readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: AgitationTextV1
    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. -->
  
  # AgitationTextV1
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5634
  - Accuracy: 0.84
  - Precision: 0.9054
  - Recall: 0.8816
  - F1: 0.8933
  
  ## 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
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.558         | 1.0   | 50   | 0.5387          | 0.75     | 0.9048    | 0.75   | 0.8201 |
  | 0.4431        | 2.0   | 100  | 0.4989          | 0.75     | 0.9474    | 0.7105 | 0.8120 |
  | 0.3334        | 3.0   | 150  | 0.4703          | 0.79     | 0.9661    | 0.75   | 0.8444 |
  | 0.2601        | 4.0   | 200  | 0.4391          | 0.85     | 0.9420    | 0.8553 | 0.8966 |
  | 0.2081        | 5.0   | 250  | 0.4560          | 0.84     | 0.9167    | 0.8684 | 0.8919 |
  | 0.1655        | 6.0   | 300  | 0.5424          | 0.84     | 0.8947    | 0.8947 | 0.8947 |
  | 0.1427        | 7.0   | 350  | 0.4977          | 0.84     | 0.9286    | 0.8553 | 0.8904 |
  | 0.1237        | 8.0   | 400  | 0.5642          | 0.85     | 0.9067    | 0.8947 | 0.9007 |
  | 0.1122        | 9.0   | 450  | 0.6052          | 0.85     | 0.9067    | 0.8947 | 0.9007 |
  | 0.0962        | 10.0  | 500  | 0.5634          | 0.84     | 0.9054    | 0.8816 | 0.8933 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  