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# NER_EHR_Spanish_model_Mulitlingual_BERT

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the DisTEMIST shared task 2022 dataset. It is available at: https://temu.bsc.es/distemist/category/data/


It achieves the following results on the evaluation set:
- Loss: 0.2603
- Precision: 0.5637
- Recall: 0.5801
- F1: 0.5718
- Accuracy: 0.9534

## Model description

For a complete description of our system, please go to: https://ceur-ws.org/Vol-3180/paper-26.pdf

## Training and evaluation data

Dataset provided by DisTEMIST shared task, it is available at: https://temu.bsc.es/distemist/category/data/

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.2060          | 0.5017    | 0.5540 | 0.5266 | 0.9496   |
| No log        | 2.0   | 142  | 0.2163          | 0.5363    | 0.5433 | 0.5398 | 0.9495   |
| No log        | 3.0   | 213  | 0.2245          | 0.5521    | 0.5356 | 0.5438 | 0.9514   |
| No log        | 4.0   | 284  | 0.2453          | 0.5668    | 0.5985 | 0.5822 | 0.9522   |
| No log        | 5.0   | 355  | 0.2433          | 0.5657    | 0.5579 | 0.5617 | 0.9530   |
| No log        | 6.0   | 426  | 0.2553          | 0.5762    | 0.5762 | 0.5762 | 0.9536   |
| No log        | 7.0   | 497  | 0.2603          | 0.5637    | 0.5801 | 0.5718 | 0.9534   |


### How to cite this work:

Tamayo, A., Burgos, D. A., & Gelbukh, A. (2022). mbert and simple post-processing: A baseline for disease mention detection in spanish. In Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings.

@inproceedings{tamayo2022mbert,
  title={mbert and simple post-processing: A baseline for disease mention detection in spanish},
  author={Tamayo, Antonio and Burgos, Diego A and Gelbukh, Alexander},
  booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings},
  year={2022}
}

### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "widget": [{"text": "Presentamos el caso de una mujer de 30 a\u00f1os, fumadora de 20 cigarrillos/d\u00eda y sin otros antecedentes personales de inter\u00e9s. La paciente refiere infecciones urinarias de repetici\u00f3n. Se indica realizaci\u00f3n de ecograf\u00eda abdominal, observ\u00e1ndose una lesi\u00f3n nodular intravesical, por lo que es derivada a consulta de urolog\u00eda. En cistoscopia se visualiza tumoraci\u00f3n exof\u00edtica de 3x3 cms. en cara lateral derecha con mucosa vesical \u00edntegra, no encontr\u00e1ndose alteraciones en el resto de la vejiga. Se realiza exploraci\u00f3n bajo anestesia (EBA) y resecci\u00f3n transuretral de dicha lesi\u00f3n (RTU). En el informe de anatom\u00eda patol\u00f3gica macrosc\u00f3picamente se describen fragmentos de pared vesical con urotelio conservado sin displasia, destacando en la capa muscular propia y en continuidad con el tejido muscular de la misma, una tumoraci\u00f3n fusocelular con c\u00e9lulas que muestran unos n\u00facleos de gran tama\u00f1o, pleom\u00f3rficos, de aspecto vesiculoso y unos citoplasmas amplios eosin\u00f3filos. Esta celularidad se dispone en formas de fasc\u00edculos mal definidos y entre la misma se reconoce abundante celularidad constituida fundamentalmente por numerosas c\u00e9lulas plasm\u00e1ticas y leucocitos polimorfonucleares eosin\u00f3filos. No se observa un \u00edndice mit\u00f3tico elevado, aunque el \u00edndice de proliferaci\u00f3n medido como positividad nuclear con anticuerpos frente a MIB-1 se encuentra entre el 10 y el 25% de la celularidad tumoral. No se han objetivado \u00e1reas de necrosis. En estudio inmunohistoqu\u00edmico se observa marcada positividad frente a citoqueratinas (AE1/AE3) y CAM5.2 a nivel citoplasm\u00e1tico, as\u00ed como una marcada positividad citoplasm\u00e1tica con anticuerpos frente a p80 (prote\u00edna ALK). La celularidad descrita ha resultado negativa con anticuerpos frente a m\u00fasculo liso (actina de m\u00fasculo liso, MyO D1 y Calretinina), as\u00ed como para CEA y citoqueratinas de alto peso molecular, observ\u00e1ndose tan s\u00f3lo positividad focal y aislada frente a EMA. Tras realizaci\u00f3n de FISH sobre material parafinado no se evidencia traslocaci\u00f3n en el gen de la ALK. El diagn\u00f3stico anatomopatol\u00f3gico definitivo es tumor miofibrobl\u00e1stico inflamatorio vesical."}], "model-index": [{"name": "NER_EHR_Spanish_model_Mulitlingual_BERT", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

NER\_EHR\_Spanish\_model\_Mulitlingual\_BERT
============================================


This model is a fine-tuned version of bert-base-multilingual-cased on the DisTEMIST shared task 2022 dataset. It is available at: URL


It achieves the following results on the evaluation set:


* Loss: 0.2603
* Precision: 0.5637
* Recall: 0.5801
* F1: 0.5718
* Accuracy: 0.9534


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


For a complete description of our system, please go to: URL


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


Dataset provided by DisTEMIST shared task, it is available at: URL


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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



### How to cite this work:


Tamayo, A., Burgos, D. A., & Gelbukh, A. (2022). mbert and simple post-processing: A baseline for disease mention detection in spanish. In Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings.


@inproceedings{tamayo2022mbert,
title={mbert and simple post-processing: A baseline for disease mention detection in spanish},
author={Tamayo, Antonio and Burgos, Diego A and Gelbukh, Alexander},
booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings},
year={2022}
}


### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 ### How to cite this work:


Tamayo, A., Burgos, D. A., & Gelbukh, A. (2022). mbert and simple post-processing: A baseline for disease mention detection in spanish. In Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings.


@inproceedings{tamayo2022mbert,
title={mbert and simple post-processing: A baseline for disease mention detection in spanish},
author={Tamayo, Antonio and Burgos, Diego A and Gelbukh, Alexander},
booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings},
year={2022}
} ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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 ### How to cite this work:


Tamayo, A., Burgos, D. A., & Gelbukh, A. (2022). mbert and simple post-processing: A baseline for disease mention detection in spanish. In Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings.


@inproceedings{tamayo2022mbert,
title={mbert and simple post-processing: A baseline for disease mention detection in spanish},
author={Tamayo, Antonio and Burgos, Diego A and Gelbukh, Alexander},
booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings},
year={2022}
} ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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### How to cite this work:


Tamayo, A., Burgos, D. A., & Gelbukh, A. (2022). mbert and simple post-processing: A baseline for disease mention detection in spanish. In Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings.


@inproceedings{tamayo2022mbert,
title={mbert and simple post-processing: A baseline for disease mention detection in spanish},
author={Tamayo, Antonio and Burgos, Diego A and Gelbukh, Alexander},
booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings},
year={2022}
}### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0+cu113
* Datasets 2.2.0
* Tokenizers 0.12.1