248532882
  - config_name: bidCorpus_sections_type
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 3141390
        num_examples: 1224
      - name: test
        num_bytes: 387562
        num_examples: 153
      - name: validation
        num_bytes: 477489
        num_examples: 153
    download_size: 2010213
    dataset_size: 4006441
  - config_name: bidCorpus_sections_type_cleaned
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 4006441
        num_examples: 1530
    download_size: 1873797
    dataset_size: 4006441
  - config_name: bidCorpus_synthetic
    features:
      - name: text
        dtype: string
      - name: certidao_protesto
        dtype: int64
      - name: certificado_boas_praticas
        dtype: int64
      - name: comprovante_localizacao
        dtype: int64
      - name: idoneidade_financeira
        dtype: int64
      - name: integralizado
        dtype: int64
      - name: licenca_ambiental
        dtype: int64
      - name: n_min_max_limitacao_atestados
        dtype: int64
    splits:
      - name: train
        num_bytes: 11104985
        num_examples: 1454
      - name: test
        num_bytes: 1400000
        num_examples: 182
      - name: validation
        num_bytes: 1438114
        num_examples: 182
    download_size: 5673825
    dataset_size: 13943099
configs:
  - config_name: bidCorpus_NER_keyphrase
    data_files:
      - split: train
        path: bidCorpus_NER_keyphrase/train-*
      - split: test
        path: bidCorpus_NER_keyphrase/test-*
      - split: validation
        path: bidCorpus_NER_keyphrase/validation-*
  - config_name: bidCorpus_gold
    data_files:
      - split: train
        path: bidCorpus_gold/train-*
      - split: test
        path: bidCorpus_gold/test-*
      - split: validation
        path: bidCorpus_gold/validation-*
  - config_name: bidCorpus_object_similarity
    data_files:
      - split: train
        path: bidCorpus_object_similarity/train-*
      - split: test
        path: bidCorpus_object_similarity/test-*
      - split: validation
        path: bidCorpus_object_similarity/validation-*
  - config_name: bidCorpus_objects_correct_allowed
    data_files:
      - split: train
        path: bidCorpus_objects_correct_allowed/train-*
      - split: test
        path: bidCorpus_objects_correct_allowed/test-*
      - split: validation
        path: bidCorpus_objects_correct_allowed/validation-*
  - config_name: bidCorpus_objects_type
    data_files:
      - split: train
        path: bidCorpus_objects_type/train-*
      - split: test
        path: bidCorpus_objects_type/test-*
      - split: validation
        path: bidCorpus_objects_type/validation-*
  - config_name: bidCorpus_objects_type_cased
    data_files:
      - split: train
        path: bidCorpus_objects_type_cased/train-*
      - split: test
        path: bidCorpus_objects_type_cased/test-*
  - config_name: bidCorpus_qual_model
    data_files:
      - split: train
        path: bidCorpus_qual_model/train-*
      - split: test
        path: bidCorpus_qual_model/test-*
      - split: validation
        path: bidCorpus_qual_model/validation-*
  - config_name: bidCorpus_qual_weak_sup
    data_files:
      - split: train
        path: bidCorpus_qual_weak_sup/train-*
      - split: test
        path: bidCorpus_qual_weak_sup/test-*
      - split: validation
        path: bidCorpus_qual_weak_sup/validation-*
  - config_name: bidCorpus_raw
    data_files:
      - split: train
        path: bidCorpus_raw/train-*
  - config_name: bidCorpus_sections_type
    data_files:
      - split: train
        path: bidCorpus_sections_type/train-*
      - split: test
        path: bidCorpus_sections_type/test-*
      - split: validation
        path: bidCorpus_sections_type/validation-*
  - config_name: bidCorpus_sections_type_cleaned
    data_files:
      - split: train
        path: bidCorpus_sections_type_cleaned/train-*
  - config_name: bidCorpus_synthetic
    data_files:
      - split: train
        path: bidCorpus_synthetic/train-*
      - split: test
        path: bidCorpus_synthetic/test-*
      - split: validation
        path: bidCorpus_synthetic/validation-*
tags:
  - legal

Dataset Card for "BidCorpus"

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage:
Repository:
Paper:
Leaderboard:
Point of Contact:

How to load the datasets

To load one of the datasets, simply provide the tcepi/bidCorpus argument as the first parameter, followed by the name of the desired dataset, such as bid_corpus_raw.

from datasets import load_dataset
dataset = load_dataset("tcepi/bidCorpus", "bidCorpus_raw")

The csv format version of the datasets is available in the bidCorpus_csvs folder.

Dataset Summary

The BidCorpus dataset consists of various configurations related to bidding documents. It includes datasets for Named Entity Recognition, Multi-label Classification, Sentence Similarity, and more. Each configuration focuses on different aspects of bidding documents and is designed for specific tasks.

Supported Tasks and Leaderboards

The supported tasks are the following:

Dataset	Source	Sub-domain	Task Type	Classes
bidCorpus_NER_keyphrase	-	Seção Objeto de Editais de Licitação	Named Entity Recognition	4
bidCorpus_gold	-	Seção de Habilitação de Editais de Licitação	Multi-label Classification	7
bidCorpus_object_similarity	-	Seção Objeto de Editais de Licitação	Sentence Similarity	2
bidCorpus_objects_correct_allowed	-	Seção Objeto de Editais de Licitação	Multi-class Classification	4
bidCorpus_objects_type	-	Seção Objeto de Editais de Licitação	Multi-class Classification	4
bidCorpus_qual_model	-	Seção de Habilitação de Editais de Licitação	Multi-label Classification	7
bidCorpus_qual_weak_sup	-	Seção de Habilitação de Editais de Licitação	Multi-label Classification	7
bidCorpus_synthetic	-	Seção de Habilitação de Editais de Licitação	Multi-label Classification	7
bidCorpus_sections_type	-	Seções de Editais de Licitação	Multi-label Classification	5
bidCorpus_raw	-	Seções de Editais de Licitação	n/a	n/a

bidCorpus_NER_keyphrase

This dataset is composed of texts from the "object" section of bidding notices. The dataset is labeled with two types of named entities, following the IOB (Inside-Outside-Beginning) format.

Object of the bid: Refers to the item to be acquired or the service to be contracted. The tags can be "B-OBJECT" (beginning of the entity) and "I-OBJECT" (continuation of the entity).
Municipality of the managing unit: Indicates the location of the entity responsible for the bid. The tags can be "B-MUNICIPALITY" (beginning of the entity) and "I-MUNICIPALITY" (continuation of the entity).

This dataset is intended for training named entity recognition (NER) models, which are used to automatically identify and classify these entities within the texts. The labeled structure of the dataset facilitates the task of teaching models to distinguish between different types of relevant information in the bidding notices. The dataset follows the IOB format for named entity recognition, with entities labeled as either part of the object of the bid or the municipality of the managing unit.

bidCorpus_gold

This dataset consists of texts from the qualification section of bidding notices. Annotated by experts in public procurement, the dataset is multilabel and contains seven labels that indicate possible signs of fraud in public contracts.

Certidão de Protesto: Verification of any protests in the company's name.
Certificado de Boas Práticas: Assessment of adherence to recommended practices in the sector.
Comprovante de Localização: Confirmation of the company's physical address.
Idoneidade Financeira: Analysis of the company's financial health.
Integralização de Capital: Verification of the company's capital stock integration.
Licença Ambiental: Evaluation of compliance with environmental regulations.
Limitação de Atestados: Verification of the minimum and maximum number of certificates required.

This dataset is used for training machine learning models to detect signs of fraud in public procurement processes. The multilabel structure allows the models to learn to identify multiple suspicious characteristics simultaneously, providing a valuable tool for the analysis and prevention of fraud in public contracts.

bidCorpus_object_similarity

This dataset is designed to assess text similarity in the "object" section of bidding notices by comparing pairs of distinct notices. Annotated by experts in public procurement, each entry consists of a pair of "object" sections labeled with:

1: The sections are similar.
0: The sections are not similar.

The dataset supports tasks such as document comparison, clustering, and retrieval. It provides a valuable resource for training and evaluating models on how effectively they can determine similarities between bidding notices.

The pairs are annotated with expert labels to ensure high-quality data, making this dataset ideal for developing and testing algorithms for text similarity analysis. It helps improve the efficiency and accuracy of managing and analyzing bidding documents.

bidCorpus_objects_correct_allowed

This dataset focuses on two classifications related to the "object" section of bidding notices:

Object Classification: Determines whether a section is the "object" section of a bidding notice.
Permissivity Classification: Assesses whether the object requires permissivity, meaning whether the contract involves areas such as the purchase of medications, cleaning services, or fuels, which might necessitate a certificate of location and an environmental license from regulatory institutions overseeing these activities.

The dataset provides labels for these classifications to support the analysis of compliance and requirements in bidding documents.

bidCorpus_objects_type

This dataset focuses on classifying the type of procurement found in the "object" section of bidding notices. Specifically, it categorizes the type of product or service being bid on into one of the following categories:

Consumables: Items that are used up or consumed during use, such as office supplies or food products.
Permanent Assets: Items with a longer lifespan that are intended for repeated use, such as machinery or equipment.
Services: Non-tangible activities provided to fulfill a need, such as consulting or maintenance services.
Engineering Works: Projects related to construction, infrastructure, or other engineering tasks.

The dataset provides labels for these classifications to assist in the analysis and organization of bidding documents, facilitating a better understanding of procurement types and aiding in the efficient management of bidding processes.

bidCorpus_qual_model

Certidão de Protesto: Verification of any protests in the company's name.
Certificado de Boas Práticas: Assessment of adherence to recommended practices in the sector.
Comprovante de Localização: Confirmation of the company's physical address.
Idoneidade Financeira: Analysis of the company's financial health.
Integralização de Capital: Verification of the company's capital stock integration.
Licença Ambiental: Evaluation of compliance with environmental regulations.
Limitação de Atestados: Verification of the minimum and maximum number of certificates required.

Unlike the expert-annotated previous dataset, this dataset has been annotated by a model trained on that data. This automated process ensures consistency and scalability while utilizing insights from the original expert annotations.

The dataset is intended for training and evaluating machine learning models to detect fraud in public procurement. The automated annotation enhances research and development in fraud detection, aiming to improve the accuracy and efficiency of identifying suspicious activities in bidding notices. Its multilabel structure supports the identification and classification of multiple fraud indicators simultaneously, aiding in the ongoing analysis and prevention of fraudulent practices in public contracts.

bidCorpus_qual_weak_sup

This dataset consists of texts from the qualification section of bidding notices and is annotated using weak supervision techniques, specifically through regular expressions. It follows a multilabel format similar to the bidCorpus_gold dataset, with labels indicating possible signs of fraud in public procurement processes.

Certidão de Protesto: Verification of any protests in the company's name.
Certificado de Boas Práticas: Assessment of adherence to recommended practices in the sector.
Comprovante de Localização: Confirmation of the company's physical address.
Idoneidade Financeira: Analysis of the company's financial health.
Integralização de Capital: Verification of the company's capital stock integration.
Licença Ambiental: Evaluation of compliance with environmental regulations.
Limitação de Atestados: Verification of the minimum and maximum number of certificates required.

Unlike the previous expert-annotated dataset, this dataset has been annotated using weak supervision techniques, specifically regular expressions. This approach provides a scalable method for labeling data by applying patterns to identify potential fraud indicators, although it may lack the precision of expert annotations.

The dataset is designed for training and evaluating machine learning models to detect fraud in public procurement. The use of weak supervision through regular expressions facilitates the creation of large annotated datasets, supporting research and development in fraud detection. The multilabel structure allows models to classify multiple fraud indicators simultaneously, improving the efficiency of identifying and preventing fraudulent practices in public contracts.

bidCorpus_synthetic

This dataset consists of texts from the qualification section of bidding notices and is annotated using a model trained on the original fraud detection dataset. It follows a multilabel format similar to the bidCorpus_gold dataset, with labels indicating possible signs of fraud in public procurement processes. This dataset underwent modifications to its keywords by incorporating synonyms to evaluate the model's accuracy in handling words different from those it was previously accustomed to.

Certidão de Protesto: Verification of any protests in the company's name.
Certificado de Boas Práticas: Assessment of adherence to recommended practices in the sector.
Comprovante de Localização: Confirmation of the company's physical address.
Idoneidade Financeira: Analysis of the company's financial health.
Integralização de Capital: Verification of the company's capital stock integration.
Licença Ambiental: Evaluation of compliance with environmental regulations.
Limitação de Atestados: Verification of the minimum and maximum number of certificates required.

The dataset is intended for training and evaluating machine learning models to detect fraud in public procurement. Its multilabel structure supports the identification and classification of multiple fraud indicators simultaneously, aiding in the ongoing analysis and prevention of fraudulent practices in public contracts.

bidCorpus_sections_type

This dataset classifies different types of sections in bidding notices. The sections are categorized into the following labels:

Habilitação: Qualification section, where eligibility criteria and requirements are outlined.
Julgamento: Evaluation section, detailing the criteria and process for assessing bids.
Objeto: Object section, specifying the item or service being procured.
Outros: Other sections that do not fall into the categories above.
Credenciamento: Accreditation section, where the process for validating and registering vendors is described.

The dataset provides a systematic approach to categorize the various sections found in bidding notices, facilitating better organization and analysis of procurement documents.

bidCorpus_raw

This dataset consists of raw, unlabeled texts from sections of bidding notices. The sections included are:

Objeto: Describes the item or service being procured.
Julgamento: Outlines the criteria and process for evaluating bids.
Credenciamento: Details the procedures for vendor registration and validation.
Condições de Participação: Specifies the conditions required for participation in the bidding process.
Habilitação: Provides information on the qualifications and eligibility criteria for bidders.

This dataset offers a collection of unprocessed text from various sections of bidding notices, suitable for tasks such as text analysis, feature extraction, and the development of classification models.

Languages

We considered only datasets in Portuguese.