Datasets:

Modalities:
Tabular
Text
Formats:
parquet
Languages:
Portuguese
Tags:
legal
DOI:
Libraries:
Datasets
Dask
License:
parquet-converter commited on
Commit
e45c2ae
1 Parent(s): 1af941b

Update parquet files

Browse files
README.md DELETED
@@ -1,865 +0,0 @@
1
- ---
2
- language:
3
- - pt
4
- license: apache-2.0
5
- size_categories:
6
- - 10K<n<100K
7
- task_categories:
8
- - text-classification
9
- - token-classification
10
- - sentence-similarity
11
- pretty_name: BidCorpus
12
- dataset_info:
13
- - config_name: bidCorpus_NER_keyphrase
14
- features:
15
- - name: tokens
16
- sequence: string
17
- - name: id
18
- dtype: string
19
- - name: ner_tags
20
- sequence:
21
- class_label:
22
- names:
23
- '0': O
24
- '1': B-LOCAL
25
- '2': I-LOCAL
26
- '3': B-OBJETO
27
- '4': I-OBJETO
28
- splits:
29
- - name: train
30
- num_bytes: 3657983
31
- num_examples: 1632
32
- - name: test
33
- num_bytes: 442382
34
- num_examples: 204
35
- - name: validation
36
- num_bytes: 464585
37
- num_examples: 204
38
- download_size: 514441
39
- dataset_size: 4564950
40
- - config_name: bidCorpus_gold
41
- features:
42
- - name: text
43
- dtype: string
44
- - name: certidao_protesto
45
- dtype: int64
46
- - name: certificado_boas_praticas
47
- dtype: int64
48
- - name: comprovante_localizacao
49
- dtype: int64
50
- - name: idoneidade_financeira
51
- dtype: int64
52
- - name: integralizado
53
- dtype: int64
54
- - name: licenca_ambiental
55
- dtype: int64
56
- - name: n_min_max_limitacao_atestados
57
- dtype: int64
58
- splits:
59
- - name: train
60
- num_bytes: 10979027
61
- num_examples: 1454
62
- - name: test
63
- num_bytes: 1499746
64
- num_examples: 182
65
- - name: validation
66
- num_bytes: 1460916
67
- num_examples: 182
68
- download_size: 5647239
69
- dataset_size: 13939689
70
- - config_name: bidCorpus_object_similarity
71
- features:
72
- - name: objeto1
73
- dtype: string
74
- - name: nerObjeto1
75
- dtype: string
76
- - name: objeto2
77
- dtype: string
78
- - name: nerObjeto2
79
- dtype: string
80
- - name: humanScore
81
- dtype: float64
82
- - name: nerObjeto1_words
83
- dtype: int64
84
- - name: objeto1_words
85
- dtype: int64
86
- - name: percentual_words
87
- dtype: float64
88
- - name: nerObjeto2_words
89
- dtype: int64
90
- - name: objeto2_words
91
- dtype: int64
92
- - name: bertscore_ner
93
- dtype: int64
94
- - name: bertscore_objs
95
- dtype: int64
96
- splits:
97
- - name: train
98
- num_bytes: 2682850
99
- num_examples: 1403
100
- - name: test
101
- num_bytes: 342301
102
- num_examples: 176
103
- - name: validation
104
- num_bytes: 364743
105
- num_examples: 175
106
- download_size: 911048
107
- dataset_size: 3389894
108
- - config_name: bidCorpus_objects_correct_allowed
109
- features:
110
- - name: text
111
- dtype: string
112
- - name: corretude
113
- dtype: int64
114
- - name: permitido
115
- dtype: int64
116
- splits:
117
- - name: train
118
- num_bytes: 1737590
119
- num_examples: 1089
120
- - name: test
121
- num_bytes: 278073
122
- num_examples: 137
123
- - name: validation
124
- num_bytes: 326285
125
- num_examples: 136
126
- download_size: 1108156
127
- dataset_size: 2341948
128
- - config_name: bidCorpus_objects_type
129
- features:
130
- - name: text
131
- dtype: string
132
- - name: label
133
- dtype: int64
134
- splits:
135
- - name: train
136
- num_bytes: 1024977
137
- num_examples: 1709
138
- - name: test
139
- num_bytes: 114336
140
- num_examples: 214
141
- - name: validation
142
- num_bytes: 135216
143
- num_examples: 214
144
- download_size: 484599
145
- dataset_size: 1274529
146
- - config_name: bidCorpus_qual_model
147
- features:
148
- - name: text
149
- dtype: string
150
- - name: certidao_protesto
151
- dtype: int64
152
- - name: certificado_boas_praticas
153
- dtype: int64
154
- - name: comprovante_localizacao
155
- dtype: int64
156
- - name: idoneidade_financeira
157
- dtype: int64
158
- - name: integralizado
159
- dtype: int64
160
- - name: licenca_ambiental
161
- dtype: int64
162
- - name: n_min_max_limitacao_atestados
163
- dtype: int64
164
- splits:
165
- - name: train
166
- num_bytes: 1567039880
167
- num_examples: 177133
168
- - name: test
169
- num_bytes: 195995975
170
- num_examples: 22142
171
- - name: validation
172
- num_bytes: 195098396
173
- num_examples: 22142
174
- download_size: 767641718
175
- dataset_size: 1958134251
176
- - config_name: bidCorpus_qual_weak_sup
177
- features:
178
- - name: text
179
- dtype: string
180
- - name: certidao_protesto
181
- dtype: int64
182
- - name: certificado_boas_praticas
183
- dtype: int64
184
- - name: comprovante_localizacao
185
- dtype: int64
186
- - name: idoneidade_financeira
187
- dtype: int64
188
- - name: integralizado
189
- dtype: int64
190
- - name: licenca_ambiental
191
- dtype: int64
192
- - name: n_min_max_limitacao_atestados
193
- dtype: int64
194
- splits:
195
- - name: train
196
- num_bytes: 1566000515
197
- num_examples: 177133
198
- - name: test
199
- num_bytes: 195502355
200
- num_examples: 22142
201
- - name: validation
202
- num_bytes: 196631381
203
- num_examples: 22142
204
- download_size: 767927678
205
- dataset_size: 1958134251
206
- - config_name: bidCorpus_sections_type
207
- features:
208
- - name: text
209
- dtype: string
210
- - name: label
211
- dtype: int64
212
- splits:
213
- - name: train
214
- num_bytes: 3141390
215
- num_examples: 1224
216
- - name: test
217
- num_bytes: 387562
218
- num_examples: 153
219
- - name: validation
220
- num_bytes: 477489
221
- num_examples: 153
222
- download_size: 2010213
223
- dataset_size: 4006441
224
- - config_name: bidCorpus_sections_type_cleaned
225
- features:
226
- - name: text
227
- dtype: string
228
- - name: label
229
- dtype: int64
230
- splits:
231
- - name: train
232
- num_bytes: 4006441
233
- num_examples: 1530
234
- download_size: 1873797
235
- dataset_size: 4006441
236
- - config_name: bid_corpus_raw
237
- features:
238
- - name: ID-LICITACAO
239
- dtype: float64
240
- - name: ID-ARQUIVO
241
- dtype: float64
242
- - name: OBJETO
243
- dtype: string
244
- - name: JULGAMENTO
245
- dtype: string
246
- - name: CONDICAO_PARTICIPACAO
247
- dtype: string
248
- - name: HABILITACAO
249
- dtype: string
250
- - name: CREDENCIAMENTO
251
- dtype: string
252
- splits:
253
- - name: train
254
- num_bytes: 4248532882
255
- num_examples: 373650
256
- download_size: 1787451169
257
- dataset_size: 4248532882
258
- configs:
259
- - config_name: bidCorpus_NER_keyphrase
260
- data_files:
261
- - split: train
262
- path: bidCorpus_NER_keyphrase/train-*
263
- - split: test
264
- path: bidCorpus_NER_keyphrase/test-*
265
- - split: validation
266
- path: bidCorpus_NER_keyphrase/validation-*
267
- - config_name: bidCorpus_gold
268
- data_files:
269
- - split: train
270
- path: bidCorpus_gold/train-*
271
- - split: test
272
- path: bidCorpus_gold/test-*
273
- - split: validation
274
- path: bidCorpus_gold/validation-*
275
- - config_name: bidCorpus_object_similarity
276
- data_files:
277
- - split: train
278
- path: bidCorpus_object_similarity/train-*
279
- - split: test
280
- path: bidCorpus_object_similarity/test-*
281
- - split: validation
282
- path: bidCorpus_object_similarity/validation-*
283
- - config_name: bidCorpus_objects_correct_allowed
284
- data_files:
285
- - split: train
286
- path: bidCorpus_objects_correct_allowed/train-*
287
- - split: test
288
- path: bidCorpus_objects_correct_allowed/test-*
289
- - split: validation
290
- path: bidCorpus_objects_correct_allowed/validation-*
291
- - config_name: bidCorpus_objects_type
292
- data_files:
293
- - split: train
294
- path: bidCorpus_objects_type/train-*
295
- - split: test
296
- path: bidCorpus_objects_type/test-*
297
- - split: validation
298
- path: bidCorpus_objects_type/validation-*
299
- - config_name: bidCorpus_qual_model
300
- data_files:
301
- - split: train
302
- path: bidCorpus_qual_model/train-*
303
- - split: test
304
- path: bidCorpus_qual_model/test-*
305
- - split: validation
306
- path: bidCorpus_qual_model/validation-*
307
- - config_name: bidCorpus_qual_weak_sup
308
- data_files:
309
- - split: train
310
- path: bidCorpus_qual_weak_sup/train-*
311
- - split: test
312
- path: bidCorpus_qual_weak_sup/test-*
313
- - split: validation
314
- path: bidCorpus_qual_weak_sup/validation-*
315
- - config_name: bidCorpus_sections_type
316
- data_files:
317
- - split: train
318
- path: bidCorpus_sections_type/train-*
319
- - split: test
320
- path: bidCorpus_sections_type/test-*
321
- - split: validation
322
- path: bidCorpus_sections_type/validation-*
323
- - config_name: bidCorpus_sections_type_cleaned
324
- data_files:
325
- - split: train
326
- path: bidCorpus_sections_type_cleaned/train-*
327
- - config_name: bid_corpus_raw
328
- data_files:
329
- - split: train
330
- path: bid_corpus_raw/train-*
331
- tags:
332
- - legal
333
- ---
334
-
335
- # Dataset Card for "BidCorpus"
336
-
337
- ## Table of Contents
338
- - [Dataset Description](#dataset-description)
339
- - [Dataset Summary](#dataset-summary)
340
- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
341
- - [Languages](#languages)
342
- - [Dataset Structure](#dataset-structure)
343
- - [Data Instances](#data-instances)
344
- - [Data Fields](#data-fields)
345
- - [Data Splits](#data-splits)
346
- - [Dataset Creation](#dataset-creation)
347
- - [Curation Rationale](#curation-rationale)
348
- - [Source Data](#source-data)
349
- - [Annotations](#annotations)
350
- - [Personal and Sensitive Information](#personal-and-sensitive-information)
351
- - [Considerations for Using the Data](#considerations-for-using-the-data)
352
- - [Social Impact of Dataset](#social-impact-of-dataset)
353
- - [Discussion of Biases](#discussion-of-biases)
354
- - [Other Known Limitations](#other-known-limitations)
355
- - [Additional Information](#additional-information)
356
- - [Dataset Curators](#dataset-curators)
357
- - [Licensing Information](#licensing-information)
358
- - [Citation Information](#citation-information)
359
- - [Contributions](#contributions)
360
-
361
- ## Dataset Description
362
-
363
- - **Homepage:**
364
- - **Repository:**
365
- - **Paper:**
366
- - **Leaderboard:**
367
- - **Point of Contact:**
368
-
369
- ### Dataset Summary
370
-
371
- 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.
372
-
373
- ### Supported Tasks and Leaderboards
374
-
375
- The supported tasks are the following:
376
-
377
- <table>
378
- <tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Task Type</td><td>Classes</td></tr>
379
- <tr><td>bidCorpus_NER_keyphrase</td><td><a href="">-</a></td><td>Seção Objeto de Editais de Licitação</td><td>Named Entity Recognition</td><td>4</td></tr>
380
- <tr><td>bidCorpus_gold</td><td><a href="">-</a></td><td>Seção de Habilitação de Editais de Licitação</td><td>Multi-label Classification</td><td>7</td></tr>
381
- <tr><td>bidCorpus_object_similarity</td><td><a href="">-</a></td><td>Seção Objeto de Editais de Licitação</td><td>Sentence Similarity</td><td>2</td></tr>
382
- <tr><td>bidCorpus_objects_correct_allowed</td><td><a href="">-</a></td><td>Seção Objeto de Editais de Licitação</td><td>Multi-class Classification</td><td>4</td></tr>
383
- <tr><td>bidCorpus_objects_type</td><td><a href="">-</a></td><td>Seção Objeto de Editais de Licitação</td><td>Multi-class Classification</td><td>4</td></tr>
384
- <tr><td>bidCorpus_qual_model</td><td><a href="">-</a></td><td>Seção de Habilitação de Editais de Licitação</td><td>Multi-label Classification</td><td>7</td></tr>
385
- <tr><td>bidCorpus_qual_weak_sup</td><td><a href="">-</a></td><td>Seção de Habilitação de Editais de Licitação</td><td>Multi-label Classification</td><td>7</td></tr>
386
- <tr><td>bidCorpus_sections_type</td><td><a href="">-</a></td><td>Seções de Editais de Licitação</td><td>Multi-label Classification</td><td>5</td></tr>
387
- <tr><td>bid_corpus_raw</td><td><a href="">-</a></td><td>Seções de Editais de Licitação</td><td>n/a</td><td>n/a</td></tr>
388
- </table>
389
-
390
- #### bidCorpus_NER_keyphrase
391
-
392
- 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.
393
- 1. **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).
394
- 2. **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).
395
-
396
- 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.
397
-
398
- #### bidCorpus_gold
399
-
400
- 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.
401
-
402
- 1. **Certidão de Protesto**: Verification of any protests in the company's name.
403
- 2. **Certificado de Boas Práticas**: Assessment of adherence to recommended practices in the sector.
404
- 3. **Comprovante de Localização**: Confirmation of the company's physical address.
405
- 4. **Idoneidade Financeira**: Analysis of the company's financial health.
406
- 5. **Integralização de Capital**: Verification of the company's capital stock integration.
407
- 6. **Licença Ambiental**: Evaluation of compliance with environmental regulations.
408
- 7. **Limitação de Atestados**: Verification of the minimum and maximum number of certificates required.
409
-
410
- 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.
411
-
412
- #### bidCorpus_object_similarity
413
-
414
- 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:
415
-
416
- - **1**: The sections are similar.
417
- - **0**: The sections are not similar.
418
-
419
- 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.
420
-
421
- 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.
422
-
423
-
424
- #### bidCorpus_objects_correct_allowed
425
-
426
- This dataset focuses on two classifications related to the "object" section of bidding notices:
427
-
428
- 1. **Object Classification**: Determines whether a section is the "object" section of a bidding notice.
429
- 2. **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.
430
-
431
- The dataset provides labels for these classifications to support the analysis of compliance and requirements in bidding documents.
432
-
433
- #### bidCorpus_objects_type
434
-
435
- 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:
436
-
437
- - **Consumables**: Items that are used up or consumed during use, such as office supplies or food products.
438
- - **Permanent Assets**: Items with a longer lifespan that are intended for repeated use, such as machinery or equipment.
439
- - **Services**: Non-tangible activities provided to fulfill a need, such as consulting or maintenance services.
440
- - **Engineering Works**: Projects related to construction, infrastructure, or other engineering tasks.
441
-
442
- 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.
443
-
444
- #### bidCorpus_qual_model
445
-
446
- 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.
447
-
448
- 1. **Certidão de Protesto**: Verification of any protests in the company's name.
449
- 2. **Certificado de Boas Práticas**: Assessment of adherence to recommended practices in the sector.
450
- 3. **Comprovante de Localização**: Confirmation of the company's physical address.
451
- 4. **Idoneidade Financeira**: Analysis of the company's financial health.
452
- 5. **Integralização de Capital**: Verification of the company's capital stock integration.
453
- 6. **Licença Ambiental**: Evaluation of compliance with environmental regulations.
454
- 7. **Limitação de Atestados**: Verification of the minimum and maximum number of certificates required.
455
-
456
- 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.
457
-
458
- 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.
459
-
460
- #### bidCorpus_qual_weak_sup
461
-
462
- 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.
463
-
464
- 1. **Certidão de Protesto**: Verification of any protests in the company's name.
465
- 2. **Certificado de Boas Práticas**: Assessment of adherence to recommended practices in the sector.
466
- 3. **Comprovante de Localização**: Confirmation of the company's physical address.
467
- 4. **Idoneidade Financeira**: Analysis of the company's financial health.
468
- 5. **Integralização de Capital**: Verification of the company's capital stock integration.
469
- 6. **Licença Ambiental**: Evaluation of compliance with environmental regulations.
470
- 7. **Limitação de Atestados**: Verification of the minimum and maximum number of certificates required.
471
-
472
- 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.
473
-
474
- 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.
475
-
476
- #### bidCorpus_sections_type
477
-
478
- This dataset classifies different types of sections in bidding notices. The sections are categorized into the following labels:
479
-
480
- - **Habilitação**: Qualification section, where eligibility criteria and requirements are outlined.
481
- - **Julgamento**: Evaluation section, detailing the criteria and process for assessing bids.
482
- - **Objeto**: Object section, specifying the item or service being procured.
483
- - **Outros**: Other sections that do not fall into the categories above.
484
- - **Credenciamento**: Accreditation section, where the process for validating and registering vendors is described.
485
-
486
- The dataset provides a systematic approach to categorize the various sections found in bidding notices, facilitating better organization and analysis of procurement documents.
487
-
488
- #### bid_corpus_raw
489
-
490
- This dataset consists of raw, unlabeled texts from sections of bidding notices. The sections included are:
491
-
492
- - **Objeto**: Describes the item or service being procured.
493
- - **Julgamento**: Outlines the criteria and process for evaluating bids.
494
- - **Credenciamento**: Details the procedures for vendor registration and validation.
495
- - **Condições de Participação**: Specifies the conditions required for participation in the bidding process.
496
- - **Habilitação**: Provides information on the qualifications and eligibility criteria for bidders.
497
-
498
- 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.
499
-
500
- <!-- *Task-wise Test Results*
501
-
502
- <table>
503
- <tr><td><b>Dataset</b></td><td><b>ECtHR A</b></td><td><b>ECtHR B</b></td><td><b>SCOTUS</b></td><td><b>EUR-LEX</b></td><td><b>LEDGAR</b></td><td><b>UNFAIR-ToS</b></td><td><b>CaseHOLD</b></td></tr>
504
- <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1</td><td>μ-F1 / m-F1 </td></tr>
505
- <tr><td>TFIDF+SVM</td><td> 64.7 / 51.7 </td><td>74.6 / 65.1 </td><td> <b>78.2</b> / <b>69.5</b> </td><td>71.3 / 51.4 </td><td>87.2 / 82.4 </td><td>95.4 / 78.8</td><td>n/a </td></tr>
506
- <tr><td colspan="8" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr>
507
- <td>BERT</td> <td> 71.2 / 63.6 </td> <td> 79.7 / 73.4 </td> <td> 68.3 / 58.3 </td> <td> 71.4 / 57.2 </td> <td> 87.6 / 81.8 </td> <td> 95.6 / 81.3 </td> <td> 70.8 </td> </tr>
508
- <td>RoBERTa</td> <td> 69.2 / 59.0 </td> <td> 77.3 / 68.9 </td> <td> 71.6 / 62.0 </td> <td> 71.9 / <b>57.9</b> </td> <td> 87.9 / 82.3 </td> <td> 95.2 / 79.2 </td> <td> 71.4 </td> </tr>
509
- <td>DeBERTa</td> <td> 70.0 / 60.8 </td> <td> 78.8 / 71.0 </td> <td> 71.1 / 62.7 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.1 </td> <td> 95.5 / 80.3 </td> <td> 72.6 </td> </tr>
510
- <td>Longformer</td> <td> 69.9 / 64.7 </td> <td> 79.4 / 71.7 </td> <td> 72.9 / 64.0 </td> <td> 71.6 / 57.7 </td> <td> 88.2 / 83.0 </td> <td> 95.5 / 80.9 </td> <td> 71.9 </td> </tr>
511
- <td>BigBird</td> <td> 70.0 / 62.9 </td> <td> 78.8 / 70.9 </td> <td> 72.8 / 62.0 </td> <td> 71.5 / 56.8 </td> <td> 87.8 / 82.6 </td> <td> 95.7 / 81.3 </td> <td> 70.8 </td> </tr>
512
- <td>Legal-BERT</td> <td> 70.0 / 64.0 </td> <td> <b>80.4</b> / <b>74.7</b> </td> <td> 76.4 / 66.5 </td> <td> <b>72.1</b> / 57.4 </td> <td> 88.2 / 83.0 </td> <td> <b>96.0</b> / <b>83.0</b> </td> <td> 75.3 </td> </tr>
513
- <td>CaseLaw-BERT</td> <td> 69.8 / 62.9 </td> <td> 78.8 / 70.3 </td> <td> 76.6 / 65.9 </td> <td> 70.7 / 56.6 </td> <td> 88.3 / 83.0 </td> <td> <b>96.0</b> / 82.3 </td> <td> <b>75.4</b> </td> </tr>
514
- <tr><td colspan="8" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr>
515
- <tr><td>RoBERTa</td> <td> <b>73.8</b> / <b>67.6</b> </td> <td> 79.8 / 71.6 </td> <td> 75.5 / 66.3 </td> <td> 67.9 / 50.3 </td> <td> <b>88.6</b> / <b>83.6</b> </td> <td> 95.8 / 81.6 </td> <td> 74.4 </td> </tr>
516
- </table>
517
-
518
- *Averaged (Mean over Tasks) Test Results*
519
-
520
- <table>
521
- <tr><td><b>Averaging</b></td><td><b>Arithmetic</b></td><td><b>Harmonic</b></td><td><b>Geometric</b></td></tr>
522
- <tr><td><b>Model</b></td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td><td>μ-F1 / m-F1 </td></tr>
523
- <tr><td colspan="4" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr>
524
- <tr><td>BERT</td><td> 77.8 / 69.5 </td><td> 76.7 / 68.2 </td><td> 77.2 / 68.8 </td></tr>
525
- <tr><td>RoBERTa</td><td> 77.8 / 68.7 </td><td> 76.8 / 67.5 </td><td> 77.3 / 68.1 </td></tr>
526
- <tr><td>DeBERTa</td><td> 78.3 / 69.7 </td><td> 77.4 / 68.5 </td><td> 77.8 / 69.1 </td></tr>
527
- <tr><td>Longformer</td><td> 78.5 / 70.5 </td><td> 77.5 / 69.5 </td><td> 78.0 / 70.0 </td></tr>
528
- <tr><td>BigBird</td><td> 78.2 / 69.6 </td><td> 77.2 / 68.5 </td><td> 77.7 / 69.0 </td></tr>
529
- <tr><td>Legal-BERT</td><td> <b>79.8</b> / <b>72.0</b> </td><td> <b>78.9</b> / <b>70.8</b> </td><td> <b>79.3</b> / <b>71.4</b> </td></tr>
530
- <tr><td>CaseLaw-BERT</td><td> 79.4 / 70.9 </td><td> 78.5 / 69.7 </td><td> 78.9 / 70.3 </td></tr>
531
- <tr><td colspan="4" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr>
532
- <tr><td>RoBERTa</td><td> 79.4 / 70.8 </td><td> 78.4 / 69.1 </td><td> 78.9 / 70.0 </td></tr>
533
- </table> -->
534
-
535
- ### Languages
536
-
537
- We considered only datasets in Portuguese.
538
-
539
- ## Dataset Structure
540
-
541
- ### Data Instances
542
-
543
- #### bidCorpus_NER_keyphrase
544
-
545
- An example of 'train' looks as follows.
546
- ```json
547
- {
548
- "tokens": ["constitui", "objeto", "do", "presente", "edital", "a", "contratacao", "de", "empresa", "de", "engenharia", "para", "execucao", "da", "obra", "e", "/", "ou", "servico", "de", "elaboracao", "de", "plano", "diretor", "de", "arborizacao", "urbana", "de", "teresina", "-", "pi", ".", "a", "forma", "pela", "qual", "deverao", "ser", "executados", "os", "servicos", "licitados", "e", "as", "diversas", "obrigacoes", "dos", "licitantes", "e", "do", "adjudicatario", "do", "objeto", "desta", "licitacao", "estao", "registradas", "neste", "edital", ",", "no", "termo", "de", "referencia", "e", "minuta", "do", "contrato", "e", "demais", "anexos", "que", ",", "igualmente", ",", "integram", "as", "de", "informacoes", "sobre", "a", "licitacao", "."]
549
- "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
550
- }
551
- ```
552
-
553
- #### bidCorpus_gold
554
-
555
- An example of 'train' looks as follows.
556
- ```json
557
- {
558
- "text": ["para se habilitarem ao presente convite, os interessados deverao apresentar os documentos abaixo relacionados, nos termos dos artigos 27 a 31 e 32, paragrafo 1, da lei numero 666/93, atraves de seus representantes, no local, data e horario indicados no preambulo deste edital, em envelope inteiramente fechado, contendo em sua parte externa, alem da razao social e endereco da licitante, os seguintes dizeres: prefeitura municipal de angical ..."]
559
- "labels": "certidao_protesto": 0, "certificado_boas_praticas": 0, "comprovante_localizacao": 0, "idoneidade_financeira": 0, "integralizado": 0, "licenca_ambiental": 0, "n_min_max_limitacao_atestados": 0
560
- }
561
- ```
562
-
563
- #### bidCorpus_object_similarity
564
-
565
- An example of 'train' looks as follows.
566
- ```json
567
- {
568
- "nerObjeto1": ["execucao dos servicos de reforma e ampliacao da escola reunida francisco"],
569
- "nerObjeto2": ["execucao dos servicos de reforma da escola municipal"],
570
- "humanScore": 1.0,
571
- "bertscore_ner": 1
572
- }
573
- ```
574
-
575
- #### bidCorpus_objects_correct_allowed
576
-
577
- An example of 'train' looks as follows.
578
- ```json
579
- {
580
- "text": ["A presente licitação tem por objeto, selecionar empresas do ramo pertinente, Fornecimento de Lanches, marmitas para atender necessidade das Secretarias e Programa do Município com entrega parcelada ..."],
581
- "corretude": 1,
582
- "permitido": 0
583
- }
584
- ```
585
-
586
- #### bidCorpus_objects_type
587
-
588
- An example of 'train' looks as follows.
589
- ```json
590
- {
591
- "text": ["destina - se a presente licitacao a prestacao de servicos de pavimentacao em paralelepipedo, conforme especificacoes e quantidades constantes do anexo <numero> sao ..."],
592
- "label": 0
593
- }
594
- ```
595
-
596
- #### bidCorpus_qual_model
597
-
598
- An example of 'train' looks as follows.
599
- ```json
600
- {
601
- "text": ["regras gerais. 1 os documentos de habilitacao deverao ser enviados concomitantemente com o envio da proposta, conforme item 9 deste edital 2 havendo a necessidade de envio de documentos de habilitacao complementares ..."],
602
- "certidao_protesto": 0, "certificado_boas_praticas": 0, "comprovante_localizacao": 0, "idoneidade_financeira": 0, "integralizado": 0, "licenca_ambiental": 0, "n_min_max_limitacao_atestados": 0
603
- }
604
- ```
605
-
606
- #### bidCorpus_qual_weak_sup
607
-
608
- An example of 'train' looks as follows.
609
- ```json
610
- {
611
- "text": ["os licitantes encaminharao, exclusivamente por meio do sistema, concomitantemente com os documentos de habilitacao. exigidos no edital, proposta com a descricao ..."],
612
- "certidao_protesto": 0, "certificado_boas_praticas": 0, "comprovante_localizacao": 0, "idoneidade_financeira": 0, "integralizado": 0, "licenca_ambiental": 0, "n_min_max_limitacao_atestados": 0
613
- }
614
- ```
615
-
616
- #### bidCorpus_sections_type
617
-
618
- An example of 'train' looks as follows.
619
- ```json
620
- {
621
- "text": ["IMPUGNAÇÃO DO ATO CONVOCATÓRIO 5.1 No prazo de até 03 (três) dias úteis, antes da data fixada para abertura da Sessão Pública, qualquer pessoa poderá solicitar esclarecimentos e providências sobre o ato convocatório deste pregão ..."],
622
- "label": "outros"
623
- }
624
- ```
625
-
626
- #### bid_corpus_raw
627
-
628
- An example of 'train' looks as follows.
629
- ```json
630
- {
631
- "ID-LICITACAO": 910809.0,
632
- "ID-ARQUIVO": 745202022.0,
633
- "OBJETO": "Artigo 20 Definição do Objeto\n1 – O objeto da licitação deve ser definido pela unidade ...",
634
- "JULGAMENTO":"Artigo 46 Disposições gerais 1 – As licitações podem adotar os modos de disputa aberto, fechado ou combinado, que deve ...",
635
- "CONDICAO_PARTICIPACAO": "5.1 - A participação no certame se dará por meio da digitação da senha pessoal e intransferível do representante ...",
636
- "HABILITACAO": "6.1 - Os proponentes encaminharão, exclusivamente por meio do sistema eletrônico, os documentos de habilitação exigidos no edital, proposta ...",
637
- "CREDENCIAMENTO": "4.1 - O credenciamento é o nível básico do registro cadastral no SICAF, que permite a participação dos interessados na modalidade licitatória ..."
638
- }
639
- ```
640
-
641
- ### Data Fields
642
-
643
- #### bidCorpus_NER_keyphrase
644
-
645
- - `tokens`: a list of `string` features (list of tokens in a text).
646
- - `ner_tags`: a list of classification labels (a list of named entity recognition tags).
647
- <details>
648
- <summary>List of NER tags</summary>
649
- `O`, `B-LOCAL`, `I-LOCAL`, `B-OBJETO`, `I-OBJETO`
650
- </details>
651
-
652
- #### bidCorpus_gold
653
-
654
- - `text`: a `string` feature (string of factual paragraphs from the case description).
655
- - `certidao_protesto`: a 'int64` feature (indicates the presence or absence of a protest certificate).
656
- - `certificado_boas_praticas`: a 'int64` feature (indicates the presence or absence of a good practices certificate).
657
- - `comprovante_localizacao`: a 'int64` feature (indicates the presence or absence of a location proof).
658
- - `idoneidade_financeira`: a 'int64` feature (indicates the presence or absence of financial soundness).
659
- - `integralizado`: a 'int64` feature (indicates the presence or absence of full completion).
660
- - `licenca_ambiental`: a 'int64` feature (indicates the presence or absence of an environmental license).
661
- - `n_min_max_limitacao_atestados`: a 'int64` feature (indicates the presence or absence of limitation of certificates).
662
-
663
- #### bidCorpus_object_similarity
664
-
665
- - `objeto1`: a `string` feature (first object for comparison).
666
- - `nerObjeto1`: a `string` feature (NER tags for the first object).
667
- - `objeto2`: a `string` feature (second object for comparison).
668
- - `nerObjeto2`: a `string` feature (NER tags for the second object).
669
- - `humanScore`: a `float64` feature (human-provided similarity score).
670
- - `nerObjeto1_words`: a `int64` feature (number of words in the first object with NER tags).
671
- - `objeto1_words`: a `int64` feature (number of words in the first object).
672
- - `percentual_words`: a `float64` feature (percentage of similar words).
673
- - `nerObjeto2_words`: a 'int64` feature (number of words in the second object with NER tags).
674
- - `objeto2_words`: a `int64` feature (number of words in the second object).
675
- - `bertscore_ner`: a 'int64` feature (BERT score for NER).
676
- - `bertscore_objs`: a 'int64` feature (BERT score for objects).
677
-
678
- #### bidCorpus_objects_correct_allowed
679
-
680
- - `text`: a list of `string` features (list of factual paragraphs from the case description).
681
- - `corretude`: a list of `int64` features (correctness score).
682
- - `permitido`: a list of `int64` features (allowed score).
683
-
684
- #### bidCorpus_objects_type
685
-
686
- - `text`: a list of `string` features (list of factual paragraphs from the case description).
687
- - `label`: a list of `int64` features (classification labels for object types).
688
-
689
- #### bidCorpus_qual_model
690
-
691
- - `text`: a list of `string` features (list of factual paragraphs from the case description).
692
- - `certidao_protesto`: a list of `int64` features (presence or absence of protest certificate).
693
- - `certificado_boas_praticas`: a list of `int64` features (presence or absence of good practices certificate).
694
- - `comprovante_localizacao`: a list of `int64` features (presence or absence of location proof).
695
- - `idoneidade_financeira`: a list of `int64` features (presence or absence of financial soundness).
696
- - `integralizado`: a list of `int64` features (presence or absence of full completion).
697
- - `licenca_ambiental`: a list of `int64` features (presence or absence of environmental license).
698
- - `n_min_max_limitacao_atestados`: a list of `int64` features (presence or absence of limitation of certificates).
699
-
700
- #### bidCorpus_qual_weak_sup
701
-
702
- - `text`: a list of `string` features (list of factual paragraphs from the case description).
703
- - `certidao_protesto`: a list of `int64` features (presence or absence of protest certificate).
704
- - `certificado_boas_praticas`: a list of `int64` features (presence or absence of good practices certificate).
705
- - `comprovante_localizacao`: a list of `int64` features (presence or absence of location proof).
706
- - `idoneidade_financeira`: a list of `int64` features (presence or absence of financial soundness).
707
- - `integralizado`: a list of `int64` features (presence or absence of full completion).
708
- - `licenca_ambiental`: a list of `int64` features (presence or absence of environmental license).
709
- - `n_min_max_limitacao_atestados`: a list of `int64` features (presence or absence of limitation of certificates).
710
-
711
- #### bidCorpus_sections_type
712
-
713
- - `text`: a list of `string` features (list of factual paragraphs from the case description).
714
- - `label`: a list of `string` features (classification labels for sections types).
715
-
716
- #### bid_corpus_raw
717
-
718
- - `ID-LICITACAO`: a list of `float64` features (auction ID).
719
- - `ID-ARQUIVO`: a list of `float64` features (file ID).
720
- - `OBJETO`: a list of `string` features (object of the auction).
721
- - `JULGAMENTO`: a list of `string` features (judgment details).
722
- - `CONDICAO_PARTICIPACAO`: a list of `string` features (participation conditions).
723
- - `HABILITACAO`: a list of `string` features (qualification details).
724
- - `CREDENCIAMENTO`: a list of `string` features (accreditation details).
725
-
726
-
727
-
728
- ### Data Splits
729
-
730
- <table>
731
- <tr>
732
- <td>Dataset</td>
733
- <td>Training</td>
734
- <td>Development</td>
735
- <td>Test</td>
736
- <td>Total</td>
737
- </tr>
738
- <tr>
739
- <td>bidCorpus_NER_keyphrase</td>
740
- <td>1,632</td>
741
- <td>204</td>
742
- <td>204</td>
743
- <td>2,040</td>
744
- </tr>
745
- <tr>
746
- <td>bidCorpus_gold</td>
747
- <td>1,454</td>
748
- <td>182</td>
749
- <td>182</td>
750
- <td>1,818</td>
751
- </tr>
752
- <tr>
753
- <td>bidCorpus_object_similarity</td>
754
- <td>1,403</td>
755
- <td>175</td>
756
- <td>176</td>
757
- <td>1,754</td>
758
- </tr>
759
- <tr>
760
- <td>bidCorpus_objects_correct_allowed</td>
761
- <td>1,089</td>
762
- <td>136</td>
763
- <td>137</td>
764
- <td>1,362</td>
765
- </tr>
766
- <tr>
767
- <td>bidCorpus_objects_type</td>
768
- <td>1,709</td>
769
- <td>214</td>
770
- <td>214</td>
771
- <td>2,137</td>
772
- </tr>
773
- <tr>
774
- <td>bidCorpus_qual_model</td>
775
- <td>177,133</td>
776
- <td>22,142</td>
777
- <td>22,142</td>
778
- <td>221,417</td>
779
- </tr>
780
- <tr>
781
- <td>bidCorpus_qual_weak_sup</td>
782
- <td>177,133</td>
783
- <td>22,142</td>
784
- <td>22,142</td>
785
- <td>221,417</td>
786
- </tr>
787
- <tr>
788
- <td>bidCorpus_sections_type</td>
789
- <td>177,133</td>
790
- <td>22,142</td>
791
- <td>22,142</td>
792
- <td>221,417</td>
793
- </tr>
794
- <tr>
795
- <td>bid_corpus_raw</td>
796
- <td>373,650</td>
797
- <td>N/A</td>
798
- <td>N/A</td>
799
- <td>373,650</td>
800
- </tr>
801
- </table>
802
-
803
-
804
- ## Dataset Creation
805
-
806
- ### Curation Rationale
807
-
808
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
809
-
810
- ### Source Data
811
-
812
- #### Initial Data Collection and Normalization
813
-
814
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
815
-
816
- #### Who are the source language producers?
817
-
818
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
819
-
820
- ### Annotations
821
-
822
- #### Annotation process
823
-
824
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
825
-
826
- #### Who are the annotators?
827
-
828
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
829
-
830
- ### Personal and Sensitive Information
831
-
832
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
833
-
834
- ## Considerations for Using the Data
835
-
836
- ### Social Impact of Dataset
837
-
838
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
839
-
840
-
841
- ### Discussion of Biases
842
-
843
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
844
-
845
-
846
- ### Other Known Limitations
847
-
848
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
849
-
850
-
851
- ## Additional Information
852
-
853
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
854
-
855
-
856
- ### Dataset Curators
857
-
858
- ### Licensing Information
859
-
860
- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
861
-
862
- ### Citation Information
863
-
864
-
865
- ### Contributions
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bidCorpus_NER_keyphrase/{test-00000-of-00001.parquet → test/0000.parquet} RENAMED
File without changes
bidCorpus_NER_keyphrase/{train-00000-of-00001.parquet → train/0000.parquet} RENAMED
File without changes
bidCorpus_NER_keyphrase/{validation-00000-of-00001.parquet → validation/0000.parquet} RENAMED
File without changes