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# bertimbau-base-finetuned-lener-br-finetuned-brazilian_court_decisions

This model is a fine-tuned version of [Luciano/bertimbau-base-finetuned-lener-br](https://huggingface.co/Luciano/bertimbau-base-finetuned-lener-br) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8017
- Accuracy: 0.7698

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 405  | 0.7790          | 0.6535   |
| 0.8276        | 2.0   | 810  | 0.6739          | 0.7277   |
| 0.5818        | 3.0   | 1215 | 0.8767          | 0.7302   |
| 0.4147        | 4.0   | 1620 | 0.8229          | 0.7896   |
| 0.287         | 5.0   | 2025 | 0.9874          | 0.7921   |
| 0.287         | 6.0   | 2430 | 1.2301          | 0.7772   |
| 0.1727        | 7.0   | 2835 | 1.2864          | 0.7946   |
| 0.1179        | 8.0   | 3240 | 1.5097          | 0.7772   |
| 0.0709        | 9.0   | 3645 | 1.4772          | 0.7921   |
| 0.0437        | 10.0  | 4050 | 1.5581          | 0.7797   |
| 0.0437        | 11.0  | 4455 | 1.6317          | 0.7896   |
| 0.0318        | 12.0  | 4860 | 1.7295          | 0.7822   |
| 0.0158        | 13.0  | 5265 | 1.7333          | 0.7797   |
| 0.0108        | 14.0  | 5670 | 1.8008          | 0.7772   |
| 0.0137        | 15.0  | 6075 | 1.8017          | 0.7698   |


### Framework versions

- Transformers 4.22.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["joelito/brazilian_court_decisions"], "metrics": ["accuracy"], "train-eval-index": [{"config": "joelito--brazilian_court_decisions", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"eval_split": "test"}, "col_mapping": {"decision_description": "text", "judgment_label": "target"}}], "widget": [{"text": "AGRAVO DE INSTRUMENTO. A\u00c7\u00c3O REVISIONAL DE CONTRATO. DEP\u00d3SITO DO VALOR INCONTROVERSO. IMPOSSIBILIDADE. N\u00c3O PREENCHIMENTO DOS REQUISITOS PELO DEVEDOR. MANUTEN\u00c7\u00c3O NA POSSE DO BEM E VEDA\u00c7\u00c3O DE INSCRI\u00c7\u00c3O NOS \u00d3RG\u00c3OS DE PROTE\u00c7\u00c3O AO CR\u00c9DITO MEDIANTE O DEP\u00d3SITO DO VALOR ORIGINALMENTE CONTRATADO. 1. O autor requereu a sua manuten\u00e7\u00e3o na posse do bem, o dep\u00f3sito em ju\u00edzo dos valores que entende incontroversos, a proibi\u00e7\u00e3o de inscri\u00e7\u00e3o de seu nome nos \u00f3rg\u00e3os de prote\u00e7\u00e3o ao cr\u00e9dito e a suspens\u00e3o de eventual a\u00e7\u00e3o de busca e apreens\u00e3o do ve\u00edculo. 2. O artigo 330, PARAGRAFO 2 e PARAGRAFO 3 do CODIGO DE PROCESSO CIVIL autoriza expressamente o dep\u00f3sito do valor incontroverso nas a\u00e7\u00f5es revisionais de contrato mediante presen\u00e7a de tr\u00eas requisitos concomitantes: a) propositura de a\u00e7\u00e3o contestando a exist\u00eancia integral ou parcial do d\u00e9bito; b) efetiva demonstra\u00e7\u00e3o de que a contesta\u00e7\u00e3o da cobran\u00e7a indevida se funda na apar\u00eancia do bom direito; e, c) que a parte efetue o dep\u00f3sito do valor incontroverso ou preste cau\u00e7\u00e3o id\u00f4nea. Contudo, tal conduta n\u00e3o afastar\u00e1 a incid\u00eancia das restri\u00e7\u00f5es legais decorrentes da d\u00edvida, porquanto os valores considerados incontroversos s\u00e3o inferiores \u00e0queles contratados. 3. A jurisprud\u00eancia \u00e9 pac\u00edfica no sentido de ser necess\u00e1rio o dep\u00f3sito do valor integral da parcela da d\u00edvida para inibir os efeitos da mora e suas consequ\u00eancias. 4. Nos termos da jurisprud\u00eancia consolidada nesta Corte, dever\u00e1 o devedor depositar os valores pactuados originalmente no contrato, tanto para as parcelas vencidas, quanto para as vincendas, nas datas pactuadas, se desejar se manter na posse do bem e obstacular sua inscri\u00e7\u00e3o de seu nome nos cadastros restritivos de cr\u00e9dito."}, {"text": "O constrangimento ilegal deve ser aferido segundo as circunstancias do caso concreto, especialmente diante da complexidade e das dilig\u00eancias necess\u00e1rias, ainda mais quando tais elementos s\u00e3o necess\u00e1rios para embasar a pr\u00f3pria den\u00fancia em si. Intensa movimenta\u00e7\u00e3o processual no sentido de angariar as informa\u00e7\u00f5es necess\u00e1rias. Prazo que merece ser mitigado. Den\u00fancia oferecida nos autos da a\u00e7\u00e3o penal 0800162-9820188020001. II - Presentes a materialidade dos delitos e seus ind\u00edcios de autoria, mormente diante das colabora\u00e7\u00f5es premiadas colhidas durante as investiga\u00e7\u00f5es, n\u00e3o h\u00e1 falar em embasamento abstrato da decis\u00e3o especialmente quando esta expressamente apontou que o paciente era propriet\u00e1rio de empresas supostamente fantasmas, que recebiam benef\u00edcios de diversas prefeituras. III - Aus\u00eancia de constrangimento ilegal, tanto pela presen\u00e7a dos requisitos para a pris\u00e3o preventiva em quest\u00e3o, quanto pelo fato de que resta evidenciado que o paciente ficou foragido. Precedentes. IV - Especificamente em rela\u00e7\u00e3o ao pedido de extens\u00e3o dos efeitos dos benef\u00edcios concedidos a outros investigados (Josimar Campos, Raphael de Barros Lima e Raulene Karoline Barros e Gabriel Brand\u00e3o), entendo, a partir do narrado pela pr\u00f3pria peti\u00e7\u00e3o inicial, que a situa\u00e7\u00e3o do paciente \u00e9 diferente dos demais investigados, uma vez que, al\u00e9m de ele ter foragido do distrito da culpa e responder a outras a\u00e7\u00f5es penais, aparentemente n\u00e3o est\u00e1 colaborando com as investiga\u00e7\u00f5es (os outros investigados firmaram acordo de colabora\u00e7\u00e3o premiada). V - No que tange ao pedido relacionado aos ex-prefeitos (M\u00e1rcia Coutinho Nogueira de Albuquerque, Jos\u00e9 Jacob Gomes Brand\u00e3o, F\u00e1bio Rangel Nunes de Oliveira) que se encontram em liberdade, entendo que a situa\u00e7\u00e3o do paciente tamb\u00e9m \u00e9 diferente, uma vez que a decis\u00e3o de origem da conta que existem indicativos de que o esquema tenha ocorrido em 80 (oitenta) prefeituras. VI - A partir da leitura da decis\u00e3o prolatada pelos ju\u00edzes impetrados, verifica-se que o decreto de pris\u00e3o n\u00e3o \u00e9 fundamentado apenas no risco de a liberdade do paciente atrapalhar as investiga\u00e7\u00f5es, mas tamb\u00e9m, para, sobretudo, garantir a aplica\u00e7\u00e3o da lei penal pelo fato de o paciente ter foragido do distrito da culpa."}, {"text": "APELA\u00c7\u00c3O C\u00cdVEL. A\u00c7\u00c3O ORIGIN\u00c1RIA DE USUCAPI\u00c3O. JULGAMENTO ANTECIPADO DA LIDE. 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NULIDADE DA SENTEN\u00c7A COM RETORNO DOS AUTOS \u00c0 ORIGEM PARA DAR SEGUIMENTO A INSTRU\u00c7\u00c3O COM PROLA\u00c7\u00c3O DE NOVA DECIS\u00c3O."}], "base_model": "Luciano/bertimbau-base-finetuned-lener-br", "model-index": [{"name": "bertimbau-base-finetuned-lener-br-finetuned-brazilian_court_decisions", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "joelito/brazilian_court_decisions", "type": "joelito/brazilian_court_decisions", "config": "joelito--brazilian_court_decisions", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.7925925925925926, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGYwNzJiZjE0ZjE1NzM5ZWI2ZWQ3MDZiNTkxNDMzY2IwODJmNjRjMGFjYWNjMzg0MWM0YjZmNjNlODJiMzZiZCIsInZlcnNpb24iOjF9.yuzroeBIxzMUISrKslpXl6mw_pdmZ5rsx1_yBRvneBF5Y18NIwbaSidUJ4A_AsR91yrHStvCi2LV9mi0pSUSBg"}, {"type": "precision", "value": 0.7561026936026934, "name": "Precision Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTU1YTMxOGQ3MjcxZjViNTYxOTFmN2MwYzk1YjAwMTU4NjVmNzEwODBlNWZlN2EzNWI5Y2FmZjE5NmNjODQ4NSIsInZlcnNpb24iOjF9.NC1NGFneaJFl-aA0veGNiaHXhZ7_7Xp14DoCRQkqNuDbjmcEqARaT2zvcfmUlC1KFafqGdA9zGxPyPvLYGD9Dg"}, {"type": "precision", "value": 0.7925925925925926, "name": "Precision Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTIwMzg2ZjkwNzNiZTNjMTg1MTExY2E3NmNhYWM5MGQxYjlkMGNkOTFjMjU5YWRlZGJkOGRmNzUyOTI1MGE4MSIsInZlcnNpb24iOjF9.e-2no4ZzEQh_o2IZLwqAgKnandZ60gkLQwkHQ1chblFJldFJVeiD_VuYPG6oYxT0i6olPZob3soV1qRLOMBOAw"}, {"type": "precision", "value": 0.7913112607557052, "name": "Precision Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGQ3YTE5Y2YzYzQzYjllMDMxMTQ3ODRmNzI1ZWZkZTNmMGFkMjAxZTE3M2FhZGEzMjM3OTcwODg4NjBmMWU2ZSIsInZlcnNpb24iOjF9.uLD9Bqp5E6S0vZSc82RUtB49jOxMWPwqCp18YjDqJQRBpRgm7kyQwxmtenDci9UieuJ0d1DtD2b8sPYyPQ7dDA"}, {"type": "recall", "value": 0.7408785957173055, "name": "Recall Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTM2YTVkNmM1ZGRhNzY0OWYyYTg2NmIxYWRkOWYyMTc4ZWVlYjdmNDliMDlhOTk2MGVjNjJlMjFhZGJmMGYxYyIsInZlcnNpb24iOjF9.KsRzS_NH6bbyIyhKD-P2BPKKtAZjdOXrwZrq_U9zensLOY7C5RZCFGuYESqm0vQwpQf0ZKqKEYCUL9WRwONhBg"}, {"type": "recall", "value": 0.7925925925925926, "name": "Recall Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWUxMzM5OWQwMDg3ZjIxMTFiNzg0MmJkMzYwODU5MjNmMDllZWFjYmMzNTRlNWRhY2NkNmJiNGZkYmU0MjVhNSIsInZlcnNpb24iOjF9.AtDYhA7T8793on76uVa0tbzCKfq1UD4U_pU1Q0vFRST8lDjQYyw6_Q6JzY3Jh9qw1gSU6qA52zMlesb8ANckDw"}, {"type": "recall", "value": 0.7925925925925926, "name": "Recall Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTBiYTYwZmRiZjY3YjM4YmFlMjkwNDVlN2JiOTY2MDgxOWNmYmE1YmJkZGY1NGU5ZDA1MjdjZDgyMzI3NDhlMCIsInZlcnNpb24iOjF9.uXSLCN14oZb6mP0hLTH8RxD6Og6OYrS7OSxRbIqsx_JauIXdca4RFZcXptzeq190gbRWT0lNz5GEhzjeT7qmAA"}, {"type": "f1", "value": 0.7466596343178621, "name": "F1 Macro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzMzNDIwZGMzNmVjZmUxNGVhZGRhOWZkNzI3OWViYmNiOGZkNGI4ZjE5M2U2MjA1OGRjMzE2YzEyODFjNDE3NiIsInZlcnNpb24iOjF9.GHA7gAvoIQQuhhoXMeGuMdGo0f4Pma5AUgCA9T6qDKeXMeuRs6gqumLzi3lXgMBB4RuPxE49NOgVLLyrq-J2Aw"}, {"type": "f1", "value": 0.7925925925925926, "name": "F1 Micro", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzFkNTU5NTM1NjhjNjAwMzQ1ZTNjYzczNDc5MzI1NjI3ODkyMzk5OTExMTBlZDlhYzAzNTkzYzM3ZjU2MTJjMSIsInZlcnNpb24iOjF9.Gw1w7Fsv--XRx0Hxuw6pYAzyhd9vb-n31Y5Mwgh6lbQKEDsBMauztkT3rqPIIqqCLVI3LsaVoLk4ECTAIngwBA"}, {"type": "f1", "value": 0.7908149710892328, "name": "F1 Weighted", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDVkNTU3MmUxYjY0NmFiMGU2ZTM1M2YzYmUxMGM3NDNjOTkxMGY2NzU5ODkwYTc3YTRhMWIwMjE4YzVhMzU0YSIsInZlcnNpb24iOjF9.KrMgoICICm8QeS6usYrHMhQ7PoscE6wyLz9QGnUPpQa0LOr0EcasCpgeK_yyVytBCIR3lT1doYAvKH8mTf9lBA"}, {"type": "loss", "value": 1.6538628339767456, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODZkMWEzODY5ZTA2N2MzMjJkYmQ2NWE4MWJmZWIxMmNjZmVmZTA3Yjk2ZTdjNzFmODIyMThkY2NhOWIzYTgwMCIsInZlcnNpb24iOjF9.IyEKEBbdbNYTcUzOF09r2vp7umxYSIeTGN_muWNujaSbibd6uSooNRWzSfZOS37L0S4_GPdMvDXQuMsUeybWAQ"}]}]}]}
transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer pt dataset:joelito/brazilian_court_decisions base_model:Luciano/bertimbau-base-finetuned-lener-br license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #pt #dataset-joelito/brazilian_court_decisions #base_model-Luciano/bertimbau-base-finetuned-lener-br #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

bertimbau-base-finetuned-lener-br-finetuned-brazilian\_court\_decisions
=======================================================================


This model is a fine-tuned version of Luciano/bertimbau-base-finetuned-lener-br on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8017
* Accuracy: 0.7698


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #pt #dataset-joelito/brazilian_court_decisions #base_model-Luciano/bertimbau-base-finetuned-lener-br #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #pt #dataset-joelito/brazilian_court_decisions #base_model-Luciano/bertimbau-base-finetuned-lener-br #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15### Training results### Framework versions


* Transformers 4.22.0
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1