Update README.md

README.md

@@ -421,6 +421,7 @@ The supported tasks are the following:
 <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>
 <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>
 <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>
+<tr><td>bidCorpus_synthetic</td><td><a href="">-</a></td><td>Seção de Habilitação de Editais de Licitação</td><td>Multi-label Classification</td><td>7</td></tr>
 <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>
 <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>
 </table>
@@ -511,6 +512,20 @@ Unlike the previous expert-annotated dataset, this dataset has been annotated us
 
 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. 
+
+1. **Certidão de Protesto**: Verification of any protests in the company's name.
+2. **Certificado de Boas Práticas**: Assessment of adherence to recommended practices in the sector.
+3. **Comprovante de Localização**: Confirmation of the company's physical address.
+4. **Idoneidade Financeira**: Analysis of the company's financial health.
+5. **Integralização de Capital**: Verification of the company's capital stock integration.
+6. **Licença Ambiental**: Evaluation of compliance with environmental regulations.
+7. **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:
@@ -535,41 +550,6 @@ This dataset consists of raw, unlabeled texts from sections of bidding notices.
 
 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.
 
-<!-- *Task-wise Test Results*
-
-<table>
-        <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>
-<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>
-<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>
-<tr><td colspan="8" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr>
-<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>
-<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>
-<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>
-<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>
-<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>
-<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>
-<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>
-<tr><td colspan="8" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr>
-<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>
-</table>
-
-*Averaged (Mean over Tasks) Test Results*
-
-<table>
-<tr><td><b>Averaging</b></td><td><b>Arithmetic</b></td><td><b>Harmonic</b></td><td><b>Geometric</b></td></tr>
-<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>
-<tr><td colspan="4" style='text-align:center'><b>Medium-sized Models (L=12, H=768, A=12)</b></td></tr>
-<tr><td>BERT</td><td> 77.8 /  69.5 </td><td> 76.7 /  68.2 </td><td> 77.2 /  68.8 </td></tr>
-<tr><td>RoBERTa</td><td> 77.8 /  68.7 </td><td> 76.8 /  67.5 </td><td> 77.3 /  68.1 </td></tr>
-<tr><td>DeBERTa</td><td> 78.3 /  69.7 </td><td> 77.4 /  68.5 </td><td> 77.8 /  69.1 </td></tr>
-<tr><td>Longformer</td><td> 78.5 /  70.5 </td><td> 77.5 /  69.5 </td><td> 78.0 /  70.0 </td></tr>
-<tr><td>BigBird</td><td> 78.2 /  69.6 </td><td> 77.2 /  68.5 </td><td> 77.7 /  69.0 </td></tr>
-<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>
-<tr><td>CaseLaw-BERT</td><td> 79.4 /  70.9 </td><td> 78.5 /  69.7 </td><td> 78.9 /  70.3 </td></tr> 
-<tr><td colspan="4" style='text-align:center'><b>Large-sized Models (L=24, H=1024, A=18)</b></td></tr>
-<tr><td>RoBERTa</td><td> 79.4 /  70.8 </td><td> 78.4 /  69.1 </td><td> 78.9 /  70.0 </td></tr>
-</table> -->
-
 ### Languages
 
 We considered only datasets in Portuguese.
@@ -651,6 +631,16 @@ An example of 'train' looks as follows.
 }
 ```
 
+#### bidCorpus_synthetic
+
+An example of 'train' looks as follows. 
+```json
+{
+  "text": ["os licitantes encaminharao, exclusivamente por meio do sistema, concomitantemente com os documentos de habilitacao. exigidos no edital, proposta com a descricao ..."],
+  "certidao_protesto": 0, "certificado_boas_praticas": 0, "comprovante_localizacao": 0, "idoneidade_financeira": 0, "integralizado": 0, "licenca_ambiental": 0, "n_min_max_limitacao_atestados": 0
+}
+```
+
 #### bidCorpus_sections_type
 
 An example of 'train' looks as follows. 
@@ -746,6 +736,17 @@ An example of 'train' looks as follows.
 - `licenca_ambiental`: a list of `int64` features (presence or absence of environmental license).
 - `n_min_max_limitacao_atestados`: a list of `int64` features (presence or absence of limitation of certificates).
 
+#### bidCorpus_synthetic
+
+- `text`: a list of `string` features (list of factual paragraphs from the case description).
+- `certidao_protesto`: a list of `int64` features (presence or absence of protest certificate).
+- `certificado_boas_praticas`: a list of `int64` features (presence or absence of good practices certificate).
+- `comprovante_localizacao`: a list of `int64` features (presence or absence of location proof).
+- `idoneidade_financeira`: a list of `int64` features (presence or absence of financial soundness).
+- `integralizado`: a list of `int64` features (presence or absence of full completion).
+- `licenca_ambiental`: a list of `int64` features (presence or absence of environmental license).
+- `n_min_max_limitacao_atestados`: a list of `int64` features (presence or absence of limitation of certificates).
+
 #### bidCorpus_sections_type
 
 - `text`: a list of `string` features (list of factual paragraphs from the case description).
@@ -761,8 +762,6 @@ An example of 'train' looks as follows.
 - `HABILITACAO`: a list of `string` features (qualification details).
 - `CREDENCIAMENTO`: a list of `string` features (accreditation details).
 
-
-
 ### Data Splits
 
 <table>
@@ -822,6 +821,13 @@ An example of 'train' looks as follows.
     <td>22,142</td>
     <td>221,417</td>
   </tr>
+  <tr>
+    <td>bidCorpus_synthetic</td>
+    <td>1,454</td>
+    <td>182</td>
+    <td>182</td>
+    <td>1,818</td>
+  </tr>
   <tr>
     <td>bidCorpus_sections_type</td>
     <td>177,133</td>