readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: bert-base-uncased-misogyny-sexism-4tweets-3e-05-0.05-3
    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. -->
  
  # bert-base-uncased-misogyny-sexism-4tweets-3e-05-0.05-3
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8767
  - Accuracy: 0.7094
  - F1: 0.7184
  - Precision: 0.6491
  - Recall: 0.8043
  - Mae: 0.2906
  - Tn: 338
  - Fp: 200
  - Fn: 90
  - Tp: 370
  
  ## 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: 3e-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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    | Tn  | Fp  | Fn  | Tp  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:|
  | 0.4767        | 1.0   | 1346 | 0.6853          | 0.6323   | 0.6501 | 0.5789    | 0.7413 | 0.3677 | 290 | 248 | 119 | 341 |
  | 0.3783        | 2.0   | 2692 | 0.7041          | 0.6653   | 0.6528 | 0.6255    | 0.6826 | 0.3347 | 350 | 188 | 146 | 314 |
  | 0.2803        | 3.0   | 4038 | 0.8767          | 0.7094   | 0.7184 | 0.6491    | 0.8043 | 0.2906 | 338 | 200 | 90  | 370 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  