readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  - accuracy
  - precision
  - recall
  model-index:
  - name: soft-search
    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. -->
  
  # soft-search
  
  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: 0.5558
  - F1: 0.5960
  - Accuracy: 0.7109
  - Precision: 0.5769
  - Recall: 0.6164
  
  ## 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: 12
  - eval_batch_size: 12
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
  | 0.5939        | 1.0   | 71   | 0.5989          | 0.0533 | 0.6635   | 1.0       | 0.0274 |
  | 0.5903        | 2.0   | 142  | 0.5558          | 0.5960 | 0.7109   | 0.5769    | 0.6164 |
  | 0.4613        | 3.0   | 213  | 0.6670          | 0.5641 | 0.6777   | 0.5301    | 0.6027 |
  | 0.4454        | 4.0   | 284  | 0.7647          | 0.5541 | 0.6872   | 0.5467    | 0.5616 |
  | 0.2931        | 5.0   | 355  | 0.8726          | 0.5139 | 0.6682   | 0.5211    | 0.5068 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  