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# Negation_and_Uncertainty_Scope_Detection_mBERT_fine_tuned

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the NUBES dataset. This is a result of the PhD dissertation of Antonio Tamayo.
It achieves the following results on the evaluation set:
- Loss: 0.1954
- Neg Precision: 0.9056
- Neg Recall: 0.9332
- Neg F1: 0.9192
- Nsco Precision: 0.8973
- Nsco Recall: 0.9079
- Nsco F1: 0.9026
- Unc Precision: 0.864
- Unc Recall: 0.9019
- Unc F1: 0.8825
- Usco Precision: 0.8210
- Usco Recall: 0.8650
- Usco F1: 0.8424
- Precision: 0.8886
- Recall: 0.9127
- F1: 0.9005
- Accuracy: 0.9698

## 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: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Unc Precision | Unc Recall | Unc F1 | Usco Precision | Usco Recall | Usco F1 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.1688        | 1.0   | 1726  | 0.1386          | 0.8885        | 0.9064     | 0.8974 | 0.8706         | 0.8697      | 0.8702  | 0.8326        | 0.8309     | 0.8318 | 0.7694         | 0.8304      | 0.7987  | 0.8621    | 0.8775 | 0.8697 | 0.9633   |
| 0.111         | 2.0   | 3452  | 0.1455          | 0.8803        | 0.9128     | 0.8962 | 0.8726         | 0.8924      | 0.8824  | 0.8244        | 0.8622     | 0.8429 | 0.7391         | 0.8125      | 0.7741  | 0.8547    | 0.8888 | 0.8714 | 0.9624   |
| 0.0678        | 3.0   | 5178  | 0.1621          | 0.9204        | 0.9004     | 0.9103 | 0.9051         | 0.8961      | 0.9006  | 0.8571        | 0.8643     | 0.8607 | 0.8454         | 0.7935      | 0.8187  | 0.9010    | 0.8828 | 0.8918 | 0.9679   |
| 0.043         | 4.0   | 6904  | 0.1590          | 0.9099        | 0.9212     | 0.9155 | 0.8885         | 0.9042      | 0.8963  | 0.8525        | 0.8810     | 0.8665 | 0.8288         | 0.8426      | 0.8356  | 0.8874    | 0.9020 | 0.8946 | 0.9696   |
| 0.0278        | 5.0   | 8630  | 0.1679          | 0.9140        | 0.9272     | 0.9206 | 0.8977         | 0.9057      | 0.9017  | 0.8656        | 0.8873     | 0.8763 | 0.8315         | 0.8594      | 0.8452  | 0.8939    | 0.9076 | 0.9007 | 0.9696   |
| 0.0153        | 6.0   | 10356 | 0.1908          | 0.8994        | 0.9359     | 0.9173 | 0.8888         | 0.9101      | 0.8993  | 0.8676        | 0.8894     | 0.8784 | 0.8122         | 0.8638      | 0.8372  | 0.8819    | 0.9137 | 0.8975 | 0.9688   |
| 0.0082        | 7.0   | 12082 | 0.1954          | 0.9056        | 0.9332     | 0.9192 | 0.8973         | 0.9079      | 0.9026  | 0.864         | 0.9019     | 0.8825 | 0.8210         | 0.8650      | 0.8424  | 0.8886    | 0.9127 | 0.9005 | 0.9698   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Paciente con los antecedentes rese\u00f1ados que ingresa por cuadro de escasas horas de evoluci\u00f3n consistente en exacerbacion de su temblor habitual, que parece haberse hecho generalizado y cuya descripcion es incapaz de precisar."}, {"text": "Mujer con 5 meses de gestaci\u00f3n que ingresa por cuadro grave debido a bloqueo AV completo, que parece haber ocurrido en la madrugada y cuya sintomatolog\u00eda es incapaz de precisar."}], "model-index": [{"name": "Negation_and_Uncertainty_Scope_Detection_mBERT_fine_tuned", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Negation\_and\_Uncertainty\_Scope\_Detection\_mBERT\_fine\_tuned
================================================================


This model is a fine-tuned version of bert-base-multilingual-cased on the NUBES dataset. This is a result of the PhD dissertation of Antonio Tamayo.
It achieves the following results on the evaluation set:


* Loss: 0.1954
* Neg Precision: 0.9056
* Neg Recall: 0.9332
* Neg F1: 0.9192
* Nsco Precision: 0.8973
* Nsco Recall: 0.9079
* Nsco F1: 0.9026
* Unc Precision: 0.864
* Unc Recall: 0.9019
* Unc F1: 0.8825
* Usco Precision: 0.8210
* Usco Recall: 0.8650
* Usco F1: 0.8424
* Precision: 0.8886
* Recall: 0.9127
* F1: 0.9005
* Accuracy: 0.9698


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: 7


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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: 7 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #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: 7 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3