readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: scibert-cased-AstrID-tok
    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. -->
  
  # scibert-cased-AstrID-tok
  
  This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1843
  - Precision: 0.7307
  - Recall: 0.7575
  - F1: 0.7439
  - Accuracy: 0.9517
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 220  | 0.3141          | 0.6254    | 0.6518 | 0.6383 | 0.9182   |
  | No log        | 2.0   | 440  | 0.1988          | 0.7002    | 0.7275 | 0.7136 | 0.9480   |
  | 0.3514        | 3.0   | 660  | 0.1843          | 0.7307    | 0.7575 | 0.7439 | 0.9517   |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  