readme: ---
  license: apache-2.0
  
  widget:
  - text: "Ayer dormí la siesta durante 3 horas"
  - text: "Recuerda tu cita con el médico el lunes a las 8 de la tarde"
  - text: "Recuerda tomar la medicación cada noche"
  
  tags:
  - LABEL-0 = NONE  
  - LABEL-1 = B-DATE  
  - LABEL-2 = I-DATE
  - LABEL-3 = B-TIME
  - LABEL-4 = I-TIME
  - LABEL-5 = B-DURATION
  - LABEL-6 = B-DURATION
  - LABEL-7 = B-SET  
  - LABEL-8 = B-SET
   
  
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  
  datasets:
  - Spanish-Timebank
  
  model-index:
  - name: roberta-base-biomedical-clinical-es-finetuned-ner-timebank
    results: []
  ---
  
  <!-- 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. -->
  
  # roberta-base-biomedical-clinical-es-finetuned-ner-timebank
  
  This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0162
  - Precision: 0.8070
  - Recall: 0.8854
  - F1: 0.8444
  - Accuracy: 0.9921
  
  ## 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: 8e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 82
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 24
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0658        | 1.0   | 8    | 0.0613          | 0.3704    | 0.4514 | 0.4069 | 0.9758   |
  | 0.038         | 2.0   | 16   | 0.0328          | 0.4528    | 0.6667 | 0.5393 | 0.9789   |
  | 0.0067        | 3.0   | 24   | 0.0212          | 0.4515    | 0.7431 | 0.5617 | 0.9776   |
  | 0.0225        | 4.0   | 32   | 0.0177          | 0.4913    | 0.7847 | 0.6043 | 0.9814   |
  | 0.0119        | 5.0   | 40   | 0.0170          | 0.5531    | 0.7778 | 0.6465 | 0.9848   |
  | 0.0109        | 6.0   | 48   | 0.0151          | 0.7027    | 0.8125 | 0.7536 | 0.9893   |
  | 0.003         | 7.0   | 56   | 0.0136          | 0.6260    | 0.8194 | 0.7098 | 0.9861   |
  | 0.0059        | 8.0   | 64   | 0.0134          | 0.7164    | 0.8333 | 0.7705 | 0.9895   |
  | 0.0065        | 9.0   | 72   | 0.0125          | 0.7040    | 0.8507 | 0.7704 | 0.9890   |
  | 0.0017        | 10.0  | 80   | 0.0118          | 0.6177    | 0.8472 | 0.7145 | 0.9852   |
  | 0.0021        | 11.0  | 88   | 0.0150          | 0.7812    | 0.8681 | 0.8224 | 0.9915   |
  | 0.0041        | 12.0  | 96   | 0.0165          | 0.8078    | 0.8611 | 0.8336 | 0.9922   |
  | 0.0025        | 13.0  | 104  | 0.0142          | 0.7723    | 0.8715 | 0.8189 | 0.9909   |
  | 0.0024        | 14.0  | 112  | 0.0134          | 0.7440    | 0.8681 | 0.8013 | 0.9905   |
  | 0.0027        | 15.0  | 120  | 0.0142          | 0.7699    | 0.8715 | 0.8176 | 0.9912   |
  | 0.0019        | 16.0  | 128  | 0.0151          | 0.7918    | 0.8715 | 0.8298 | 0.9918   |
  | 0.0036        | 17.0  | 136  | 0.0151          | 0.7837    | 0.8681 | 0.8237 | 0.9917   |
  | 0.0011        | 18.0  | 144  | 0.0149          | 0.7730    | 0.875  | 0.8208 | 0.9914   |
  | 0.0013        | 19.0  | 152  | 0.0152          | 0.7913    | 0.8819 | 0.8342 | 0.9918   |
  | 0.0017        | 20.0  | 160  | 0.0159          | 0.8038    | 0.8819 | 0.8411 | 0.9922   |
  | 0.0006        | 21.0  | 168  | 0.0164          | 0.8070    | 0.8854 | 0.8444 | 0.9922   |
  | 0.002         | 22.0  | 176  | 0.0164          | 0.8121    | 0.8854 | 0.8472 | 0.9922   |
  | 0.0021        | 23.0  | 184  | 0.0163          | 0.8121    | 0.8854 | 0.8472 | 0.9922   |
  | 0.0013        | 24.0  | 192  | 0.0162          | 0.8070    | 0.8854 | 0.8444 | 0.9921   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  