readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-ner_cv
    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. -->
  
  # distilbert-base-uncased-ner_cv
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8548
  - Precision: 0.3327
  - Recall: 0.2358
  - F1: 0.2760
  - Accuracy: 0.7815
  
  ## 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: 20
  - num_epochs: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 5.0   | 30   | 1.0790          | 0.0       | 0.0    | 0.0    | 0.7537   |
  | No log        | 10.0  | 60   | 0.9589          | 0.3208    | 0.1207 | 0.1754 | 0.7677   |
  | No log        | 15.0  | 90   | 0.8975          | 0.3363    | 0.1591 | 0.2160 | 0.7773   |
  | No log        | 20.0  | 120  | 0.8675          | 0.3354    | 0.2259 | 0.2699 | 0.7786   |
  | No log        | 25.0  | 150  | 0.8568          | 0.3333    | 0.2443 | 0.2820 | 0.7811   |
  | No log        | 30.0  | 180  | 0.8548          | 0.3327    | 0.2358 | 0.2760 | 0.7815   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.8.1+cu111
  - Datasets 1.6.2
  - Tokenizers 0.12.1
  