readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilBERT_bio_pv_superset
    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_bio_pv_superset
  
  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: 0.2328
  - Precision: 0.5462
  - Recall: 0.5325
  - F1: 0.5393
  - Accuracy: 0.9495
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0964        | 1.0   | 5467  | 0.1593          | 0.4625    | 0.3682 | 0.4100 | 0.9416   |
  | 0.1918        | 2.0   | 10934 | 0.1541          | 0.4796    | 0.4658 | 0.4726 | 0.9436   |
  | 0.0394        | 3.0   | 16401 | 0.1508          | 0.5349    | 0.4744 | 0.5028 | 0.9482   |
  | 0.1207        | 4.0   | 21868 | 0.1615          | 0.5422    | 0.4953 | 0.5177 | 0.9490   |
  | 0.0221        | 5.0   | 27335 | 0.1827          | 0.5377    | 0.5018 | 0.5191 | 0.9487   |
  | 0.0629        | 6.0   | 32802 | 0.1874          | 0.5479    | 0.5130 | 0.5299 | 0.9493   |
  | 0.0173        | 7.0   | 38269 | 0.2025          | 0.5388    | 0.5323 | 0.5356 | 0.9488   |
  | 0.2603        | 8.0   | 43736 | 0.2148          | 0.5437    | 0.5397 | 0.5417 | 0.9493   |
  | 0.0378        | 9.0   | 49203 | 0.2323          | 0.5430    | 0.5194 | 0.5310 | 0.9489   |
  | 0.031         | 10.0  | 54670 | 0.2328          | 0.5462    | 0.5325 | 0.5393 | 0.9495   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  