readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: distilbert-base-uncased-finetuned-sst-2-english-misogyny-sexism-4tweets-2e-05-0.05
    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-base-uncased-finetuned-sst-2-english-misogyny-sexism-4tweets-2e-05-0.05
  
  This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0536
  - Accuracy: 0.6723
  - F1: 0.6889
  - Precision: 0.6125
  - Recall: 0.7870
  - Mae: 0.3277
  - Tn: 309
  - Fp: 229
  - Fn: 98
  - Tp: 362
  
  ## 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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Mae    | Tn  | Fp  | Fn  | Tp  |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:|
  | 0.4807        | 1.0   | 1346 | 0.6685          | 0.6623   | 0.6750 | 0.6066    | 0.7609 | 0.3377 | 311 | 227 | 110 | 350 |
  | 0.3913        | 2.0   | 2692 | 0.7041          | 0.6723   | 0.6935 | 0.6096    | 0.8043 | 0.3277 | 301 | 237 | 90  | 370 |
  | 0.3305        | 3.0   | 4038 | 0.8915          | 0.6713   | 0.6963 | 0.6065    | 0.8174 | 0.3287 | 294 | 244 | 84  | 376 |
  | 0.2556        | 4.0   | 5384 | 1.0536          | 0.6723   | 0.6889 | 0.6125    | 0.7870 | 0.3277 | 309 | 229 | 98  | 362 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  