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README.md

@@ -1,199 +1,80 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-4.0
+base_model: eduagarcia/RoBERTaLexPT-base
+tags:
+- generated_from_trainer
+datasets:
+- ulysses_ner_br
+model-index:
+- name: robertalex-ptbr-ulyssesner
+  results: []
 ---
 
-# Model Card for Model ID
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-<!-- Provide a quick summary of what the model is/does. -->
+# robertalex-ptbr-ulyssesner
 
+This model is a fine-tuned version of [eduagarcia/RoBERTaLexPT-base](https://huggingface.co/eduagarcia/RoBERTaLexPT-base) on the ulysses_ner_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0711
+- Data: {'precision': 0.96, 'recall': 1.0, 'f1': 0.9795918367346939, 'number': 72}
+- Evento: {'precision': 0.6666666666666666, 'recall': 0.4, 'f1': 0.5, 'number': 5}
+- Fundamento: {'precision': 0.7967479674796748, 'recall': 0.9158878504672897, 'f1': 0.8521739130434782, 'number': 107}
+- Local: {'precision': 0.950354609929078, 'recall': 0.9241379310344827, 'f1': 0.9370629370629371, 'number': 145}
+- Organizacao: {'precision': 0.75, 'recall': 0.8888888888888888, 'f1': 0.8135593220338982, 'number': 81}
+- Pessoa: {'precision': 0.823076923076923, 'recall': 0.9385964912280702, 'f1': 0.8770491803278688, 'number': 114}
+- Produtodelei: {'precision': 0.6470588235294118, 'recall': 0.717391304347826, 'f1': 0.6804123711340206, 'number': 46}
+- Overall Precision: 0.8368
+- Overall Recall: 0.9088
+- Overall F1: 0.8713
+- Overall Accuracy: 0.9860
 
+## Model description
 
-## Model Details
+More information needed
 
-### Model Description
+## Intended uses & limitations
 
-<!-- Provide a longer summary of what this model is. -->
+More information needed
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Training and evaluation data
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+More information needed
 
-### Model Sources [optional]
+## Training procedure
 
-<!-- Provide the basic links for the model. -->
+### Training hyperparameters
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
 
-## Uses
+### Training results
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+| Training Loss | Epoch | Step | Validation Loss | Data                                                                                                    | Evento                                                                    | Fundamento                                                                                               | Local                                                                                                    | Organizacao                                                                                              | Pessoa                                                                                                      | Produtodelei                                                                                            | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.4776        | 1.0   | 71   | 0.2170          | {'precision': 1.0, 'recall': 0.4166666666666667, 'f1': 0.5882352941176471, 'number': 72}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}                 | {'precision': 0.5714285714285714, 'recall': 0.5607476635514018, 'f1': 0.5660377358490566, 'number': 107} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145}                                              | {'precision': 0.13445378151260504, 'recall': 0.5925925925925926, 'f1': 0.2191780821917808, 'number': 81} | {'precision': 0.16793893129770993, 'recall': 0.19298245614035087, 'f1': 0.17959183673469387, 'number': 114} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 46}                                              | 0.2528            | 0.2807         | 0.2660     | 0.9344           |
+| 0.124         | 2.0   | 142  | 0.0854          | {'precision': 0.8666666666666667, 'recall': 0.9027777777777778, 'f1': 0.8843537414965987, 'number': 72} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}                 | {'precision': 0.7165354330708661, 'recall': 0.8504672897196262, 'f1': 0.7777777777777777, 'number': 107} | {'precision': 0.8187919463087249, 'recall': 0.8413793103448276, 'f1': 0.8299319727891157, 'number': 145} | {'precision': 0.6078431372549019, 'recall': 0.7654320987654321, 'f1': 0.6775956284153005, 'number': 81}  | {'precision': 0.8303571428571429, 'recall': 0.8157894736842105, 'f1': 0.8230088495575222, 'number': 114}    | {'precision': 0.6590909090909091, 'recall': 0.6304347826086957, 'f1': 0.6444444444444444, 'number': 46} | 0.7586            | 0.8105         | 0.7837     | 0.9783           |
+| 0.0463        | 3.0   | 213  | 0.0699          | {'precision': 0.9210526315789473, 'recall': 0.9722222222222222, 'f1': 0.9459459459459458, 'number': 72} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}                 | {'precision': 0.7404580152671756, 'recall': 0.9065420560747663, 'f1': 0.8151260504201681, 'number': 107} | {'precision': 0.9236111111111112, 'recall': 0.9172413793103448, 'f1': 0.9204152249134949, 'number': 145} | {'precision': 0.7156862745098039, 'recall': 0.9012345679012346, 'f1': 0.7978142076502731, 'number': 81}  | {'precision': 0.8048780487804879, 'recall': 0.868421052631579, 'f1': 0.8354430379746836, 'number': 114}     | {'precision': 0.6304347826086957, 'recall': 0.6304347826086957, 'f1': 0.6304347826086957, 'number': 46} | 0.8055            | 0.8789         | 0.8406     | 0.9838           |
+| 0.0277        | 4.0   | 284  | 0.0709          | {'precision': 0.9473684210526315, 'recall': 1.0, 'f1': 0.972972972972973, 'number': 72}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}                 | {'precision': 0.8347826086956521, 'recall': 0.897196261682243, 'f1': 0.8648648648648648, 'number': 107}  | {'precision': 0.9246575342465754, 'recall': 0.9310344827586207, 'f1': 0.9278350515463917, 'number': 145} | {'precision': 0.7553191489361702, 'recall': 0.8765432098765432, 'f1': 0.8114285714285715, 'number': 81}  | {'precision': 0.796875, 'recall': 0.8947368421052632, 'f1': 0.8429752066115702, 'number': 114}              | {'precision': 0.6481481481481481, 'recall': 0.7608695652173914, 'f1': 0.7000000000000001, 'number': 46} | 0.8336            | 0.8965         | 0.8639     | 0.9833           |
+| 0.0165        | 5.0   | 355  | 0.0640          | {'precision': 0.9473684210526315, 'recall': 1.0, 'f1': 0.972972972972973, 'number': 72}                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}                 | {'precision': 0.8448275862068966, 'recall': 0.9158878504672897, 'f1': 0.8789237668161435, 'number': 107} | {'precision': 0.9640287769784173, 'recall': 0.9241379310344827, 'f1': 0.943661971830986, 'number': 145}  | {'precision': 0.8111111111111111, 'recall': 0.9012345679012346, 'f1': 0.8538011695906432, 'number': 81}  | {'precision': 0.7969924812030075, 'recall': 0.9298245614035088, 'f1': 0.8582995951417005, 'number': 114}    | {'precision': 0.673469387755102, 'recall': 0.717391304347826, 'f1': 0.6947368421052631, 'number': 46}   | 0.8543            | 0.9053         | 0.8790     | 0.9848           |
+| 0.0087        | 6.0   | 426  | 0.0612          | {'precision': 0.9594594594594594, 'recall': 0.9861111111111112, 'f1': 0.9726027397260274, 'number': 72} | {'precision': 0.5, 'recall': 0.2, 'f1': 0.28571428571428575, 'number': 5} | {'precision': 0.8048780487804879, 'recall': 0.9252336448598131, 'f1': 0.8608695652173913, 'number': 107} | {'precision': 0.9574468085106383, 'recall': 0.9310344827586207, 'f1': 0.9440559440559441, 'number': 145} | {'precision': 0.8131868131868132, 'recall': 0.9135802469135802, 'f1': 0.8604651162790699, 'number': 81}  | {'precision': 0.8333333333333334, 'recall': 0.9210526315789473, 'f1': 0.875, 'number': 114}                 | {'precision': 0.7083333333333334, 'recall': 0.7391304347826086, 'f1': 0.723404255319149, 'number': 46}  | 0.8579            | 0.9105         | 0.8834     | 0.9873           |
+| 0.0057        | 7.0   | 497  | 0.0691          | {'precision': 0.9473684210526315, 'recall': 1.0, 'f1': 0.972972972972973, 'number': 72}                 | {'precision': 1.0, 'recall': 0.2, 'f1': 0.33333333333333337, 'number': 5} | {'precision': 0.784, 'recall': 0.9158878504672897, 'f1': 0.8448275862068965, 'number': 107}              | {'precision': 0.9375, 'recall': 0.9310344827586207, 'f1': 0.9342560553633218, 'number': 145}             | {'precision': 0.8202247191011236, 'recall': 0.9012345679012346, 'f1': 0.8588235294117647, 'number': 81}  | {'precision': 0.8106060606060606, 'recall': 0.9385964912280702, 'f1': 0.8699186991869918, 'number': 114}    | {'precision': 0.5789473684210527, 'recall': 0.717391304347826, 'f1': 0.6407766990291262, 'number': 46}  | 0.8317            | 0.9105         | 0.8693     | 0.9866           |
+| 0.0042        | 8.0   | 568  | 0.0701          | {'precision': 0.96, 'recall': 1.0, 'f1': 0.9795918367346939, 'number': 72}                              | {'precision': 0.3333333333333333, 'recall': 0.2, 'f1': 0.25, 'number': 5} | {'precision': 0.8181818181818182, 'recall': 0.9252336448598131, 'f1': 0.868421052631579, 'number': 107}  | {'precision': 0.9640287769784173, 'recall': 0.9241379310344827, 'f1': 0.943661971830986, 'number': 145}  | {'precision': 0.7634408602150538, 'recall': 0.8765432098765432, 'f1': 0.8160919540229884, 'number': 81}  | {'precision': 0.828125, 'recall': 0.9298245614035088, 'f1': 0.8760330578512396, 'number': 114}              | {'precision': 0.6538461538461539, 'recall': 0.7391304347826086, 'f1': 0.693877551020408, 'number': 46}  | 0.8462            | 0.9070         | 0.8755     | 0.9863           |
+| 0.0029        | 9.0   | 639  | 0.0713          | {'precision': 0.96, 'recall': 1.0, 'f1': 0.9795918367346939, 'number': 72}                              | {'precision': 0.6666666666666666, 'recall': 0.4, 'f1': 0.5, 'number': 5}  | {'precision': 0.8448275862068966, 'recall': 0.9158878504672897, 'f1': 0.8789237668161435, 'number': 107} | {'precision': 0.9436619718309859, 'recall': 0.9241379310344827, 'f1': 0.9337979094076655, 'number': 145} | {'precision': 0.7708333333333334, 'recall': 0.9135802469135802, 'f1': 0.8361581920903954, 'number': 81}  | {'precision': 0.8294573643410853, 'recall': 0.9385964912280702, 'f1': 0.8806584362139916, 'number': 114}    | {'precision': 0.6415094339622641, 'recall': 0.7391304347826086, 'f1': 0.6868686868686867, 'number': 46} | 0.8485            | 0.9140         | 0.8801     | 0.9860           |
+| 0.0025        | 10.0  | 710  | 0.0711          | {'precision': 0.96, 'recall': 1.0, 'f1': 0.9795918367346939, 'number': 72}                              | {'precision': 0.6666666666666666, 'recall': 0.4, 'f1': 0.5, 'number': 5}  | {'precision': 0.7967479674796748, 'recall': 0.9158878504672897, 'f1': 0.8521739130434782, 'number': 107} | {'precision': 0.950354609929078, 'recall': 0.9241379310344827, 'f1': 0.9370629370629371, 'number': 145}  | {'precision': 0.75, 'recall': 0.8888888888888888, 'f1': 0.8135593220338982, 'number': 81}                | {'precision': 0.823076923076923, 'recall': 0.9385964912280702, 'f1': 0.8770491803278688, 'number': 114}     | {'precision': 0.6470588235294118, 'recall': 0.717391304347826, 'f1': 0.6804123711340206, 'number': 46}  | 0.8368            | 0.9088         | 0.8713     | 0.9860           |
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+### Framework versions
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-## Glossary [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 3.0.0
+- Tokenizers 0.19.1