readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - species_800
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-SPECIES800-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: species_800
        type: species_800
        config: species_800
        split: train
        args: species_800
      metrics:
      - name: Precision
        type: precision
        value: 0.6221498371335505
      - name: Recall
        type: recall
        value: 0.7470664928292047
      - name: F1
        type: f1
        value: 0.6789099526066352
      - name: Accuracy
        type: accuracy
        value: 0.9831434110359828
  ---
  
  <!-- 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. -->
  
  # electramed-small-SPECIES800-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the species_800 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0513
  - Precision: 0.6221
  - Recall: 0.7471
  - F1: 0.6789
  - Accuracy: 0.9831
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0536        | 1.0   | 359  | 0.0971          | 0.6138    | 0.5554 | 0.5832 | 0.9795   |
  | 0.0309        | 2.0   | 718  | 0.0692          | 0.6175    | 0.6063 | 0.6118 | 0.9808   |
  | 0.0563        | 3.0   | 1077 | 0.0582          | 0.6424    | 0.6910 | 0.6658 | 0.9819   |
  | 0.0442        | 4.0   | 1436 | 0.0553          | 0.5900    | 0.7523 | 0.6613 | 0.9814   |
  | 0.0069        | 5.0   | 1795 | 0.0511          | 0.6291    | 0.7497 | 0.6841 | 0.9827   |
  | 0.0141        | 6.0   | 2154 | 0.0505          | 0.6579    | 0.7471 | 0.6996 | 0.9837   |
  | 0.0052        | 7.0   | 2513 | 0.0513          | 0.5965    | 0.7458 | 0.6628 | 0.9826   |
  | 0.0573        | 8.0   | 2872 | 0.0509          | 0.6140    | 0.7445 | 0.6730 | 0.9828   |
  | 0.0203        | 9.0   | 3231 | 0.0516          | 0.6118    | 0.7458 | 0.6722 | 0.9830   |
  | 0.0101        | 10.0  | 3590 | 0.0513          | 0.6221    | 0.7471 | 0.6789 | 0.9831   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  