readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-cased-ner_cv-med-ft
    results: []
  
  widget:
  - text: "Vivo en Buenos Aires,Argentina. Egresado de la carrera Ingeniería en Computación Conocimientos de lenguajes HTML, CSS, Javascript y MySQL. Experiencia trabajando en ámbitos de redes de pequeña y mediana escala. Inglés Hablado nivel básico, escrito nivel intermedio.HTML, CSS y JavaScript. Realidad aumentada.  Lenguaje R. HTML5, JavaScript y Nodejs"
  
  ---
  
  <!-- 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. -->
  
  # bert-base-cased-ner_cv-med-ft
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5926
  - Precision: 0.2559
  - Recall: 0.3460
  - F1: 0.2942
  - Accuracy: 0.8368
  
  ## 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: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 10
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.9616        | 2.73  | 30   | 0.7717          | 0.0       | 0.0    | 0.0    | 0.8608   |
  | 0.9266        | 5.45  | 60   | 0.6687          | 0.0       | 0.0    | 0.0    | 0.8608   |
  | 0.8486        | 8.18  | 90   | 0.6100          | 0.2133    | 0.0488 | 0.0794 | 0.8635   |
  | 0.7421        | 10.91 | 120  | 0.5922          | 0.2534    | 0.1966 | 0.2215 | 0.8542   |
  | 0.6481        | 13.64 | 150  | 0.5696          | 0.2889    | 0.2378 | 0.2609 | 0.8596   |
  | 0.5948        | 16.36 | 180  | 0.5798          | 0.2678    | 0.3034 | 0.2845 | 0.8472   |
  | 0.5621        | 19.09 | 210  | 0.5913          | 0.2486    | 0.3293 | 0.2833 | 0.8381   |
  | 0.5234        | 21.82 | 240  | 0.5816          | 0.2585    | 0.3262 | 0.2884 | 0.8404   |
  | 0.5028        | 24.55 | 270  | 0.5944          | 0.2545    | 0.3476 | 0.2938 | 0.8368   |
  | 0.4975        | 27.27 | 300  | 0.5923          | 0.2531    | 0.3476 | 0.2929 | 0.8368   |
  | 0.4791        | 30.0  | 330  | 0.5926          | 0.2559    | 0.3460 | 0.2942 | 0.8368   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.8.1+cu111
  - Datasets 1.6.2
  - Tokenizers 0.12.1
  